From 7cd37198fe6e4988da2e2ee754ddaf6aa2a8e0f6 Mon Sep 17 00:00:00 2001
From: syeoo <149696518+syeoo@users.noreply.github.com>
Date: Fri, 3 Nov 2023 14:40:10 -0400
Subject: [PATCH 1/4] Distillation column diagram (Mccabe thiele)

---
 media/Distillation Column.png | Bin 0 -> 23079 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 media/Distillation Column.png

diff --git a/media/Distillation Column.png b/media/Distillation Column.png
new file mode 100644
index 0000000000000000000000000000000000000000..895bc230d97478c6b3cc2ee36790c640dbabbca2
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From 60efbea3002dffea583610c6cef0d6e56522f983 Mon Sep 17 00:00:00 2001
From: syeoo <149696518+syeoo@users.noreply.github.com>
Date: Fri, 3 Nov 2023 14:44:03 -0400
Subject: [PATCH 2/4] Images for distillation column from Steven

---
 media/Distillation_column.png | Bin 0 -> 23079 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 media/Distillation_column.png

diff --git a/media/Distillation_column.png b/media/Distillation_column.png
new file mode 100644
index 0000000000000000000000000000000000000000..895bc230d97478c6b3cc2ee36790c640dbabbca2
GIT binary patch
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From 08fef490f5e103e168cf06149060e4c0df11e78c Mon Sep 17 00:00:00 2001
From: syeoo <149696518+syeoo@users.noreply.github.com>
Date: Fri, 3 Nov 2023 15:12:18 -0400
Subject: [PATCH 3/4] Final version,

 change grammar, spelling, and more discussion section
---
 .../McCabe_Thiele_Steven_Final.ipynb          | 1665 +++++++++++++++++
 1 file changed, 1665 insertions(+)
 create mode 100644 notebooks/contrib-dev/McCabe_Thiele_Steven_Final.ipynb

diff --git a/notebooks/contrib-dev/McCabe_Thiele_Steven_Final.ipynb b/notebooks/contrib-dev/McCabe_Thiele_Steven_Final.ipynb
new file mode 100644
index 00000000..ef9487dc
--- /dev/null
+++ b/notebooks/contrib-dev/McCabe_Thiele_Steven_Final.ipynb
@@ -0,0 +1,1665 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2wgBEY7p6-9B"
+      },
+      "source": [
+        "# **Plotting McCabe-Thiele diagram through computational methods**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GpAjVy8DS_P-"
+      },
+      "source": [
+        "Prepared by:\n",
+        "\n",
+        "Zeping Chen - zchen23@nd.edu\n",
+        "\n",
+        "Suporna Paul - spaul2@nd.edu\n",
+        "\n",
+        "Steven Yeo - syeo@nd.edu"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XtXFNP0z6ccg"
+      },
+      "source": [
+        "## **1. Introduction**\n",
+        "\n",
+        "Do you ever find yourself frustrated with the painstaking process of manually sketching McCabe-Thiele diagrams? Have you ever questioned whether there's a more efficient and precise approach, especially when dealing with the intricacies of the operating line and stepping lines? We will embark on a journey that combines data science and chemical engineering to improve separation processes! In this notebook, we'll delve into harnessing the power of Python to streamline the McCabe-Thiele diagram plotting process, making it not only simpler but also more data-centric and accurate, whilst giving special attention to the construction of the operating line and stepping lines."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LB88rTwE6n8H"
+      },
+      "source": [
+        "## 1.1 Target audience and learning objectives\n",
+        "\n",
+        "\n",
+        "This notebook is intended for Chemical Engineering students (both undergraduates and graduate students) who have completed or are currently enrolled in a Chemical Engineering Separations Process class.\n",
+        "\n",
+        "After studying this notebook, completing the activities, and asking questions in class, you should be able to:\n",
+        "\n",
+        "*   Use numpy to solve system of linear equations\n",
+        "*   Use pandas to read csv\n",
+        "*   Produce \"publication ready\" plots\n",
+        "*   Graph the McCabe-Thiele diagram using computational techniques\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "soxKSlO669DC"
+      },
+      "source": [
+        "## 1.2 Relevant notebooks from the class:\n",
+        "\n",
+        "\n",
+        "1.15. [Preparing Publication Quality Figures in Python](https://ndcbe.github.io/data-and-computing/notebooks/01/Publication-Quality-Figures.html)\n",
+        "\n",
+        "1.3. [Modeling Systems of Linear Equations](https://ndcbe.github.io/data-and-computing/notebooks/01/Python-Basics-III-Lists-Dictionaries-Enumeration.html)\n",
+        "\n",
+        "1.5.[Functions and Scope](https://ndcbe.github.io/data-and-computing/notebooks/01/Functions-and-Scope.html)\n",
+        "\n",
+        "4.1. [Python Basics II: Loopy Logic](https://ndcbe.github.io/data-and-computing/notebooks/01/Flow-control.html)\n",
+        "\n",
+        "14.7. [Multivariate Linear Regression](https://ndcbe.github.io/data-and-computing/notebooks/14/Multivariate-Linear-Regression.html)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uvxlvi_gIaQ3"
+      },
+      "source": [
+        "## 1.3 References:\n",
+        "1. McCabe-Thiele Plot | Neutrium. https://neutrium.net/unit-operations/distillation/mccabe-thiele-plot/ (accessed 2022-10-15\n",
+        "\n",
+        "\n",
+        "2. Vapor-Liquid Equilibrium (VLE) Model for vapor mole frac methanol and liquid mole frac methanol. https://raw.githubusercontent.com/chennieXD/McCabe-Thiele/main/LiquidVaporEquil.csv (accessed 2022-10-15).\n",
+        "\n",
+        "3. Stichlmair, G.; Klein, H.; Rehfeldt, S. Distillation: Principles and Practice, 2nd ed.; Wiley, 2021.\n",
+        "\n",
+        "4. Gorak, A.; Sorensen, E.  Distillation: Fundamentals and Principles (Handbooks in Separation Science), 1st ed.; Academic Press, 2014."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KaVgvFRXDqLH"
+      },
+      "source": [
+        "## 1.4 Background information\n",
+        "\n",
+        "### Distillation\n",
+        "Distillation is a widely used separation technique that exploits the differences in the boiling points of components within a liquid mixture. The process involves heating a liquid mixture to create vapor, and then cooling that vapor to condense it back into a liquid, separating the components in the process. Distillation can be used to separate two or more components from a mixture based on their volatility, which is determined by their boiling points."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###Components of a Distillation Column:\n",
+        "\n",
+        "A distillation column is a tall vertical vessel. Consists of several components\n",
+        "\n",
+        "1. **Reboiler**: Where the liquid feed is heated to create vapor. This is in the bottom of the column\n",
+        "2. **Distillation Trays or packing**: Stages inside the column. This is designed to facilitate contact between vapor and liquid\n",
+        "3. **Condenser**: Cools the vapor to condense back to liquid at the top of the tower"
+      ],
+      "metadata": {
+        "id": "rzy5Fb4pIzDl"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Key Operating Parameters:\n",
+        "\n",
+        "1. **Temperature**: Temperature gradient allows for components with different boiling point to separate\n",
+        "2. **Pressure**: Changes along the column's height. In this problem, we assume that it's constant\n",
+        "3. **Vapor and Liquid flow Rates**: Flow rate of vapor and liquid in the columns\n",
+        "4. **Reflux Ratio**: The portion of the condensed liquid returned to the column as liquid"
+      ],
+      "metadata": {
+        "id": "HD7iWk5mJ3F_"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "![](../../media/Distillation_column.png)\n",
+        "\n",
+        "(Gunawan et al. (2021))"
+      ],
+      "metadata": {
+        "id": "NEDDdEdGfcz2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CiAHPqhIMJhl"
+      },
+      "source": [
+        "## **2. Solving distillation column using McCabe-Thiele method**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "26k7Oq1lc8QF"
+      },
+      "outputs": [],
+      "source": [
+        "# libraries used\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cYiBIRTOWE81"
+      },
+      "source": [
+        "### **Problem description**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QP4UfEimWCgg"
+      },
+      "source": [
+        "A student is trying to calculate the number of stages in a distillation column. Since there is insulation installed to improve the efficiency of the column, he can not just count the number of stages physically. To figure out the number of stages, he is feeding a methanol-water mixture into the column to observe how the column performs. Using a hydrometer, the student is able to measure the specific gravity of the feed: 0.887, distillate: 0.815 and bottoms: 0.990. He is also able to measure the reflux ratio to be 1.25 and the feed has a liquid mole fraction of 0.36.\n",
+        "\n",
+        "Using the above information:\n",
+        "1. Calculate the mole fraction  of each stream from specific gravity\n",
+        "2.  Plot the vapor liquid equilibrium line and 45 degree line.\n",
+        "3.  Plot the McCabe-Thiele diagram for a total reflux run for the mixture and calculate the number of stages.\n",
+        "4.  Plot the McCabe-Thiele diagram for a feed run for the mixture and calculate the number of stages.\n",
+        "5. Discuss what the McCabe-Thiele diagram tells you about the feed condiiton\n",
+        "6. Discuss how a McCabe-Thiele plot with 100% efficiency is unrealistic.\n",
+        "\n",
+        "**Note:** This problem is developed by Zeping Chen and Suporna Paul. Here, we demonstrated a step-by-step process of using this python notebook to solve for distillation column parameters.\n",
+        "\n",
+        "For further information, readers are encouraged to read these following text books which contains similar math problems.\n",
+        "\n",
+        "**References:**\n",
+        "\n",
+        "1. Stichlmair, G.; Klein, H.; Rehfeldt, S. Distillation: Principles and Practice, 2nd ed.; Wiley, 2021.\n",
+        "2. Gorak, A.; Sorensen, E.  Distillation: Fundamentals and Principles (Handbooks in Separation Science), 1st ed.; Academic Press, 2014.\n",
+        "3. Gunawan, P., Kwan, J., Cai, Y., &amp; Yang, R. (2021). Augmented reality application for Chemical Engineering Unit Operations. Virtual and Augmented Reality, Simulation and Serious Games for Education, 29–43. https://doi.org/10.1007/978-981-16-1361-6_4\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cDaMW2X3XOak"
+      },
+      "source": [
+        "## 2.1. Solve for mole fraction of each stream"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "M0uJ9fiFmQPe"
+      },
+      "source": [
+        "We can rearrange the definition of specific gravity (SG) to get the density of mixture in each stream:\n",
+        "\n",
+        "\n",
+        "\\begin{align}\n",
+        "        SG = \\frac{density \\ of \\ mixture}{density \\ of \\ water}\n",
+        "    \\end{align}\n",
+        "\n",
+        "We also know that the density (ρ) of a mixture can be calculated by summing the mole fraction (X) of each component by that component's density.\n",
+        "\n",
+        "\\begin{align}\n",
+        "        ρ_t = ρ_a X_a + ρ_b X_b\n",
+        "    \\end{align}\n",
+        "\n",
+        "\n",
+        "From these information, we can write a system of linear equations to calculate the mole fraction of methanol and water in each stream:\n",
+        "\n",
+        "\n",
+        "\\begin{align}\n",
+        "        1 = X_W + X_M\n",
+        "    \\end{align}\n",
+        "    \n",
+        "\\begin{align}\n",
+        "        ρ_t = ρ_M X_M + ρ_W X_W\n",
+        "    \\end{align}\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Co3E5xSUALY0"
+      },
+      "source": [
+        "## 2.1.a. Obtaining density of mixture through specific gravity\n",
+        "\n",
+        "The density of each component is given as below:\n",
+        "*   Water: 997 kg/m$^3$\n",
+        "*   Methanol: 792 kg/m$^3$"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "o4rndH2QgnAi"
+      },
+      "outputs": [],
+      "source": [
+        "# Density of each species\n",
+        "\n",
+        "# Water\n",
+        "rho_water = 997  # kg/m^3\n",
+        "# Methanol\n",
+        "rho_methanol = 792  # kg/m^3"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "q6PdrxgweFCQ"
+      },
+      "source": [
+        "## 2.1.b. Create a function to solve  Linear System\n",
+        "\n",
+        "An easier method to calculate the mole fraciton of Methanol in each stream is to use linear algebra.\n",
+        "\n",
+        "Matrix form:\n",
+        "$$\n",
+        "\\begin{equation}\n",
