Course Details
Subject {L-T-P / C} : FP6234 : Food Process Modelling and Simulation { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Dr. Sushil Kumar Singh
Syllabus
Module 1: Introduction to Statistical Analysis in Food Processing
• Definition of statistical analysis and its importance in the food processing industry
• Understanding the various types of statistical methods used in food processing analysis.
• Overview of hypothesis testing and its use in food analysis
• Steps involved in conducting hypothesis tests and interpreting the results.
Module 2: 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 3: Introduction to Food Process Modelling
• Overview of food process modelling and its applications
• Types of models and their use in food processing
Module 4: Empirical Model Development
• Factorial, fractional factorial and rotatable central composite experimental design
• Developing empirical equations using experimental data.
Module 5: 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 6: 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 7: Simulation of Food Processes
• Introduction to simulation of food processes
• Tools and software for simulation in food processing
• Applications
Course Objectives
- To introduce students to the principles and applications of statistical methods in the food processing industry fundamental principles of food process modelling and simulation.
- To provide students with a comprehensive understanding of the different types of food process models the role of food process modeling and simulation in the food industry and its potential for improving food quality and safety, reducing costs and waste, and promoting sustainability.
- To teach students how to develop and validate food process models using experimental data and statistical techniques, such as regression analysis and optimization methods.
- To explore the applications of food process modeling and simulation in various food processing operations, such as baking, canning, and refrigeration.
Course Outcomes
On Completion of the course student will be able to: <br />1. Understand the statistical techniques and their applications in food processing, including ANOVA, and hypothesis testing. <br /> <br />2. Understand the fundamental principles of food process modelling. <br /> <br />3. Know the different types of food process models, including empirical models, analytical models, and numerical models, ANN. <br /> <br />4. Develop and validate food process models using experimental data and statistical techniques, such as regression analysis and optimization methods. <br /> <br />5. Apply mathematical models to describe and predict the behavior of food systems during processing operations, such as drying, heating, cooling, and preservation. <br /> <br />6. 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 />7. Know the applications of process modeling and simulation in various food processing operations, such as baking, canning, and refrigeration. <br /> <br />8. 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
- Freund and W.J. Wilson, Statistical Methods, Academic Press , Second Edition
- H. Das, Food Processing Operations Analysis, Asian Books Private Limited
Supplementary Reading
- Serafim Bakalis, Kai Knoerzer and Peter J. Frye, Modeling Food Processing Operations, Woodhead Publishing
- Soojin Jun, Joseph M. Irudayaraj, Food Processing Operations Modeling:Design and Analysis, Routledge Taylor &Francis Group , Second Edition