CH4002D COMPUTER APPLICATIONS IN CHEMICAL ENGINEERING
Course Outcomes:
CO1: List, Identify and differentiate traditional computational methods in chemical engineering
CO2: Develop skills to solve chemical engineering problems by traditional methods
CO3: Illustrate artificial intelligence (AI) methodologies and learning rules
CO4: Develop working knowledge of Artificial Neural Networks and their applications in chemical engineering
CO5: Develop MATLAB programming skills to solve traditional and non-traditional computational methods
Module 1: (16 hours)
Introduction to process modeling and simulation, limitations and usefulness of process modeling and simulation, model building steps, classification of process models- steady and unsteady state models; lumped parameter versus distributed parameter models; continuous versus discrete models; linear versus non-linear models; deterministic versus stochastic models; mechanistic versus empirical models, development of models for various chemical engineering process units, computational methods for the solution of process models, simulation of the process models using MATLAB and interpretation of results, introduction to process engineering packages (Aspen Plus, Aspen Hysys).
Module 2: (12 hours)
Introduction to knowledge based applications, applications of artificial intelligence (AI) in chemical engineering, AI principles, introduction to AI programming, introduction to prolog and programming in prolog, expert system design and development- expert system for separation process synthesis, applications in various chemical processes.
Module 3: (11 hours)
Introduction to artificial neural networks (ANNs), fundamentals of ANNs, setting of weights, activation function, bias, threshold, learning rules, perceptron networks, feedforward networks, back propagation strategy, training algorithms, application of ANNs in chemical engineering- process modeling; process control; fault diagnosis and process forecasting, ANN MATLAB toolbox, limitations of ANNs, problem solving using MATLAB neural network toolbox.
References:
W. L. Luyben, Process Modeling, Simulation and Control for Chemical Engineers, 2nd ed., New York: McGraw-Hill Publisbing Company, 1999.
S.C. Chapra and R.P. Canale, Numerical Methods for Engineers, 7th ed., New York: McGraw-Hill Education, 2015.
T.E Quantrille and YA Liu, Artificial Intelligence in Chemical Engineering, Sand Diego: Academic Press, 1991.
Angelo Basile, Stefano Curcio, Artificial Neural Networks in Chemical Engineering, Nova Science Publishers, 2017.