+        "\\begin{bmatrix}\n",
+        "ρ_W & ρ_M\\\\\n",
+        "1 & 1\n",
+        "\\end{bmatrix} \\cdot\n",
+        "\\begin{bmatrix}\n",
+        "\tX_W \\\\\n",
+        "\tX_M\n",
+        "\\end{bmatrix} =\n",
+        "\\begin{bmatrix}\n",
+        "\tρ_t \\\\\n",
+        "\t1\n",
+        "\\end{bmatrix}\n",
+        "\\end{equation}\n",
+        "$$\n",
+        "\n",
+        "\n",
+        "Now let's write a function that will solve the linear system of equations to obtain the mole fraction of Methanol in each stream **using Python**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "2jmiPXBfdZW4"
+      },
+      "outputs": [],
+      "source": [
+        "def conc_solver(SG):\n",
+        "    ### BEGIN SOLUTIONS\n",
+        "\n",
+        "    \"\"\"calculates the mole fraction of Methanol in the stream by solving one variable in one equation\n",
+        "\n",
+        "    Arguments:\n",
+        "        SG: specfic gravity\n",
+        "\n",
+        "    Returns:\n",
+        "        x: molar fraction of Methanol\n",
+        "    \"\"\"\n",
+        "\n",
+        "    # the left matrix above\n",
+        "    a = np.array([[rho_water, rho_methanol], [1, 1]])\n",
+        "\n",
+        "    # the right matrix above\n",
+        "    b = np.array([SG * rho_water, 1])\n",
+        "\n",
+        "    #mole fraction for each component\n",
+        "    x = np.linalg.solve(a, b)\n",
+        "\n",
+        "    #mole fraction of metanol\n",
+        "    x_methanol = x[1]\n",
+        "\n",
+        "    return x_methanol\n",
+        "\n",
+        "    ### END SOLUTIONS"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qZDUyGUpM1CA"
+      },
+      "source": [
+        "The specific gravity of each stream (Feed, Distillate, Bottoms) is given as below:\n",
+        "*   $SG_Z$ = 0.887\n",
+        "*  $SG_D$ = 0.815\n",
+        "*   $SG_B$ = 0.990"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "aNJxVaIcf3i_"
+      },
+      "outputs": [],
+      "source": [
+        "# Specify the Specific Gravity of each stream\n",
+        "\n",
+        "### BEGIN SOLUTIONS\n",
+        "\n",
+        "# Specific Gravity of feed\n",
+        "SG_Z = 0.887\n",
+        "\n",
+        "# Specific Gravity of distillate\n",
+        "SG_D = 0.815\n",
+        "\n",
+        "# Specific Gravity of bottoms\n",
+        "SG_B = 0.990\n",
+        "\n",
+        "### END SOLUTIONS"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OmdZMkxneBPA"
+      },
+      "source": [
+        "## 2.1.c. Solve for the mole fraction of Methanol in each stream\n",
+        "\n",
+        "Using the function created previously to calculate and print the mole fraction of Methanol in each stream:\n",
+        "\n",
+        "Use **$x_D$** for the distillate stream\n",
+        "\n",
+        "Use **$x_B$** for the bottom stream\n",
+        "\n",
+        "Use **$x_Z$** for the feed stream"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JeOQpz4kgWIc",
+        "outputId": "fa878419-23cc-4b3a-e6c7-bdefb83db9a0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The molar fraction of Methanol in the distillate stream is: 0.900\n",
+            "The molar fraction of Methanol in the bottoms stream is: 0.049\n",
+            "The molar fraction of Methanol in the feed stream is: 0.550\n"
+          ]
+        }
+      ],
+      "source": [
+        "# calculate the molar fraction of each stream\n",
+        "### BEGIN SOLUTIONS\n",
+        "\n",
+        "# Mole fraction of Feed\n",
+        "xZ = float(conc_solver(SG_Z))\n",
+        "\n",
+        "# Mole fraction of Distillate\n",
+        "xD = float(conc_solver(SG_D))\n",
+        "\n",
+        "# Mole fraction of Bottoms\n",
+        "xB = float(conc_solver(SG_B))\n",
+        "\n",
+        "\n",
+        "print(\"The molar fraction of Methanol in the distillate stream is: %1.3f\" % xD)\n",
+        "print(\"The molar fraction of Methanol in the bottoms stream is: %1.3f\" % xB)\n",
+        "print(\"The molar fraction of Methanol in the feed stream is: %1.3f\" % xZ)\n",
+        "\n",
+        "\n",
+        "\n",
+        "### END SOLUTIONS"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "De31VcuNfeFt"
+      },
+      "source": [
+        "## **3. Vapor-liquid equilibrium (VLE) model**\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FuGr8o9l-dqR"
+      },
+      "source": [
+        "## 3.1. Fit the VLE data points to create a best fit line\n",
+        "\n",
+        "Using the points of VLE obtained from Aspen Plus to generate a best fit line to model VLE. **Hint:** Use Excel or Python."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "04W1iERGHTxO",
+        "outputId": "00590f3f-6c0d-481b-a055-8773532c73de"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    vapor mole frac methanol  liquid mole frac methanol\n",
+            "0                   1.000000                       1.00\n",
+            "1                   0.991953                       0.98\n",
+            "2                   0.983907                       0.96\n",
+            "3                   0.975858                       0.94\n",
+            "4                   0.967805                       0.92\n",
+            "5                   0.959746                       0.90\n",
+            "6                   0.951678                       0.88\n",
+            "7                   0.943597                       0.86\n",
+            "8                   0.935500                       0.84\n",
+            "9                   0.927383                       0.82\n",
+            "10                  0.919242                       0.80\n",
+            "11                  0.911073                       0.78\n",
+            "12                  0.902868                       0.76\n",
+            "13                  0.894623                       0.74\n",
+            "14                  0.886331                       0.72\n",
+            "15                  0.877983                       0.70\n",
+            "16                  0.869572                       0.68\n",
+            "17                  0.861087                       0.66\n",
+            "18                  0.852518                       0.64\n",
+            "19                  0.843852                       0.62\n",
+            "20                  0.835076                       0.60\n",
+            "21                  0.826174                       0.58\n",
+            "22                  0.817129                       0.56\n",
+            "23                  0.807920                       0.54\n",
+            "24                  0.798526                       0.52\n",
+            "25                  0.788920                       0.50\n",
+            "26                  0.779072                       0.48\n",
+            "27                  0.768950                       0.46\n",
+            "28                  0.758514                       0.44\n",
+            "29                  0.747718                       0.42\n",
+            "30                  0.736511                       0.40\n",
+            "31                  0.724832                       0.38\n",
+            "32                  0.712610                       0.36\n",
+            "33                  0.699758                       0.34\n",
+            "34                  0.686178                       0.32\n",
+            "35                  0.671750                       0.30\n",
+            "36                  0.656326                       0.28\n",
+            "37                  0.639734                       0.26\n",
+            "38                  0.621753                       0.24\n",
+            "39                  0.602116                       0.22\n",
+            "40                  0.580482                       0.20\n",
+            "41                  0.556419                       0.18\n",
+            "42                  0.529364                       0.16\n",
+            "43                  0.498575                       0.14\n",
+            "44                  0.463048                       0.12\n",
+            "45                  0.421395                       0.10\n",
+            "46                  0.371637                       0.08\n",
+            "47                  0.310848                       0.06\n",
+            "48                  0.234504                       0.04\n",
+            "49                  0.135217                       0.02\n",
+            "50                  0.000000                       0.00\n"
+          ]
+        }
+      ],
+      "source": [
+        "# imports the csv data into python\n",
+        "url = \"https://raw.githubusercontent.com/chennieXD/McCabe-Thiele/main/LiquidVaporEquil.csv\"\n",
+        "liq_vap_data = pd.read_csv(url)\n",
+        "print(liq_vap_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WyG_uHyiYLQr"
+      },
+      "source": [
+        "## 3.2. Vapor-liquid equilibrium of methanol\n",
+        "\n",
+        "Plot the VLE of methanol using the model generated from linear regression using the format $a+bx+cx^2+dx^3+ex^4+fx^5+gx^6$ and define the equation in the function \"VLE_Eq\"  Plot the 45 degree line on the same plot.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We will do a linear regression fitting of VLE data to a 6th degree polynomial:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "VLE(x) = a + bx + cx^2 + dx^3 + ex^4 + fx^5 + gx^6\n",
+        "\\end{equation}\n",
+        "\n",
+        "where:\n",
+        "\\begin{align*}\n",
+        "x & : \\text{Mole Fraction of Methanol} \\in [0, 1] \\\\\n",
+        "VLE(x) & : \\text{Mole Fraction of Methanol in Vapor} \\\\\n",
+        "a, b, c, d, e, f, g & : \\text{Coefficients to be determined}\n",
+        "\\end{align*}\n",
+        "\n",
+        "The goal is to obtain the coefficients $a, b, c, d, e, f, g$ that best fit the VLE data. The model is fitted using linear regression in matrix form, following these equations:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\mathbf{X}^T = \\text{np.transpose}(X)\n",
+        "\\end{equation}\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\mathbf{XX}^{-1} = \\text{np.linalg.inv}(\\mathbf{X}^T \\cdot X)\n",
+        "\\end{equation}\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\mathbf{X}^T \\mathbf{Y} = \\mathbf{X}^T \\cdot Y\n",
+        "\\end{equation}\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\text{coefficients} = \\mathbf{XX}^{-1} \\cdot (\\mathbf{X}^T \\cdot Y)\n",
+        "\\end{equation}\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "l5jgZIR5MW5s"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Function for matrix operations\n",
+        "\n",
+        "def calculate_regression_coefficients(X, Y):\n",
+        "    \"\"\"solving for the coefficients of the 6th degree polynomials\n",
+        "\n",
+        "    Arguments:\n",
+        "        X = the polynomial we are trying to fit, based on the liquid vapor fraction\n",
+        "        Y = feature of the data, in this case the vapor fraction\n",
+        "\n",
+        "    Returns:\n",
+        "        Coefficient of each term of the polynomials\n",
+        "\n",
+        "    \"\"\"\n",
+        "    ### BEGIN SOLUTION\n",
+        "\n",
+        "    XT = np.transpose(X)\n",
+        "    XXinv = np.linalg.inv(XT.dot(X))\n",
+        "    XTy = XT.dot(Y)\n",
+        "    return XXinv.dot(XTy)\n",
+        "\n",
+        "    ### END SOLUTION"
+      ],
+      "metadata": {
+        "id": "ykIL9J_IeWjk"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 556
+        },
+        "id": "4djK6hGuh-f1",
+        "outputId": "17fcdc58-4046-4925-eb82-f02ca638930b"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "liqvapy = liq_vap_data[\"vapor mole frac methanol\"]\n",
+        "liqvapx = liq_vap_data[\"liquid mole frac methanol\"]\n",
+        "\n",
+        "# Feature matrix (store in 'X')\n",
+        "X = np.column_stack([np.ones(len(liqvapx))] + [liqvapx**i for i in range(1, 7)])\n",
+        "Y = np.array(liqvapy)\n",
+        "\n",
+        "# Calculate regression coefficients\n",
+        "beta_hat = calculate_regression_coefficients(X, Y)\n",
+        "\n",
+        "# Separate data preparation from plotting\n",
+        "x = liqvapx\n",
+        "y = X.dot(beta_hat)\n",
+        "\n",
+        "# create empty lists to store x and y coordinates for the VLE equilibrium graph\n",
+        "x = liqvapx\n",
+        "liqvapy = liqvapy\n",
+        "\n",
+        "def VLE_eq(x):\n",
+        "    \"\"\"plot the \"stair case\" line of the McCabe-Thiele diagram\n",
+        "\n",
+        "    Arguments:\n",
+        "        x: x value on the VLE diagram\n",
