This document outlines a course on process modeling and simulation taught by Madhusree Kundu at the National Institute of Technology in Rourkela, India. The 40-lecture course covers first principle modeling, process identification using MATLAB, machine learning algorithms, and exploring MATLAB toolboxes. Key topics include developing mathematical models using conservation principles, dynamic simulation, time series analysis, system identification, regression, classification, and dimensionality reduction. The course aims to equip students to model and simulate chemical processes, identify plant models, and apply machine learning and artificial intelligence techniques.

1. Process Modelling And Simulation
(CH-4311)
Total Number of Lectures: 40
Madhusree Kundu
Department of Chemical Engineering
National Institute of Technology
Rourkela, Orissa
India
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2. • Instructional Objectives
1. The students will be able to Develop mathematical models for various
chemical processes with the help of first principles.
Find the numerical solution of the proposed model using available
numerical methods and MATLAB as simulation Platform.
2. The students will be able to Identification of plant data (time series
signals) using MATLAB System identification toolbox.
3. The students will be able to Learn Machine learning algorithms and
its superset Artificial Intelligence (AI)
4. The students will be able to explore and utilize MATLAB BASED
TOOLBOX: System identification toolbox, Differential equation solver,
and machine-learning toolbox
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On Completion of the Course

3. Course Modules
1. First principle Modeling and Simulation
2. Identification of approximate/lower order models
3. Machine Learning
4. Exploration of MATLAB BASED TOOLBOX
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4. Module Instructional Objectives
Process Modeling and Dynamic Simulation
1. The students will be able to develop mathematical models of
processes related to mass, heat and fluid transfer, separation and
reaction using conservation principles.
2. The students will be able to analyze the dynamics of the aforesaid
processes performing dynamic simulation using the
MATLAB/SIMULINK environment which would help them to monitor
an ongoing process and design appropriate controllers for that
process to ensure product quality and process safety
Process Identification
The students will be able to develop input-output models like ARX, AR,
ARMA, NRMA etc. using plant/experimental time series data, which might
help them in successful monitoring and control of a specific process.
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5. Module Instructional Objectives
Machine Learning
The students will be able to utilize various machine learning
algorithms including AI in solving regression, classification, parameter
estimation problems of data based models, in developing process
monitoring tools and controlling of a process
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System identification toolbox, Differential equation solver, and
machine-learning toolbox.
Exploration of MATLAB BASED TOOLBOX

6. Units
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7. Process Modeling and Dynamic Simulation
Unit 1: Introduction to mathematical modeling and dynamic simulation
Conservation principles, Definition of mathematical model and different types
of models and necessity of them, transfer function models, time-domain-
Laplace domain-Z-domain transformation, Dynamic simulation and solvers .
No of Lecture: 1
Unit 2: Development of mathematical models using conservation principles
I. Distillation columns,
II. Isothermal and non- isothermal reactors
III. Heat exchangers
No of Lecture: 6
Unit 3: Linearization and state space models
No of Lecture: 3
Unit 4: Dynamic simulation using MATLAB ODE solver
All the unit 2 models and analysis of their response.
No of Lecture: 2
Total number of lectures: 12
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8. Process Identification
Unit 1: Introduction to time series data
Time series data and their attributes, auto correlation, partial correlation, cross
correlation, Stationary and non-stationary data, sampling continuous signal,
reconstruction Of continuous signals from their discrete time values, conversion of
continuous to discrete Time models
No of Lecture: 5
Unit 2: Time series models
AR, ARMA, ARMAX, NRMA, NRMAX, output error models for correlating time
series data
No of Lecture:3
Unit 3: System identification tool-box in MATLAB
Use of system identification tool-box for correlating time series data
No of Lecture: 2
Total number of lectures: 10
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9. 10/19/2021 9
Machine Learning
Unit 3: Support vector machines (SVM) Algorithms ,and AI architectures
No of Lecture: 3
Unit 1: Introduction to Machine Learning, Chemmometrics, and Artificial
Intelligence (AI). supervised learning, unsupervised learning, and reinforcement
learning and its application for Chemical Proses Industries in Specific, Data
acquisition and preprocessing, attributes of data
No of Lecture:4
Unit 2: : Dimensionality Reduction and Regression and classification
Algorithms, k- Nearest Neighbors (kNN), K-Means Algorithms
No of Lecture: 3
Total number of lectures: 10

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Exploration of MATLAB BASED TOOLBOX
Unit 1: System identification toolbox
2 Classes
Unit 2: Differential equation solver
2 Classes
Unit 3: Machine Learning toolbox
4 Classes
Total number of lectures: 8

11. Essential Reading:
B. Roffel, B. Betlem, “Process Dynamics & Control: Modeling for control and
prediction. John Wiley & Sons Ltd., 2006
William L. Luyben, “Process modeling, simulation, and control for chemical
engineers. ” Second Edition, McGraw-Hill, 1996
Supplementary Reading:
B. Wayne Bequette, “Process Control: Modeling, Design, and Simulation
(International Series in the Physical and Chemical Engineering Sciences) 2nd Edition,
2021.
Madhusree Kundu, Palash Kundu, Seshu Kumar Damarla (2017). A Chemometric
Approach to Monitoring: Product Quality Assessment, Process Fault Detection and
Miscellaneous Applications, 1st Edition, CRC Press, Taylor & Francis Group
(Published 3 October, 2017).
Books
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12. Process Modelling and
Simulation
Madhusree Kundu

13. CSTR Modeling
Continuously Stirred Tank Reactor (CSTR)
A reactor is used to convert a hazardous
chemical A to an acceptable chemical B in waste
stream before entering a nearby lake. This
particular reactor is dynamically modeled as a
Continuously Stirred Tank Reactor (CSTR) with a
simplified kinetic mechanism that describes the
conversion of reactant A to product B with an
irreversible and exothermic reaction. It is
desired to maintain the temperature at a
constant setpoint that maximizes the
destruction of A (highest possible temperature).