Course Details

Subject {L-T-P / C} : FP4213 : Food Process Modeling and Simulation { 3-0-0 / 3}

Subject Nature : Theory

Coordinator : Dr. Sushil Kumar Singh

Syllabus

Module 1: Fundamentals of Linear Regression
• Introduction to regression analysis
• Simple linear regression and its assumptions
• Estimation of regression coefficients
• Hypothesis testing in linear regression.
• Model evaluation metrics: R-squared, adjusted R-squared, Mean Squared Error (MSE)
• Introduction to multiple linear regression

Module 2: Introduction to Food Process Modelling
• Overview of food process modelling and its applications
• Types of models and their use in food processing

Module 3: Empirical Model Development
• Factorial, fractional factorial and rotatable central composite experimental design
• Developing empirical equations using experimental data.

Module 4: Artificial Neural Networks
• Overview of the biological basis for ANNs
• Neurons, activation functions, and the sigmoid function
• The backpropagation algorithm for training ANNs
• Feedforward Neural Networks
• Developing predictive model using Neural network

Module 5: Optimization using Genetic Algorithm
• Overview of the biological basis for genetic algorithms
• Genetic operators, such as selection, crossover, and mutation
• Fitness functions and their role in optimization
• Optimization of processing parameters using Genetic algorithms

Module 6: Sensory Evaluation using Fuzzy Logic
• Overview of sensory evaluation and its importance in the food and beverage industry
• Definition and purpose of fuzzy logic
• Application of Fuzzy logic to sensory evaluation and ranking of foods.

Module 7: Simulation of Food Processes
• Introduction to simulation of food processes
• Tools and software for simulation in food processing
• Applications of simulation in food processing

Course Objectives

Course Outcomes

On Completion of the course student will be able to: <br />1. Understand the fundamental principles of food process modelling. <br /> <br />2. Know the different types of food process models, including empirical models, analytical models, and numerical models, ANN. <br /> <br />3. Develop and validate food process models using experimental data and statistical techniques, such as regression analysis and optimization methods. <br /> <br />4. Apply mathematical models to describe and predict the behavior of food systems during processing operations, such as drying, heating, cooling, and preservation. <br /> <br />5. Know the different types of simulation software and their applications in the food industry, including computational fluid dynamics (CFD) software, such as ANSYS Fluent, POLYFLOW etc. <br /> <br />6. Know the applications of process modeling and simulation in various food processing operations, such as baking, canning, and refrigeration. <br /> <br />7. Understand the limitations and uncertainties associated with food process modeling and simulation, and how to account for these in the analysis and interpretation of results.

Essential Reading

Supplementary Reading