+        "\n",
+        "    Returns:\n",
+        "        liqvap: y value on the VLE diagram\n",
+        "\n",
+        "    \"\"\"\n",
+        "    liqvap = (\n",
+        "        beta_hat[0]\n",
+        "        + beta_hat[1] * x\n",
+        "        + beta_hat[2] * x**2\n",
+        "        + beta_hat[3] * x**3\n",
+        "        + beta_hat[4] * x**4\n",
+        "        + beta_hat[5] * x**5\n",
+        "        + beta_hat[6] * x**6\n",
+        "    )\n",
+        "    return liqvap\n",
+        "\n",
+        "\n",
+        "y = VLE_eq(x)\n",
+        "\n",
+        "# Plot the VLE line\n",
+        "fig = plt.figure(figsize=(6, 6))\n",
+        "plt.plot(liqvapx, liqvapy, \"o\", label=\"Data Points\")\n",
+        "plt.plot(x, y, color=\"black\", label=\"Modeled Equation VLE Line\")\n",
+        "plt.plot([0, 1], [0, 1], color=\"orange\", linestyle=\":\", label=\"45° Line\")\n",
+        "plt.xlabel(\"Liquid Mole Fraction of Methanol\", fontsize=10)\n",
+        "plt.ylabel(\"Vapor Mole Fraction of Methanol\", fontsize=10)\n",
+        "plt.xticks(fontsize=10)\n",
+        "plt.yticks(fontsize=10)\n",
+        "plt.tick_params(direction=\"in\", top=True, right=True)\n",
+        "plt.title(\"VLE Equilibrium of Methanol\", fontsize=10)\n",
+        "plt.legend(fontsize=10, bbox_to_anchor=(1.0, 0.15), borderaxespad=0)\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xpHXH5xD-6as"
+      },
+      "source": [
+        "## 3.3. Plot the McCabe-Thiele for a Total Reflux Run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZiG2JcDWfyPn"
+      },
+      "source": [
+        "## 3.3.a. Stepping line\n",
+        "\n",
+        "The stepping line connects the points that represent the mole fraction of Methanol in each stage of the distillation column. It starts at the bottom mole fraction of Methanol on the 45 degree line and ends after reaching the mole fraction of Methanol in the distillate on the 45 degree line.\n",
+        "\n",
+        "![](../../media/MCabe_thiele_stepping_line.png)\n",
+        "\n",
+        "Create a function that is able to generate the points requried to plot the stepping line."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "mioHfOn4ht0G"
+      },
+      "outputs": [],
+      "source": [
+        "def stair(slope, y_intercept, x_start, y_start, y_end, Efficiency=1):\n",
+        "\n",
+        "    \"\"\"plot the \"stair case\" line of the McCabe-Thiele diagram\n",
+        "\n",
+        "    Arguments:\n",
+        "        slope: slope of the line compared to the vapor-liquid equilibrium line\n",
+        "        y_intercept: y_intercept of the line compared to the vapor-liquid equilibrium line\n",
+        "        x_start: the bottom mole fraction of Methanol\n",
+        "        y_start: the mole fraction of the Methanol\n",
+        "        y_end: the distillate mole fraction of the mixture\n",
+        "\n",
+        "    Returns:\n",
+        "        xplot: x cordinates of the stair case\n",
+        "        yplot: y cordinates of the stair case\n",
+        "        n: number of stages in the distillation column\n",
+        "\n",
+        "    \"\"\"\n",
+        "    # establish arrays for the \"stair case\"\n",
+        "    xplot = []\n",
+        "    yplot = []\n",
+        "\n",
+        "    # first point on 45 line\n",
+        "    x = x_start\n",
+        "    y = y_start\n",
+        "    xplot.append(x)\n",
+        "    yplot.append(y)\n",
+        "\n",
+        "    # initial number of stages\n",
+        "    n = 0\n",
+        "\n",
+        "    ### BEGIN SOLUTIONS\n",
+        "\n",
+        "    # while the mole fraction is less than the distillate product\n",
+        "    while y < y_end:\n",
+        "        xplot.append(x)\n",
+        "        # create the equation from slope and y-intercept of equation\n",
+        "        equation = slope * x + y_intercept\n",
+        "        y = ((VLE_eq(x)) - equation) * Efficiency + equation\n",
+        "        yplot.append(y)\n",
+        "        x = (y - y_intercept) / slope\n",
+        "        # sol=solve(Equation)\n",
+        "        # x=sol[0]\n",
+        "\n",
+        "        # append points to list\n",
+        "        xplot.append(x)\n",
+        "        yplot.append(y)\n",
+        "\n",
+        "        # counts the number of stages\n",
+        "        n += 1\n",
+        "    return (xplot, yplot, n)\n",
+        "    ### END SOLUTIONS"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PxYdeMPXiJ1h"
+      },
+      "source": [
+        "## 3.3.b. McCabe-Thiele for a total reflux run\n",
+        "\n",
+        "In a total reflux run, there are no feed entering the column and no product leaving the column. The distillate is all refluxed back into the top of the column. The stepping line is plotted along the 45 degree line and the vapor-liquid equilibrium line."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 459
+        },
+        "id": "K9ogeBGqP8rS",
+        "outputId": "fa9461ea-1bce-4bb6-91b7-59d408e94393"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The number of total theoretical stage is  4\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "### BEGIN SOLUTIONS\n",
+        "\n",
+        "# slope and y-intercept of the 45 degree line\n",
+        "slope = 1\n",
+        "y_intercept = 0\n",
+        "Efficiency = 1\n",
+        "\n",
+        "# call stair funciton to generate the points needed to plot\n",
+        "complete_reflux = stair(slope=1, y_intercept=0, x_start=xB, y_start=xB, y_end=xD)\n",
+        "# extract x and y points for the plot\n",
+        "xplot = complete_reflux[0]\n",
+        "yplot = complete_reflux[1]\n",
+        "total_stage = complete_reflux[2]\n",
+        "print(\"The number of total theoretical stage is \", total_stage)\n",
+        "\n",
+        "# McCabe-Thiele diagram\n",
+        "fig = plt.figure(figsize=(4, 4))\n",
+        "# plot the LVE line\n",
+        "plt.plot(liqvapx, liqvapy)\n",
+        "# plot the 45 degree line\n",
+        "plt.plot([0, 1], [0, 1], color=\"orange\", linestyle=\":\")\n",
+        "# plot the VLE line\n",
+        "plt.plot(xplot, yplot, color=\"red\")\n",
+        "plt.xlabel(\"Liquid Mole Fraction \\n of Methanol\", fontsize=10)\n",
+        "plt.ylabel(\"Vapor Mole Fraction \\n of Methanol\", fontsize=10)\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\", top=True, right=True)\n",
+        "plt.title(\"McCabe-Thiele of a total \\n reflux system\", fontsize=10)\n",
+        "plt.legend(\n",
+        "    labels=(\"LVE line\", \"45 line\", \"step line\"),\n",
+        "    fontsize=10,\n",
+        "    bbox_to_anchor=(1.0, 0.23),\n",
+        "    borderaxespad=0,\n",
+        ")\n",
+        "plt.show()\n",
+        "\n",
+        "### END SOLUTIONS"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zp6Kin33_jBE"
+      },
+      "source": [
+        "## **4. Plot the McCabe-Thiele diagram of a feed run**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OVURADZKkp3v"
+      },
+      "source": [
+        "In a more realistic sense, there will be mixture feed entering the column to be separated. As a result, there will also be a distillate stream and a bottoms stream. When there are feed entering the column and product leaving the column, the McCabe-Thiele diagram will have the feed condition (q) line, rectifying line, and stripping line. The rectifying starts at the distillate mole fraction of Methanol and comes down until it crosses the q-line. The q-line starts at the feed mole fraction of Methanol and meets the stripping and rectifying line. The stripping line starts at the bottom mole fraction of Methanol and meets the q-line and rectifying line.  \n",
+        "\n",
+        "![](../../media/MCabe_thiele_diagram.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mwsK8bEMhVPf"
+      },
+      "source": [
+        "## 4.1. Solve for the slope of the q and rectifying line\n",
+        "\n",
+        "Using the relationship below, calculate the slope of the q and rectifying line. q is the mole fraction of liquid in the feed stream and R is the reflux ratio. Store the slope of q-line as **m_q** and slope of rectifying operating line as **m_rec**\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ZbzkD_6hySQ"
+      },
+      "source": [
+        "\\begin{align}\n",
+        "        m_{feed} = \\frac{q}{ q - 1 }\n",
+        "    \\end{align}\n",
+        "\n",
+        "\\begin{align}\n",
+        "        m_{rec} = \\frac{R}{ R + 1 }        \n",
+        "    \\end{align}\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "UMuh1VJ5l1-o"
+      },
+      "outputs": [],
+      "source": [
+        "# Mole fraction of liquid in feed\n",
+        "q = 0.3\n",
+        "# Reflux Ratio\n",
+        "R = 1\n",
+        "\n",
+        "### BEGIN SOLUTIONS\n",
+        "\n",
+        "# slope of rectifying opearting line\n",
+        "m_rec = R / (R + 1)\n",
+        "# slope of feed condition line (q-line)\n",
+        "m_q = q / (q - 1)\n",
+        "\n",
+        "### END SOLUTIONS"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wzlsXB8UjNSj"
+      },
+      "source": [
+        "## 4.2. Intercept of the q-line and the rectifying line, and calculate the slope and y-intercept of the stripping line\n",
+        "\n",
+        "To find the intercept of the q-line and the rectifying line, the point-slope equation of the q-line and the rectifying line can be turned into slope-interecept form and made into a system of linear equations.\n",
+        "\n",
+        "\\begin{equation}\n",
+        "        y-y_q = m_q(x-x_q)\n",
+        "\\end{equation}\n",
+        "\n",
+        "\\begin{align}\n",
+        "        y-y_{rec} = m_{rec}(x-x_{rec})\n",
+        "\\end{align}\n",
+        "\n",
+        "\n",
+        "Matrix form:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\begin{bmatrix}\n",
+        "m_{rec} & -1\\\\\n",
+        "m_{q} & -1\n",
+        "\\end{bmatrix} \\cdot\n",
+        "\\begin{bmatrix}\n",
+        "\tx  \\\\\n",
+        "\ty\n",
+        "\\end{bmatrix} =\n",
+        "\\begin{bmatrix}\n",
+        "\tm_{rec}*x_{rec} - y_{rec}  \\\\\n",
+        "\tm_{q} *x_q - y_q\n",
+        "\\end{bmatrix}\n",
+        "\\end{equation}\n",
+        "\n",
+        "After calculating the intercept, we have two points of the stripping line to calculate the slope and y-intercept to get the slope-intercept form of the equation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "ogUO2OzdtfSD"
+      },
+      "outputs": [],
+      "source": [
+        "### BEGIN SOLUTIONS\n",
+        "\n",
+        "# solve for the  intercept of the rectifying line and q-line\n",
+        "a = np.array([[m_rec, -1], [m_q, -1]])\n",
+        "b = np.array([m_rec * xD - xD, m_q * xZ - xZ])\n",
+        "intercept = np.linalg.solve(a, b)\n",
+        "\n",
+        "# solve for stripping operating line slope and y-intercept\n",
+        "m_strip = (intercept[1] - xB) / (intercept[0] - xB)\n",
+        "y_intercept_strip = xB * (1 - m_strip)\n",
+        "\n",
+        "# y-intercept of the rectifying opearting line\n",
+        "y_intercept_rec = xD * (1 - m_rec)\n",
+        "\n",
+        "### END SOLUTIONS"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0dD5nKV6jjaD"
+      },
+      "source": [
+        "## 4.3. Generate the points needed to plot the stepping line, plot the McCabe-Thiele diagram, and calculate the number of stages\n",
+        "\n",
+        "Think where you want the \"stair\" to stop on your plot when you enter the y_start and y_end for the function.  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "FAIXZi5YVJwf"
+      },
+      "outputs": [],
+      "source": [
+        "### BEGIN SOLUTIONS\n",
+        "\n",
+        "# McCabe-Thiele of a feed run\n",
+        "# stripping portion of stepping line generation\n",
+        "stripping_line = stair(\n",
+        "    slope=m_strip,\n",
+        "    y_intercept=y_intercept_strip,\n",
+        "    x_start=xB,\n",
+        "    y_start=xB,\n",
+        "    y_end=intercept[1],\n",
+        ")\n",
+        "xplot_stripping = stripping_line[0]\n",
+        "yplot_stripping = stripping_line[1]\n",
+        "\n",
+        "# rectifying portion of stepping line generation\n",
+        "rectifying_line = stair(\n",
+        "    slope=m_rec,\n",
+        "    y_intercept=y_intercept_rec,\n",
+        "    x_start=(yplot_stripping[-1] - y_intercept_rec) / m_rec,\n",
+        "    y_start=yplot_stripping[-1],\n",
+        "    y_end=xD,\n",
+        ")\n",
+        "xplot_rectifying = rectifying_line[0]\n",
+        "yplot_rectifying = rectifying_line[1]\n",
+        "\n",
+        "# complie the x and y points to respective lists\n",
+        "xplot = xplot_stripping + xplot_rectifying\n",
+        "yplot = yplot_stripping + yplot_rectifying\n",
+        "\n",
+        "### END SOLUTIONS"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "### BEGIN SOLUTIONS\n",
+        "\n",
+        "# McCabe-Thiele diagram\n",
+        "fig = plt.figure(figsize=(4, 4))\n",
+        "# plot the LVE line\n",
+        "plt.plot(liqvapx, liqvapy)\n",
+        "# plot the 45 degree line\n",
+        "plt.plot([0, 1], [0, 1], color=\"orange\", linestyle=\":\")\n",
+        "# plot the step line\n",
+        "plt.plot(xplot, yplot, color=\"red\")\n",
+        "# plot the SOP\n",
+        "plt.plot([xB, intercept[0]], [xB, intercept[1]], color=\"purple\")\n",
+        "# plot the ROP\n",
+        "plt.plot([xD, intercept[0]], [xD, intercept[1]], color=\"green\")\n",
+        "# plot the q-line\n",
+        "plt.plot([xZ, intercept[0]], [xZ, intercept[1]], color=\"grey\")\n",
+        "# Formating the plot\n",
+        "plt.xlabel(\"Liquid Mole Fraction \\n of Methanol\", fontsize=10)\n",
+        "plt.ylabel(\"Vapor Mole Fraction \\n of Methanol\", fontsize=10)\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\", top=True, right=True)\n",
+        "plt.title(\"McCabe-Thiele of a \\n feed system\", fontsize=10)\n",
+        "plt.legend(\n",
+        "    labels=(\"LVE line\", \"45 line\", \"step line\", \"SOP\", \"ROP\", \"q-line\"),\n",
+        "    fontsize=10,\n",
+        "    bbox_to_anchor=(1.01, 0.43),\n",
+        "    borderaxespad=0,\n",
+        ")\n",
+        "plt.show()\n",
+        "total_stage = stripping_line[2] + rectifying_line[2]\n",
+        "print(\"The number of total theoretical stage is \", total_stage)\n",
+        "\n",
+        "### END SOLUTIONS"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 459
+        },
+        "id": "JsQlY8ycS6jI",
+        "outputId": "d8edee15-3f20-4b6b-c9b1-36af2c4a516d"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The number of total theoretical stage is  6\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OJBs_kYcdD4j"
+      },
+      "source": [
+        "## **5. Discussion Question 1**\n",
+        "From the McCabe-Thiele diagram plotted above what can we say about the phase of the feed stream? **Hint: Look at the slope of the q-line.**\n",
+        "\n",
+        "![](../../media/MCabe_thiele_q1.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LoP3LjSNMsOf"
+      },
+      "source": [
+        "**Your Answer:**\n",
+        "\n",
+        "Since the q-line slope is between 0 and -1, it tells us that the feed stream is mostly a vapor feed. This conclusion is in line with the problem statement where q, the mole fraction of liquid, is 0.36, which means the feed is mostly vapor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R8BY1lHjR36P",
+        "outputId": "9726ce20-231a-42d4-9f5e-7c6d03867f03"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " \n"
+          ]
+        }
+      ],
+      "source": [
+        "### BEGIN SOLUTIONS\n",
+        "\"\"\"\n",
+        "Since the q-line slope is between 0 and -1, it tells us that the feed stream is mostly a vapor feed.\n",
+        "This conclusion is in line with the problem statement where q, the mole fraction of liquid, is 0.36, which means the feed is mostly vapor.\n",
+        "\"\"\"\n",
+        "print(\" \")\n",
+        "### END SOLUTIONS"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BmdDtWrDNMdF"
+      },
+      "source": [
+        "## **6. Discussion Question 2**\n",
+        "\n",
+        "Why is the McCabe-Thiele diagram plotted above is unrealistic in the real world? What would you change to make the plot more realistic. Plot the modified plot and talk about how the number of stages changed. **Hint: Think efficiency. Is there ever 100% efficiency in the real world? A distillation column typically have a efficiency of 60%.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TlPtqtgHV1Wq"
+      },
+      "source": [
+        "**Your Answer:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "h_aWc3gSR36Q",
+        "outputId": "f4994c33-7cc3-4887-ebe9-c0319e3b922d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "### BEGIN SOLUTION\n",
+        "\"\"\" The diagram plotted above uses 100% efficiency in the column.\n",
+        "Since 100% efficiency is not possible in the real world, it makes the plot above unrealistic.\n",
+        "A more realistic plot would use a efficiency of around 60% like the one plotted below.\n",
+        "The number of stages increased from 5 to 9, which makes sense because less efficiency means more stages are required.\n",
+        "\"\"\"\n",
+        "print()\n",
+        "### END SOLUTION"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "6wmItV90cjwc",
+        "outputId": "8c89be05-05c5-4c4d-f55d-0dda0c8881df"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The number of total theoretical stage is  11\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Efficency\n",
+        "\n",
+        "### BEGIN SOLUTIONS\n",
+        "efficiency = 0.6\n",
+        "### END SOLUTIONS\n",
+        "\n",
+        "# McCabe-Thiele of a feed run\n",
+        "# stripping portion of stepping line generation\n",
+        "stripping_line = stair(\n",
+        "    slope=m_strip,\n",
+        "    y_intercept=y_intercept_strip,\n",
+        "    x_start=xB,\n",
+        "    y_start=xB,\n",
+        "    y_end=intercept[1],\n",
+        "    Efficiency=efficiency,\n",
+        ")\n",
+        "xplot_stripping = stripping_line[0]\n",
+        "yplot_stripping = stripping_line[1]\n",
+        "\n",
+        "# rectifying portion of stepping line generation\n",
+        "rectifying_line = stair(\n",
+        "    slope=m_rec,\n",
+        "    y_intercept=y_intercept_rec,\n",
+        "    x_start=(yplot_stripping[-1] - y_intercept_rec) / m_rec,\n",
+        "    y_start=yplot_stripping[-1],\n",
+        "    y_end=xD,\n",
+        "    Efficiency=efficiency,\n",
+        ")\n",
+        "xplot_rectifying = rectifying_line[0]\n",
+        "yplot_rectifying = rectifying_line[1]\n",
+        "\n",
+        "# complie the x and y points to respective liststogether\n",
+        "xplot = xplot_stripping + xplot_rectifying\n",
+        "yplot = yplot_stripping + yplot_rectifying\n",
+        "\n",
+        "\n",
+        "# McCabe-Thiele diagram\n",
+        "fig = plt.figure(figsize=(4, 4))\n",
+        "# plot the LVE line\n",
+        "plt.plot(liqvapx, liqvapy)\n",
+        "# plot the 45 degree line\n",
+        "plt.plot([0, 1], [0, 1], color=\"orange\", linestyle=\":\")\n",
+        "# plot the step line\n",
+        "plt.plot(xplot, yplot, color=\"red\")\n",
+        "# plot the SOP\n",
+        "plt.plot([xB, intercept[0]], [xB, intercept[1]], color=\"purple\")\n",
+        "# plot the ROP\n",
+        "plt.plot([xD, intercept[0]], [xD, intercept[1]], color=\"green\")\n",
+        "# plot the q-line\n",
+        "plt.plot([xZ, intercept[0]], [xZ, intercept[1]], color=\"grey\")\n",
+        "# Formating the plot\n",
+        "plt.xlabel(\"Liquid Mole Fraction \\n of Methanol\", fontsize=10)\n",
+        "plt.ylabel(\"Vapor Mole Fraction \\n of Methanol\", fontsize=10)\n",
+        "plt.xticks(fontsize=7)\n",
+        "plt.yticks(fontsize=7)\n",
+        "plt.tick_params(direction=\"in\", top=True, right=True)\n",
+        "plt.title(\"McCabe-Thiele of a \\n feed system\", fontsize=10)\n",
+        "plt.legend(\n",
+        "    labels=(\"LVE line\", \"45 line\", \"step line\", \"SOP\", \"ROP\", \"q-line\"),\n",
+        "    fontsize=10,\n",
+        "    bbox_to_anchor=(1.0, 0.43),\n",
+        "    borderaxespad=0,\n",
+        ")\n",
+        "plt.show()\n",
+        "total_stage = stripping_line[2] + rectifying_line[2]\n",
+        "print(\"The number of total theoretical stage is \", total_stage)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **7. Discussion Question 3: Evaluating the minimum reflux rate of the system (Graphical)**"
+      ],
+      "metadata": {
+        "id": "HtyCIZXTqodc"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Under the conditions given by the problem statement, what is the theoretical minimum reflux of the system? And describe how you would find this. Hint: At the minimum reflux rate, it would require infinite number of stage to achieve separation. **Do this by hand and submit**\n",
+        "\n",
+        "---\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "QAAt5d64qtJB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Mole fraction of Feed\n",
+        "xZ = float(conc_solver(SG_Z))\n",
+        "\n",
+        "# Mole fraction of Distillate\n",
+        "xD = float(conc_solver(SG_D))\n",
+        "\n",
+        "# Mole fraction of Bottoms\n",
+        "xB = float(conc_solver(SG_B))\n",
+        "\n",
+        "##BEGIN SOLUTION\n",
+        "\n",
+        "# Mole fraction of liquid in feed\n",
+        "q = 0.3\n",
+        "# Reflux Ratio\n",
+        "R = 0.65\n",
+        "\n",
+        "\"\"\" The total reflux gives out the maximum number of reflux that can be theoretically achieved for a given system; this was discussed in discussion 5.\n",
+        "The minimum reflux is the opposite, as the reflux rate gets smaller, the q-line and the operating line gets closer to the VLE line. Therefore, N gets larger as it requires more stepping to reach xD\n",
+        "Graphically, this is where the q-line intersects with the VLE line given a specified q.\n",
+        "In traditional chemical engineering term, this is called a \"Pinch\"\n",
+        "\"\"\"\n",
+        "\n",
+        "###END SOLUTION\n",
+        "\n",
+        "# slope of rectifying opearting line\n",
+        "m_rec = R / (R + 1)\n",
+        "# slope of feed condition line (q-line)\n",
+        "m_q = q / (q - 1)\n",
+        "\n",
+        "\n",
+        "# solve for the  intercept of the rectifying line and q-line\n",
+        "a = np.array([[m_rec, -1], [m_q, -1]])\n",
+        "b = np.array([m_rec * xD - xD, m_q * xZ - xZ])\n",
+        "intercept = np.linalg.solve(a, b)\n",
+        "# solve for stripping operating line slope and y-intercept\n",
+        "m_strip = (intercept[1] - xB) / (intercept[0] - xB)\n",
+        "y_intercept_strip = xB * (1 - m_strip)\n",
+        "\n",
+        "# y-intercept of the rectifying opearting line\n",
+        "y_intercept_rec = xD * (1 - m_rec)\n",
+        "\n",
+        "# McCabe-Thiele diagram\n",
+        "fig = plt.figure(figsize=(4, 4))\n",
+        "\n",
+        "# plot the LVE line\n",
+        "plt.plot(liqvapx, liqvapy)\n",
+        "\n",
+        "# plot the 45 degree line\n",
+        "plt.plot([0, 1], [0, 1], color=\"orange\", linestyle=\":\")\n",
+        "\n",
+        "# plot the SOP\n",
+        "plt.plot([xB, intercept[0]], [xB, intercept[1]], color=\"purple\")\n",
+        "# plot the ROP\n",
+        "plt.plot([xD, intercept[0]], [xD, intercept[1]], color=\"green\")\n",
+        "# plot the q-line\n",
+        "plt.plot([xZ, intercept[0]], [xZ, intercept[1]], color=\"grey\")\n",
+        "\n",
+        "# Add points for xD and xB\n",
+        "plt.scatter(xD, xD, color=\"blue\", label=\"xD\")\n",
+        "plt.scatter(xB, xB, color=\"red\", label=\"xB\")\n",
+        "\n",
+        "# Formating the plot\n",
+        "plt.xlabel(\"Liquid Mole Fraction of Methanol\", fontsize=10)\n",
+        "plt.ylabel(\"Vapor Mole Fraction of Methanol\", fontsize=10)\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\", top=True, right=True)\n",
+        "plt.title(\"McCabe-Thiele of a feed system\", fontsize=10)\n",
+        "plt.legend(\n",
+        "    labels=(\"LVE line\", \"45 line\", \"SOP\", \"ROP\", \"q-line\", \"xD\", \"xB\"),\n",
+        "    fontsize=10,\n",
+        "    bbox_to_anchor=(1.0, 0.43),\n",
+        "    borderaxespad=0,\n",
+        ")\n",
+        "plt.show()\n",
+        "\n",
+        "# print(intercept)\n",
+        "# print(m_q)\n",
+        "\n",
+        "# print(xB)\n",
+        "# print(xD)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 408
+        },
+        "id": "JAW0SaIlT5Yz",
+        "outputId": "615471da-3247-456f-d923-92665c46a682"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##8. **Discussion Question 4: Evaluating the minimum reflux of the system (Newton's method)**"
+      ],
+      "metadata": {
+        "id": "VRE59ad9bSqM"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now that we have graphically identified the location of the pinch, we can employ our data science skills to precisely pinpoint this critical point. As previously mentioned, the pinch occurs at the intersection of the q-line and the VLE line. At this intersection, we can determine the minimum reflux ratio, R_min, by finding the slope of the ROP line.\n",
+        "\n",
+        "To approach this problem computationally, we can use numerical methods, such as Newton's method, to find the solution where the VLE line and the q-line intersect. The goal is to determine the composition at this intersection point, which corresponds to the exact location of the pinch and allows us to calculate R_min"
+      ],
+      "metadata": {
+        "id": "7v24FezT0mV-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1. Define the VLE curve equation\n",
+        "\n",
+        "2. Define the q-line equation\n",
+        "\n",
+        "3. Implement Newton's method to find the intersection point of the VLE curve and the q-line. This intersection point corresponds to the pinch location. (Solution of a 6th degree polynomial to a line)\n",
+        "\n",
+        "4. Once the intersection point is found, calculate R_min\n",
+        "   \n"
+      ],
+      "metadata": {
+        "id": "E3pOTtzq38Rt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "###BEGIN SOLUTION\n",
+        "\n",
+        "#defining the VLE-line\n",
+        "def polynomial(x):\n",
+        "\n",
+        "    liqvap = (\n",
+        "        beta_hat[0]\n",
+        "        + beta_hat[1] * x\n",
+        "        + beta_hat[2] * x**2\n",
+        "        + beta_hat[3] * x**3\n",
+        "        + beta_hat[4] * x**4\n",
+        "        + beta_hat[5] * x**5\n",
+        "        + beta_hat[6] * x**6\n",
+        "    )\n",
+        "    return liqvap\n",
+        "\n",
+        "# Define the equation of the q-line\n",
+        "def line(x):\n",
+        "    return m_q*x + xZ/(1-q)\n",
+        "\n",
+        "# Define the derivative of the polynomial function\n",
+        "def polynomial_derivative(x):\n",
+        "    \"\"\"Calculate the derivative of the polynomial function.\n",
+        "\n",
+        "    Arguments:\n",
+        "        x: x value on the VLE diagram\n",
+        "\n",
+        "    Returns:\n",
+        "        liqvap_derivative: Derivative of the polynomial at x\n",
+        "    \"\"\"\n",
+        "\n",
+        "    liqvap_derivative = (\n",
+        "        beta_hat[1]\n",
+        "        + 2 * beta_hat[2] * x\n",
+        "        + 3 * beta_hat[3] * x**2\n",
+        "        + 4 * beta_hat[4] * x**3\n",
+        "        + 5 * beta_hat[5] * x**4\n",
+        "        + 6 * beta_hat[6] * x**5\n",
+        "    )\n",
+        "\n",
+        "    return liqvap_derivative\n",
+        "\n",
+        "# Initial guess for the intersection point\n",
+        "x0 = 0.0\n",
+        "\n",
+        "# Set a tolerance level for the approximation\n",
+        "tolerance = 1e-6\n",
+        "\n",
+        "# Maximum number of iterations\n",
+        "max_iterations = 100\n",
+        "\n",
+        "# Newton's method to find the intersection\n",
+        "\n",
+        "for i in range(max_iterations):\n",
+        "    f_x0 = polynomial(x0) - line(x0)\n",
+        "    if abs(f_x0) < tolerance:\n",
+        "        break\n",
+        "    x0 = x0 - f_x0 / polynomial_derivative(x0)\n",
+        "\n",
+        "# Check if the maximum number of iterations is reached\n",
+        "if i == max_iterations - 1:\n",
+        "    print(\"Maximum iterations reached without convergence.\")\n",
+        "else:\n",
+        "    print(\"Approximate intersection point:\", x0)\n",
+        "    print(\"Approximate y-coordinate:\", line(x0))\n",
+        "\n",
+        "# Plot the polynomial\n",
+        "x_values = np.linspace(0, 1, 100)  # Adjust the range and number of points as needed\n",
+        "polynomial_values = [polynomial(x) for x in x_values]\n",
+        "\n",
+        "#plotting the line\n",
+        "plt.plot(x_values, polynomial_values, label=\"VLE\")\n",
+        "x_values_line = np.linspace(x0,xZ,100)\n",
+        "line_values = [line(x) for x in x_values_line]\n",
+        "plt.plot(x_values_line, line_values, label=\"q-line\")\n",
+        "\n",
+        "\n",
+        "plt.scatter(x0, line(x0), color=\"red\", label=\"Pinch at Rmin\", marker=\"o\")\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Add a 45-degree line\n",
+        "fortyfive_deg_line = [x for x in x_values]\n",
+        "\n",
+        "#plot of the 45 degree line\n",
+        "plt.plot(x_values, fortyfive_deg_line, label=\"45-degree Line\", linestyle=\"--\")\n",
+        "\n",
+        "#plot of xD point\n",
+        "plt.scatter(xD, xD, color=\"blue\", label=\"xD\")\n",
+        "\n",
+        "#plot of xB point\n",
+        "plt.scatter(xB, xB, color=\"red\", label=\"xB\")\n",
+        "\n",
+        "#plotting SOP line\n",
+        "plt.plot([xB,x0],[xB,line(x0)],label=\"SOP\")\n",
+        "\n",
+        "#plotting ROP line\n",
+        "plt.plot([xD,x0],[xD,line(x0)], label=\"ROP\")\n",
+        "\n",
+        "#labelling\n",
+        "plt.xlabel(\"Liquid fraction of methanol\")\n",
+        "plt.ylabel(\"Vapor fraction of methanol\")\n",
+        "plt.legend()\n",
+        "plt.grid()\n",
+        "plt.show()\n",
+        "\n",
+        "R_min = (xD - line(x0))/(line(x0) - x0)\n",
+        "\n",
+        "print(\"R_min is: \",R_min)\n",
+        "\n",
+        "###END SOLUTION"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 503
+        },
+        "id": "cyADEW_ZUSg9",
+        "outputId": "6d69b372-71f9-48e6-e2ef-b4cf5eaebd42"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Approximate intersection point: 0.2890625846948517\n",
+            "Approximate y-coordinate: 0.6612101117858299\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "R_min is:  0.6409329047427269\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "LTNaey22j1Br"
+      },
+      "execution_count": 19,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

From 6c451f1cd7547179c9074683f9b68376bad75f3e Mon Sep 17 00:00:00 2001
From: syeoo <149696518+syeoo@users.noreply.github.com>
Date: Fri, 8 Dec 2023 15:25:37 -0500
Subject: [PATCH 4/4] Steven and Farbod's Project

Addition Polymerization by Steven and Farbod
---
 .../contrib-dev/Addition_Polymerization.ipynb | 1531 +++++++++++++++++
 1 file changed, 1531 insertions(+)
 create mode 100644 notebooks/contrib-dev/Addition_Polymerization.ipynb

diff --git a/notebooks/contrib-dev/Addition_Polymerization.ipynb b/notebooks/contrib-dev/Addition_Polymerization.ipynb
new file mode 100644
index 00000000..beffa3a8
--- /dev/null
+++ b/notebooks/contrib-dev/Addition_Polymerization.ipynb
@@ -0,0 +1,1531 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KUdg15uf30TU"
+      },
+      "source": [
+        "# Addition Polymerization\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Prepared by: Farbod Shirinichi fshirini@nd.edu and Steven Yeo syeo@nd.edu"
+      ],
+      "metadata": {
+        "id": "xJ5qJj_3hN8R"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from scipy.integrate import solve_ivp\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.optimize import least_squares"
+      ],
+      "metadata": {
+        "id": "cDc6V8rkoayG"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iCqLvLrU30TW",
+        "tags": []
+      },
+      "source": [
+        "## 1. Introduction:\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1.1 Learning Objective:\n",
+        "After studying this notebook and your notes, completing the activities, asking questions in class, you should be able to:\n",
+        "* Usage of the Reaction Engineering for polymerization reactions\n",
+        "* Building a system of ODE\n",
+        "* Using numerical methods to solve for the ODE\n",
+        "* Comparing different numerical method techniques\n",
+        "* Build synthetic data\n",
+        "* statistical analysis\n",
+        "\n",
+        "    *ODE int package (e.g. scipy.integrate.odeint)\n"
+      ],
+      "metadata": {
+        "id": "o1BCK9eUdsw5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rA9hw3ZvpfyM"
+      },
+      "source": [
+        "### 1.2 Data Source:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GekBqSX1pfyN"
+      },
+      "source": [
+        "Fogler, H. S. (2010). Essentials of chemical reaction engineering. Pearson Education.\n",
+        "\n",
+        "Hicks, J. (2023, October). Addition Polymerization. Advanced Chemical Reaction Engineering. University of Notre Dame, Notre Dame, IN.\n",
+        "\n",
+        "Hill, C. G., & Root, T. W. (2014). Introduction to chemical engineering kinetics and reactor design. John Wiley & Sons.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ROoY03eUpfyQ"
+      },
+      "source": [
+        "### 1.3 Introduction:\n",
+        "\n",
+        "Polymerization is a fundamental chemical process of significant importance in various industries and scientific fields. It involves the creation of long chains by repeating monomer units to form polymers, which find diverse applications, ranging from plastics and rubber to advanced materials and biotechnology. Control over the polymerization process is crucial for several reasons. As a result, parameters in the design of polymerization reactions become an essential part of polymer production, and understanding the mechanism is crucial for anyone involved in polymer production and utilization.\n",
+        "\n",
+        "Addition polymerization is a pivotal process in polymer chemistry wherein monomers containing unsaturated double or triple bonds undergo a chain reaction to form polymers. This reaction classically occurs devoid of byproducts, resulting in the sequential addition of monomers to generate extended chains.\n",
+        "\n",
+        "The mechanism of addition polymerization is expounded through various theoretical frameworks:\n",
+        "\n",
+        "#### **Free Radical Polymerization Theory**\n",
+        "\n",
+        "This fundamental theory elucidates the polymerization process involving the creation and propagation of free radicals. Initiation is triggered by various stimuli like heat, light, or chemical initiators (e.g., peroxides, azo compounds). These initiators engender free radicals that initiate the polymerization by attacking the double bond of the monomer. The resultant chain reaction continues as more monomers are added to the growing chain, culminating in termination either by radical combination or disproportionation.\n",
+        "\n",
+        "#### **Anionic Polymerization Theory**\n",
+        "\n",
+        "This theory centers on the formation and involvement of anionic species in the polymerization process. Initiation occurs via the generation of anions from initiators such as alkali metals, alkyl lithium, or Grignard reagents. These anions act on monomers by attacking their double bonds, leading to chain growth. Termination transpires upon neutralization of the anionic species, often by proton donation or other terminating agents.\n",
+        "\n",
+        "#### **Cationic Polymerization Theory**\n",
+        "\n",
+        "In contrast to anionic polymerization, this theory revolves around the generation and activity of cationic species. Initiation involves the creation of carbocations through the action of Lewis acids or other initiating agents. These carbocations react with electron-rich double bond-containing monomers, facilitating chain elongation. Termination events occur upon neutralization of the carbocationic intermediates.\n",
+        "\n",
+        "#### **Coordination Polymerization Theory**\n",
+        "\n",
+        "This theory integrates transition metal catalysts, like Ziegler-Natta catalysts, that coordinate with monomers, enabling their insertion into metal-carbon bonds. This process is notably prominent in olefin polymerizations, such as ethylene or propylene, where these catalysts facilitate controlled chain growth.\n",
+        "\n",
+        "Each theoretical framework delineates the addition polymerization process from distinct vantage points, emphasizing the pivotal roles of initiators, radicals, anions, cations, or catalysts. The selection of a particular theory often depends on the specific monomers engaged and the reaction conditions governing the polymerization.\n",
+        "\n",
+        "Oh in this notebook, we are focusing on addition polymerization"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Addition Polymerization Reaction:\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "YZvUKnHYXobm"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2.1 Simplest Polymerization Reaction:"
+      ],
+      "metadata": {
+        "id": "zz4DL9MJZC2F"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "$$ A+nM → AMn $$\n",
+        "\n",
+        "A: Initiator\n",
+        "\n",
+        "M: Monomer\n",
+        "\n",
+        "n Repeat Units\n",
+        "\n",
+        "The equation above governs the simplest addition polymerization. To find the concentrations of each AMn, we will deconstruct this reaction into multiple pieces.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "j2mbo7D5XrRv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2.2 The growth of the polymers:\n",
+        "\n",
+        "Each monomer will react with A, and forms a product. this becomes a reaction in series of different polymer length. and we describe the rate of each reaction of each monomer as shown by the equations below"
+      ],
+      "metadata": {
+        "id": "FcTgvg_vphRv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        ":$$ A+M → AM,$$  $$r_1=k_1[A][M]$$\n",
+        "\n",
+        "$$ AM + M → AM 2$$\n",
+        "$$r_2=k_1[AM][M]$$\n",
+        "\n",
+        "$$ AM2 + M → AM 3$$\n",
+        "$$r_3=k_3[AM2][M]$$\n",
+        "$$...$$\n",
+        "\n",
+        "$$AM_j + M → AM_{j+1} ,   r_{j+1} = k [M][AM_{j+1}]$$\n"
+      ],
+      "metadata": {
+        "id": "hHYOnQZtp860"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###2.3 Describing the equations as a system of ODE:"
+      ],
+      "metadata": {
+        "id": "i-jKAu7_qVg8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Consider system of equations\n",
+        "\n",
+        "To find evaluate the concentration of the species at each point in time, we need to solve the IVP ODE for\n",
+        "\n",
+        "- \\( [M] \\) denotes the concentration of the monomer.\n",
+        "- \\( [A] \\) represents the concentration of the initiator\n",
+        "- \\( [AMj] \\) represents the concentration of the polymer product with j repeating units\n",
+        "\n",
+        "\n",
+        "The rate equation governing the polymerization process can be represented as a set of the equations described below\n",
+        "\n",
+        "For [A], the ODE is given by:\n",
+        "\n",
+        "$$ \\frac{d[A]}{dt} = -k_1[A][M] $$\n",
+        "\n",
+        "For [Amj], the ODE is given by:\n",
+        "\n",
+        "\n",
+        "$$ r j = \\frac{d[AM_j]}{dt} = k_j[A][M_{(j-1)}]- k_{j+1}[AM_j][M] $$"
+      ],
+      "metadata": {
+        "id": "AIKFpKnjqbJh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "We also have to write the rate law for monomer\n",
+        "\n",
+        "$$ \\frac{d[M]}{dt}=-k_1[A][M]-k_2[AM][M]-k_3[AM2][M]-... $$\n",
+        "\n",
+        "**Assumption: **\n",
+        " If we consider rate constants are all equal k=k1=k2=k3=...\n",
+        "so\n",
+        "\n",
+        "$$\\frac{d[AM_j]}{dt} = -k[M]( \\sum_{j=0}^{\\infty} AM_j ) $$\n",
+        "$$  \\sum_{j=0}^{\\infty} \\frac{d[AMj]}{dt}= -k_1[A][M]+k_1[A][M]-k_2[AM][M]+k_2[AM][M]-k_3[AM2][M]+...$$\n",
+        "$$ \\sum_{j=0}^{\\infty} \\frac{d[AMj]}{dt}=0+ \\sum_{j=0}^{\\infty} AM_j= Constant$$\n",
+        "let's name:\n",
+        "\n",
+        "$$\\sum_{j=0}^{\\infty} AM_j=[A]_0$$\n"
+      ],
+      "metadata": {
+        "id": "Puf_yYTcy4ZJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Class Activity:** Discuss why the equation below is a constant\n",
+        "$$\\sum_{j=0}^{\\infty} AM_j=[A]_0$$\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "Gy27uyCGNLc7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Answer**\n",
+        "This is explained by the mass balance equation. Since there is no flow in or out of the reactor, the total concentration of A is determined by the sum of reacted A, in which case all the A's are reacted to become AMj"
+      ],
+      "metadata": {
+        "id": "Sz2yxfHbKVIi"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###2.4 Solving the system of ODEs Analytically:\n",
+        "\n",
+        "\n",
+        "solving [M]\n",
+        "$$ [M] = [M]_0 e^{-k[A]_0 t} $$\n",
+        "\n",
+        "\n",
+        "---\n",
+        "Solving for [A]\n",
+        "$$ \\frac{d[A]}{dt}= -k[A][M]=-k[A][M]_0 e^{-k[A]_0 t} $$\n",
+        "$$ \\frac{d[A]}{A}=-k[M]_0 e^{-k[A]_0 t}dt$$\n",
+        "$$[A]=[A]_0e^{\\frac{-[M]_0}{[A]_0}[1-e^{-k[A]_0 t}]}$$\n",
+        "\n",
+        "\n",
+        "---\n",
+        " by plug in the [A] into the following formula wecould find [AM]:\n",
+        " $$ \\frac{d[AM]}{dt} = k[A][M]- k[AM][M] $$\n",
+        " so in the last code we found [AM] and by substiuding the [AM] in the $$ \\frac{d[AM2]}{dt} = k[A][M]- k[AM][M] $$\n",
+        "the [AM2] could be found and by following the same methode we could calculate all the [AMj]"
+      ],
+      "metadata": {
+        "id": "J-W8IzpJ0QWy"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2.5 Discussion:\n",
+        "The main analytical solution is presented here. For practice, try solving it yourself to confirm if you arrive at the same outcome.\n",
+        "\n",
+        "  Given the complexity and time-consuming nature of solving these equations analyticaly, which methods and packages would you recommend for effectively addressing these equations?\n",
+        "\n",
+        "  Would the equations remain consistent in structure if we were to introduce termination or initiation processes, and what implications might this have on their solutions?"
+      ],
+      "metadata": {
+        "id": "B0lLIY6j047U"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2.6 What if we have a termination step??\n",
+        "This model outlines a basic addition polymerization framework. Different types of addition polymerization involve various steps as it shows in the table below, with termination being a crucial and common process. However, including termination steps complicates the system of ODEs. To solve these complex equations efficiently, Python's libraries provide numerical solvers that streamline the process\n",
+        "\n",
+        "![rxn_table](https://ndcbe.github.io/data-and-computing/_images/rxn_equation.png)\n",
+        "\n",
+        "\n",
+        "$$  Table R7.1-1: From Elements of Chemical Reaction Engineering by  H. Scott Fogler $$"
+      ],
+      "metadata": {
+        "id": "nm3u28XK7qvS"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###3- Statement of the problem:\n",
+        "\n",
+        "We will use Python's function of scipy optimize to solve the ODE using a IVP problem, and integrate it accordingly to find the concentrations at each point in time\n",
+        "\n",
+        "\n",
+        "\n",
+        "To pinpoint the peak concentration of a specific polymer at a desired molecular weight and optimize yield in polymerization reactions, solving ordinary differential equations (ODEs) is essential. Python, with its potent numerical libraries like NumPy and SciPy, provides a robust platform to swiftly solve these equations. Utilizing Python's computational prowess enables accurate identification of the highest polymer concentration at a targeted molecular weight, aiding in fine-tuning conditions for maximizing yield in polymer chemistry and production."
+      ],
+      "metadata": {
+        "id": "DLjeVs8UwY0y"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###3.1 Polymerization reaction in a batch reactor:"
+      ],
+      "metadata": {
+        "id": "sZnyscgkr5Bz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Note:\n",
+        "To solve this problem, it's important to have an understanding of the following code. For reviewing or learning the code and the method, you can use the links provided below to access the class website notes.\n",
+        "* [Visualization with matplotlib](https://ndcbe.github.io/data-and-computing/notebooks/01/Matplotlib.html?highlight=plot)\n",
+        "* [Systems of Differential Equations and Scipy](https://ndcbe.github.io/data-and-computing/notebooks/07/Systems-of-Differential-Equations-and-Scipy.html)\n",
+        "*[Residual Analysis](https://ndcbe.github.io/data-and-computing/notebooks/14/Residual-Analysis.html?highlight=residual%20analysis)\n",
+        "*[Model-Based Design of Experiments](https://ndcbe.github.io/data-and-computing/notebooks/16/Reaction-MBDoE.html?highlight=synthetic%20data)\n",
+        "* [Measurement Error](https://ndcbe.github.io/data-and-computing/notebooks/12/Measurement-Error.html?highlight=noise)\n",
+        "* [Uncertainty Analysis and Statistical Inference](https://ndcbe.github.io/data-and-computing/notebooks/14/Uncertainty-Analysis-and-Statistical-Inference.html?highlight=statistics%20resi)\n",
+        "*[Nonlinear Regression](https://ndcbe.github.io/data-and-computing/notebooks/15/Nonlinear-Regression.html?highlight=uncertainty%20analysis)\n",
+        "* [Uncertainty Analysis and Statistical Inference](https://ndcbe.github.io/data-and-computing/notebooks/14/Uncertainty-Analysis-and-Statistical-Inference.html?highlight=uncertainty%20analysis)"
+      ],
+      "metadata": {
+        "id": "bL-_qPOvn6Z9"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "Consider addition polymerization in a Batch reactor,\n",
+        "\n",
+        "$$ A+M → AM $$\n",
+        "\n",
+        "$$ AM + M → AM_2$$\n",
+        "\n",
+        "$$...$$\n",
+        "\n",
+        "$$AM_j + M → AM _{j+1} ,   r_p = k_p [M][AM_j ]$$\n",
+        "\n",
+        "$$...$$\n",
+        "\n",
+        "with [M]o constant at 1.0 mole/liter, [A]o = 0.01 moles/liter, and kp identical for all j. Add a termination reaction\n",
+        "\n",
+        "\n",
+        "$$AM_j → P_j , r_t = k_t [AM_j ]$$\n",
+        "\n",
+        "\n",
+        "where Pj is a dead or unreactive polymer.\n",
+        "\n",
+        "Plot ([AMj ] + [Pj ]) and j([AMj ] + [Pj ]) versus kpt for\n",
+        "$$ j < 14$$ for $$k_t /k_p = 1.0$$"
+      ],
+      "metadata": {
+        "id": "MLpjgoqcZdPl"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Class Activity** To tackle this problem, first we will make a function that includes all the polymerization steps (Initiation, Propagation, and termination). Code the function that includes all the ODE necessary to describe the system"
+      ],
+      "metadata": {
+        "id": "j2hrqHGIsvfw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def reaction_system(t, y, kp, kt):\n",
+        "\n",
+        "    '''\n",
+        "    Function to define the reaction system for polymerization\n",
+        "\n",
+        "    Arguments:\n",
+        "        kp: constant of propagatioin\n",
+        "        kt: constant of termination\n",
+        "        t: time step\n",
+        "        y: concentration of each species at one point in time\n",
+        "    Returns:\n",
+        "        A matrix (dydt) of all the reaction system\n",
+        "    '''\n",
+        "\n",
+        "    #Initializing the matrix, make an empty matrix for the function\n",
+        "    M, A = y[0], y[1]\n",
+        "    AMs = y[2:15]  # AM1 to AM13\n",
+        "    Ps = y[15:]    # P1 to P13\n",
+        "\n",
+        "    ### BEIGN SOLUTION ###\n",
+        "    dydt = [0] * len(y)\n",
+        "\n",
+        "    #Insert ODE here Differential equations\n",
+        "    dydt[0] = -kp * A * M  # dM_dt\n",
+        "    dydt[1] = -kp * A * M  # dA_dt\n",
+        "\n",
+        "    for i in range(13):\n",
+        "        if i == 0:\n",
+        "            dydt[i + 2] = kp * M * (A - AMs[i]) - kt * AMs[i]  # dAM_dt for the first AM\n",
+        "        else:\n",
+        "            dydt[i + 2] = kp * M * (AMs[i - 1] - AMs[i]) - kt * AMs[i]  # dAM_dt for subsequent AMs\n",
+        "\n",
+        "        dydt[i + 15] = kt * AMs[i]  # dP_dt for each P\n",
+        "\n",
+        "    ### END SOLUTION ###\n",
+        "    return dydt\n"
+      ],
+      "metadata": {
+        "id": "wnCOrl2NsvNI"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define the initial conditions for each of the species, this will be the initial guess for our IVP problem. Put this into a matrix form"
+      ],
+      "metadata": {
+        "id": "5vhds2fTtt7x"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Initial conditions:  Mo = 1, Ao = 0.01\n",
+        "initial_conditions = [1, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
+      ],
+      "metadata": {
+        "id": "YVLPxyoqt2M_"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We are looking at the reaction between time 0 and 40 with a 1 s time step."
+      ],
+      "metadata": {
+        "id": "PLmMLlYNt6Xi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Time span\n",
+        "t_span = (0, 40)"
+      ],
+      "metadata": {
+        "id": "ZOU64pHtucZb"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define the initial parameters"
+      ],
+      "metadata": {
+        "id": "uomDXQhFugP1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Parameters\n",
+        "kp = 1\n",
+        "kt = 1"
+      ],
+      "metadata": {
+        "id": "dBw-iI2JuYkk"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3.2 Solving ODE using scipy:"
+      ],
+      "metadata": {
+        "id": "lOo_v8MSefn0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To solve the problem above, we will use scipy solve_ivp with method 'RK45'."
+      ],
+      "metadata": {
+        "id": "O3NMzxmpuoA5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Solve the ODEs using solve_ivp\n",
+        "solution = solve_ivp(\n",
+        "    lambda t, y: reaction_system(t, y, kp, kt),\n",
+        "    t_span,\n",
+        "    initial_conditions,\n",
+        "    method='RK45'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "mbii2rNdt52j"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Extract the solutions from the data to get the form P + AMj"
+      ],
+      "metadata": {
+        "id": "PhOUuhbku6or"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "species_data = solution.y[2:15] + solution.y[15:]"
+      ],
+      "metadata": {
+        "id": "h5SYSPX4u-bg"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3.3 Graphing the results:"
+      ],
+      "metadata": {
+        "id": "Hqe3i_69eo89"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Graph the results from ODE equations"
+      ],
+      "metadata": {
+        "id": "fXt1tdhRer_W"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot species concentrations against kp*t\n",
+        "\n",
+        "### BEGIN SOLUTION ###\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "#plotting concentration AMj + Pj against time\n",
+        "for idx, species in enumerate(species_data):\n",
+        "    plt.plot(solution.t * kp, species, label=f\"P{idx + 1} + AM{idx + 1}\")\n",
+        "\n",
+        "plt.savefig('Concentration of Species against kp*t', dpi=300)\n",
+        "plt.legend(fontsize=10)\n",
+        "plt.xticks(fontsize=10)\n",
+        "plt.yticks(fontsize=10)\n",
+        "plt.grid(False)\n",
+        "plt.xlabel('kp*t')\n",
+        "plt.ylabel('Concentration (mol/liter)')\n",
+        "plt.title('Concentration of Species against kp*t')\n",
+        "plt.legend()\n",
+        "plt.show()\n",
+        "### END SOLUTION ###"
+      ],
+      "metadata": {
+        "id": "AYgeQEEsu3Om",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "outputId": "f5a4a326-d122-4ac4-c807-f84eaa169836"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###3.4 Analysis\n",
+        "What trends do you see from the graph?. Give 3 bullet points"
+      ],
+      "metadata": {
+        "id": "sEg80JHyvJpF"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Answer**\n",
+        "- The concentration of the species goes to a plateu after a while.\n",
+        "- The highest concentration is AM1 and P1\n",
+        "- It initially increase up until kp*t = 5.0 before it plateus"
+      ],
+      "metadata": {
+        "id": "9SEyY0gHD5TL"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Discussion 1: varying kt and kp"
+      ],
+      "metadata": {
+        "id": "jROiuU7UfDVf"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "What happens if we change kp to 0.5 and kt to 0 (no termination)? Copy the code from above and solve the solution problem and store in variable name: solution2"
+      ],
+      "metadata": {
+        "id": "1eNWrFJbveI9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Parameters\n",
+        "\n",
+        "### BEGIN SOLUTON ###\n",
+        "kp2 = 0.5\n",
+        "kt2 = 0\n",
+        "### END SOLUTION ###"
+      ],
+      "metadata": {
+        "id": "wPt-rQ3tvddR"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Solve the ODEs using the new parameters kp2 and kt2\n",
+        "\n",
+        "### BEGIN SOLUTION ###\n",
+        "solution2 = solve_ivp(\n",
+        "    lambda t, y: reaction_system(t, y, kp2, kt2),\n",
+        "    t_span,\n",
+        "    initial_conditions,\n",
+        "    method='RK45'\n",
+        ")\n",
+        "### END SOLUTION ###\n",
+        "\n",
+        "# Extract AM1 to AM13 and P1 to P13 data\n",
+        "species_data2 = solution2.y[2:15] + solution2.y[15:]\n",
+        "\n",
+        "# Plotting the results with new parameters\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for idx, species in enumerate(species_data2):\n",
+        "    plt.plot(solution2.t * kp2, species, label=f\"P{idx + 1} + AM{idx + 1}\")\n",
+        "\n",
+        "plt.xlabel('kp2*t')\n",
+        "plt.ylabel('Concentration (mol/liter)')\n",
+        "plt.title('Concentration of Species against kp2*t with New Parameters')\n",
+        "plt.legend()\n",
+        "plt.savefig('Concentration of Species against kp2*t with New Parameters', dpi=300)\n",
+        "plt.legend(fontsize=10)\n",
+        "plt.xticks(fontsize=10)\n",
+        "plt.yticks(fontsize=10)\n",
+        "plt.grid(False)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "y1y1L8WaFnwf",
+        "outputId": "e2c2fd11-7565-44c3-df9c-57eb3b48017f"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "What trend do you see from the graph? How does it compare to when kt is not = 0?"
+      ],
+      "metadata": {
+        "id": "6cFyg_8HHha_"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Answer**\n",
+        "- Instead of plateuing, the concentration of each species goes down after the maximum point\n",
+        "- The peak of each species goes down from AM1 to AM13; meaning that the Max concentration of each species lowers as the polymer chains becomes longer, which is expected"
+      ],
+      "metadata": {
+        "id": "-9VR1zpfH86Z"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##5. Discussion 2: Residual Analysis"
+      ],
+      "metadata": {
+        "id": "QhnMcUnnIVcq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###5.1 Making synthetic data\n",
+        "\n",
+        "To make a synthetic experimental data, we will introduce an error of the form\n",
+        "\n",
+        "$$\\text{noisy_data} = \\text{species_data} + \\mathcal{N}(0, \\sigma^2)$$\n",
+        "\n",
+        " This will be a \"noise\" that is introduced to the data; which will resemble the data that we would get when doing the experiments in lab. Code how you'll introduce the noise to the data. We will use normal distribution of random variables to introduce the errors"
+      ],
+      "metadata": {
+        "id": "_EZKUwXuIejy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Extract AM1 to AM13 data\n",
+        "am_data = solution.y[2:5]  + solution.y[15:18]\n",
+        "\n",
+        "### BEGIN SOLUTION ###\n",
+        "\n",
+        "# Generate noisy data\n",
+        "noise_scale = 0.0003  # Adjust the scale of the noise\n",
+        "noisy_data = am_data + np.random.normal(0, noise_scale, am_data.shape)\n",
+        "\n",
+        "### END SOLUTION ###"
+      ],
+      "metadata": {
+        "id": "_UTzevDgvs_t"
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Plot the noisy data up until AM3."
+      ],
+      "metadata": {
+        "id": "iUWtoEqQLszm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plotting the results with noise\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for idx, species in enumerate(noisy_data):\n",
+        "    plt.scatter(solution.t, species, label=f\"Noisy AM{idx + 1}\")\n",
+        "plt.xlabel('t')\n",
+        "plt.ylabel('Concentration (mol/l)')\n",
+        "plt.title('Synthetic data of AM1 to AM3 over Time')\n",
+        "plt.legend()\n",
+        "plt.savefig('Synthetic data of AM1 to AM3 over Time', dpi=300)\n",
+        "plt.legend(fontsize=10)\n",
+        "plt.xticks(fontsize=10)\n",
+        "plt.yticks(fontsize=10)\n",
+        "plt.grid(False)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "Jysw9z_0LsN6",
+        "outputId": "35b6d49e-8e91-4f2c-eede-591e0ea5e742"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###5.2 Residual plot"
+      ],
+      "metadata": {
+        "id": "X0rlOz4OLno0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Next, we will compare the synthetic experimental data against our numerical solution. We will use statistical analysis to do this\n",
+        "\n",
+        "Plot the residual of the noisy data and the numerical solution"
+      ],
+      "metadata": {
+        "id": "YCb5DdCSNAHs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "### BEGIN SOLUTION ###\n",
+        "residuals = am_data - noisy_data\n",
+        "### END SOLUTION ###\n",
+        "\n",
+        "# Plotting the residuals\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for idx, species in enumerate(residuals):\n",
+        "    plt.scatter(solution.t, species, label=f\"Residuals for AM{idx + 1}\")\n",
+        "\n",
+        "plt.xlabel('Time (t)')\n",
+        "plt.ylabel('Residuals (mol/l)')\n",
+        "plt.title('Residuals for AM1 to AM13 over Time')\n",
+        "plt.legend()\n",
+        "plt.savefig('Residuals for AM1 to AM13 over Time', dpi=300)\n",
+        "plt.legend(fontsize=10)\n",
+        "plt.xticks(fontsize=10)\n",
+        "plt.yticks(fontsize=10)\n",
+        "plt.grid(False)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "d0UJMJGkJuWg",
+        "outputId": "58224050-a422-449d-c541-c11a1b718a56"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###5.3 Statistics of the residual\n",
+        "Calculate the mean, variance, and the standard deviation of the residuals"
+      ],
+      "metadata": {
+        "id": "z8zwgljJOdjy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "### BEGIN SOLUTION ###\n",
+        "# Calculate mean, variance, and standard deviation for each AM\n",
+        "for idx, species_residual in enumerate(residuals):\n",
+        "    mean_residual = np.mean(species_residual)\n",
+        "    variance_residual = np.var(species_residual)\n",
+        "    std_dev_residual = np.std(species_residual)\n",
+        "\n",
+        "    print(f\"AM{idx + 1} Residuals - Mean: {mean_residual}, Variance: {variance_residual}, Standard Deviation: {std_dev_residual}\")\n",
+        "### END SOLUTION ###\n",
+        "# [Plotting code section]"
+      ],
+      "metadata": {
+        "id": "qWLVdkx1v55c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "87f898c6-9307-48ac-e1b9-d9fd8c356d77"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "AM1 Residuals - Mean: 6.328250532088572e-05, Variance: 1.0477972340038783e-07, Standard Deviation: 0.0003236969622971273\n",
+            "AM2 Residuals - Mean: -2.2542973936612587e-05, Variance: 7.410850084967498e-08, Standard Deviation: 0.00027222876565432055\n",
+            "AM3 Residuals - Mean: -1.6880629626695078e-05, Variance: 6.464325789427053e-08, Standard Deviation: 0.0002542503842558955\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Describe the differences between AM1,AM2, and AM3 residuals\n"
+      ],
+      "metadata": {
+        "id": "jZrM4A-zwEyp"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Answer**\n",
+        "\n",
+        "AM2 has the smallest mean,\n",
+        "AM3 has the smallest variance,\n",
+        "AM3 has the smallest STD"
+      ],
+      "metadata": {
+        "id": "Ub2ZAl0cSc-s"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##6. Discussion 3: Perform a non-linear Regression analysis"
+      ],
+      "metadata": {
+        "id": "fT-RZEZTOq5o"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We will use a non-linear regression to solve for kp and kt of the data based on AM1 - AM3 noisy data and the numerical solution model.\n",
+        "\n",
+        "We will use the equation\n",
+        "\n",
+        "$$\\min_{\\hat{\\theta}} \\quad \\sum (y_i - \\hat{y}_i)^2$$\n",
+        "\n",
+        "where\n",
+        "\n",
+        "$$\\theta = [k_{p}, k_m]$$"
+      ],
+      "metadata": {
+        "id": "1DA-54jWSzPx"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define a function that will call the numerical solution"
+      ],
+      "metadata": {
+        "id": "a6kLRaEPU-Dl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def model_func(theta, t_points):\n",
+        "    '''\n",
+        "    Function to define the reaction system for polymerization\n",
+        "\n",
+        "    Arguments:\n",
+        "        theta: kp and km\n",
+        "    Returns:\n",
+        "        qhat: the numerical solution\n",
+        "\n",
+        "    '''\n",
+        "\n",
+        "    ### BEGIN SOLUTION ###\n",
+        "\n",
+        "    #lumping kp and kt into theta\n",
+        "    kp, kt = theta\n",
+        "\n",
+        "    #defining the solution of kp and kt\n",
+        "    solution = solve_ivp(\n",
+        "        lambda t, y: reaction_system(t, y, kp, kt),\n",
+        "        t_span,\n",
+        "        initial_conditions,\n",
+        "        method='RK45',\n",
+        "        t_eval=t_points\n",
+        "    )\n",
+        "    qhat = solution.y[2:5, :] + solution.y[15:18, :]\n",
+        "\n",
+        "    ### END SOLUTION ###\n",
+        "\n",
+        "    return qhat.flatten()\n"
+      ],
+      "metadata": {
+        "id": "PX_SWOlZOv5C"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define a function that will return the residuals"
+      ],
+      "metadata": {
+        "id": "MfkdK2trVBAI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def regression_func(theta, t_points, noisy_data):\n",
+        "    '''\n",
+        "    Function to define the residuals\n",
+        "\n",
+        "    Arguments:\n",
+        "        theta: kp and km\n",
+        "        t_points: time points\n",
+        "        noisy_data: synthetic experimental data\n",
+        "    Returns:\n",
+        "        residuals\n",
+        "\n",
+        "    '''\n",
+        "    ### BEGIN SOLUTION ###\n",
+        "    qhat = model_func(theta, t_points)\n",
+        "    ### END SOLUTION ###\n",
+        "    return qhat - noisy_data.flatten()\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "-KW1WdUgPA7y"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Perform a non-linear regression using least squares method built-in scipy"
+      ],
+      "metadata": {
+        "id": "kPu3Jp9vViun"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Initial guess for kp and kt\n",
+        "theta_guess = ([0.5, 0.1])\n",
+        "\n",
+        "# Time points for the model to solve at (should match the time points of noisy_data)\n",
+        "t_points = np.linspace(t_span[0], t_span[1], len(noisy_data[0]))\n",
+        "\n",
+        "\n",
+        "### BEGIN SOLUTION ###\n",
+        "# Perform the least squares optimization\n",
+        "result = least_squares(\n",
+        "    fun=regression_func,\n",
+        "    x0=theta_guess,\n",
+        "    args=(t_points, noisy_data),\n",
+        "    method='lm'  # Levenberg-Marquardt algorithm, you can choose other methods as needed\n",
+        ")\n",
+        "### END SOLUTION ###\n",
+        "\n",
+        "# Extract optimized parameters\n",
+        "optimized_kp, optimized_kt = result.x\n",
+        "\n",
+        "# Print the results\n",
+        "print(f\"Optimized kp: {optimized_kp}, Optimized kt: {optimized_kt}\")\n",
+        "\n",
+        "print('kp/kt =', optimized_kp/optimized_kt)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ROLeXYTtPKaU",
+        "outputId": "88952941-7795-4db3-dff5-ada78af81f55"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Optimized kp: 0.2510900921603164, Optimized kt: 0.24160040311498165\n",
+            "kp/kt = 1.039278448723525\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Conclusion (Write one or 2 sentence)**\n",
+        "\n",
+        "Based on the result, the kp and kt ratio is the same compared to the numerical solution (kp/kt = 1). However, due to the noise, it cannot precisely get kp and kt; which is expected"
+      ],
+      "metadata": {
+        "id": "Roe9Cc3oWFA3"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Plot the predicted data against the noisy data"
+      ],
+      "metadata": {
+        "id": "D1JY898PbLZT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Generate model predictions using the optimized parameters\n",
+        "optimized_predictions = model_func([optimized_kp, optimized_kt], t_points).reshape(-1, len(t_points))\n",
+        "\n",
+        "# Plotting the results\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "# Plot noisy data\n",
+        "for idx, species in enumerate(noisy_data):\n",
+        "    plt.scatter(t_points, species, label=f\"Noisy AM{idx + 1}\", alpha=0.5)\n",
+        "\n",
+        "# Plot optimized model predictions\n",
+        "for idx, species in enumerate(optimized_predictions):\n",
+        "    plt.plot(t_points, species, label=f\"Optimized AM{idx + 1}\")\n",
+        "\n",
+        "plt.xlabel('Time (t)')\n",
+        "plt.ylabel('Concentration (mol/l)')\n",
+        "plt.title('Optimized Model vs Noisy Data')\n",
+        "plt.legend()\n",
+        "plt.savefig('Optimized Model vs Noisy Data', dpi=300)\n",
+        "plt.legend(fontsize=10)\n",
+        "plt.xticks(fontsize=10)\n",
+        "plt.yticks(fontsize=10)\n",
+        "plt.grid(False)\n",
+        "plt.show()\n",
+        "\n",
+        "# Print the ratio of optimized kp to kt\n",
+        "print(f\"kp/kt = {optimized_kp / optimized_kt}\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 582
+        },
+        "id": "SpludxzIPNo6",
+        "outputId": "2b83dd95-a3b2-4640-cb57-89eb8264ea1f"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "kp/kt = 1.039278448723525\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##7. Discussion 4: Uncertainty Analysis"
+      ],
+      "metadata": {
+        "id": "DflVwjXkWdqU"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Our goal is to get the covariance matrix. To achieve this, we will use\n",
+        "\n",
+        "$$\n",
+        "\\Sigma_{\\theta} \\approx \\hat{\\sigma}_e^2 (J^T J)^{-1}\n",
+        "$$\n",
+        "\n",
+        "where $J$ is the Jacobian of the residuals w.r.t. $\\theta$:\n",
+        "\n",
+        "$$\n",
+        "J_{i,j} = \\frac{\\partial(y_i - \\hat{y}_i)}{\\partial \\theta_j}\n",
+        "$$"
+      ],
+      "metadata": {
+        "id": "avKITevIbuix"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Print the jacobian matrix"
+      ],
+      "metadata": {
+        "id": "nNWKIokbb9Zi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#printing the jacobian matrix\n",
+        "J = result.jac\n",
+        "print('J= ',J)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GzyDits_b8Mk",
+        "outputId": "f5b96ee8-0604-4fb4-ed6d-a080dfd79107"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "J=  [[ 0.00000000e+00  0.00000000e+00]\n",
+            " [ 6.30880931e-03  1.10213758e-04]\n",
+            " [ 6.58007922e-03  6.29269267e-04]\n",
+            " [ 4.54771618e-03  1.53636467e-03]\n",
+            " [ 1.90845146e-03  2.66444683e-03]\n",
+            " [-6.35091992e-04  3.84344561e-03]\n",
+            " [-2.76518688e-03  4.98763922e-03]\n",
+            " [-4.51535723e-03  5.99873954e-03]\n",
+            " [-5.88607362e-03  6.87342000e-03]\n",
+            " [-6.86873628e-03  7.61934516e-03]\n",
+            " [-7.68980946e-03  8.20278473e-03]\n",
+            " [-8.27905898e-03  8.68312408e-03]\n",
+            " [-8.66643597e-03  9.07521381e-03]\n",
+            " [-9.01211802e-03  9.36445187e-03]\n",
+            " [-9.28002138e-03  9.58755191e-03]\n",
+            " [-9.42842647e-03  9.77361061e-03]\n",
+            " [-9.54685315e-03  9.91377165e-03]\n",
+            " [-9.66003697e-03  1.00147705e-02]\n",
+            " [-9.75128385e-03  1.00891159e-02]\n",
+            " [-9.80491093e-03  1.01481199e-02]\n",
+            " [-9.81830221e-03  1.01994427e-02]\n",
+            " [-9.85197085e-03  1.02332759e-02]\n",
+            " [-9.88175242e-03  1.02576198e-02]\n",
+            " [-9.90342372e-03  1.02765561e-02]\n",
+            " [-9.91142595e-03  1.02940720e-02]\n",
+            " [-9.91860557e-03  1.03060810e-02]\n",
+            " [-9.92711941e-03  1.03138108e-02]\n",
+            " [-9.93347319e-03  1.03196160e-02]\n",
+            " [-9.93679156e-03  1.03245709e-02]\n",
+            " [-9.93953892e-03  1.03283007e-02]\n",
+            " [-9.94204788e-03  1.03309452e-02]\n",
+            " [-9.94389008e-03  1.03329222e-02]\n",
+            " [-9.94494606e-03  1.03345222e-02]\n",
+            " [-9.94584962e-03  1.03357156e-02]\n",
+            " [-9.94656725e-03  1.03365985e-02]\n",
+            " [-9.94709584e-03  1.03372650e-02]\n",
+            " [ 0.00000000e+00  0.00000000e+00]\n",
+            " [ 1.94459910e-03 -9.45971646e-05]\n",
+            " [ 4.63390856e-03 -4.62622536e-04]\n",
+            " [ 6.22775699e-03 -9.61057003e-04]\n",
+            " [ 6.65833708e-03 -1.40288327e-03]\n",
+            " [ 6.31626082e-03 -1.67616382e-03]\n",
+            " [ 5.50475794e-03 -1.80047477e-03]\n",
+            " [ 4.59216596e-03 -1.75906745e-03]\n",
+            " [ 3.69803409e-03 -1.62389936e-03]\n",
+            " [ 2.85774236e-03 -1.43973476e-03]\n",
+            " [ 2.19064937e-03 -1.21773504e-03]\n",
+            " [ 1.63805758e-03 -1.00153941e-03]\n",
+            " [ 1.20000107e-03 -8.00076066e-04]\n",
+            " [ 8.64306362e-04 -6.20458832e-04]\n",
+            " [ 6.01110639e-04 -4.65707965e-04]\n",
+            " [ 4.08141313e-04 -3.33953775e-04]\n",
+            " [ 2.68905390e-04 -2.28422510e-04]\n",
+            " [ 1.63633387e-04 -1.43964631e-04]\n",
+            " [ 8.25157245e-05 -7.39425413e-05]\n",
+            " [ 1.77844399e-05 -1.71260288e-05]\n",
+            " [-3.54300344e-05  2.30916662e-05]\n",
+            " [-6.57963088e-05  5.51661822e-05]\n",
+            " [-7.83307920e-05  8.30865545e-05]\n",
+            " [-8.87800516e-05  1.04012659e-04]\n",
+            " [-1.14592741e-04  1.13016107e-04]\n",
+            " [-1.30678794e-04  1.22180684e-04]\n",
+            " [-1.33856248e-04  1.32932442e-04]\n",
+            " [-1.35866890e-04  1.41127021e-04]\n",
+            " [-1.42943148e-04  1.45038651e-04]\n",
+            " [-1.47166995e-04  1.48354065e-04]\n",
+            " [-1.48246670e-04  1.51670315e-04]\n",
+            " [-1.49060900e-04  1.54208739e-04]\n",
+            " [-1.51165048e-04  1.55590432e-04]\n",
+            " [-1.52192602e-04  1.56781923e-04]\n",
+            " [-1.52714218e-04  1.57774532e-04]\n",
+            " [-1.53121674e-04  1.58520801e-04]\n",
+            " [ 0.00000000e+00  0.00000000e+00]\n",
+            " [ 2.76243780e-04 -1.45962701e-05]\n",
+            " [ 1.32837552e-03 -1.40638813e-04]\n",
+            " [ 2.71994178e-03 -4.40426541e-04]\n",
+            " [ 3.92930577e-03 -8.79060799e-04]\n",
+            " [ 4.72401678e-03 -1.37914334e-03]\n",
+            " [ 5.10667669e-03 -1.84723732e-03]\n",
+            " [ 5.14681832e-03 -2.24379639e-03]\n",
+            " [ 4.97128005e-03 -2.54143433e-03]\n",
+            " [ 4.65539249e-03 -2.74760351e-03]\n",
+            " [ 4.32530772e-03 -2.86161731e-03]\n",
+            " [ 3.98837371e-03 -2.91799625e-03]\n",
+            " [ 3.66362936e-03 -2.92993129e-03]\n",
+            " [ 3.39533572e-03 -2.90746821e-03]\n",
+            " [ 3.17417372e-03 -2.87060793e-03]\n",
+            " [ 2.99613452e-03 -2.82612998e-03]\n",
+            " [ 2.87482732e-03 -2.77128327e-03]\n",
+            " [ 2.74657985e-03 -2.72919810e-03]\n",
+            " [ 2.63398425e-03 -2.69636081e-03]\n",
+            " [ 2.57207387e-03 -2.66298072e-03]\n",
+            " [ 2.57828595e-03 -2.62026876e-03]\n",
+            " [ 2.53854765e-03 -2.59624947e-03]\n",
+            " [ 2.48157468e-03 -2.58457720e-03]\n",
+            " [ 2.44065491e-03 -2.57290457e-03]\n",
+            " [ 2.45494596e-03 -2.54564751e-03]\n",
+            " [ 2.45944482e-03 -2.53015887e-03]\n",
+            " [ 2.43830220e-03 -2.52867477e-03]\n",
+            " [ 2.42130723e-03 -2.52783659e-03]\n",
+            " [ 2.42620328e-03 -2.51994951e-03]\n",
+            " [ 2.42642290e-03 -2.51481062e-03]\n",
+            " [ 2.41949127e-03 -2.51363783e-03]\n",
+            " [ 2.41439745e-03 -2.51295893e-03]\n",
+            " [ 2.41614870e-03 -2.51046428e-03]\n",
+            " [ 2.41534458e-03 -2.50903536e-03]\n",
+            " [ 2.41381310e-03 -2.50831173e-03]\n",
+            " [ 2.41274616e-03 -2.50775520e-03]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Calculate the residual between the linear regression model and the noisy data and the variance"
+      ],
+      "metadata": {
+        "id": "RsG5mPpNcgnO"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "### BEGIN SOLUTION ###\n",
+        "\n",
+        "r3= noisy_data.flatten() - model_func([optimized_kp, optimized_kt], t_points)\n",
+        "var_r3 = np.var(r3)\n",
+        "\n",
+        "print('Variance of r = ',var_r3, 'mol/L')\n",
+        "\n",
+        "### END SOLUTION ###"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "H2BZ5RDTRL_f",
+        "outputId": "2705ab75-29e4-4dca-be91-d88959fe84b0"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Variance of r =  1.0611545634820738e-07 mol/L\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Find the covariance matrix"
+      ],
+      "metadata": {
+        "id": "JYor-0uedKHV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cov_matrix = var_r3 * np.linalg.inv(np.dot(J.T, J))\n",
+        "\n",
+        "print(\"Covariance Matrix:\\n\", cov_matrix)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Be-j1OTkcvNM",
+        "outputId": "b5234c4b-4331-437d-8bec-6c5a8c3d47de"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Covariance Matrix:\n",
+            " [[0.00029574 0.00028716]\n",
+            " [0.00028716 0.00031395]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Conclusion**\n",
+        "\n",
+        "The code exemplifies Python's versatility by simulating addition polymerization with ODEs, visualizing species concentrations over time, and elucidating reaction kinetics dynamics. Leveraging NumPy, SciPy, and Matplotlib, it facilitates precise parameter estimation for kp and kt, revealing uncertainties and parameter interdependencies through the covariance matrix. This comprehensive approach emphasizes the need for cautious interpretation, highlighting correlations between parameters for informed chemical system analyses and predictions. The off-diagonals are non-zero, therefore there must be a variation between both of the parameters"
+      ],
+      "metadata": {
+        "id": "C1-Rc9QaeI7H"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "oiyhmqUWBTEy"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file