Syllabus

Module 1: Fundamentals of Bioprocess Optimization
Introduction to Bioprocess Systems and Optimization Needs Optimization Objectives: Yield, Productivity, Cost, and Environmental Impact Optimization Techniques: Linear Programming (LP) and Non-Linear Programming (NLP), Gradient-Based Optimization Methods, Analytical vs Numerical Optimization Approaches
Module 2: Bioprocess Modeling and Simulation
Developing Mathematical Models for Bioprocesses, Simulating Bioprocess Dynamics for Batch, Fed-Batch, and Continuous Systems, Optimization Techniques: Sensitivity Analysis for Model Refinement, Computational Optimization Using Software Tools (e.g., MATLAB, Aspen Plus), Metaheuristic Methods: Genetic Algorithms (GA) and Simulated Annealing (SA)
Module 3: Kinetics and Bioreactor Parameter Optimization
Microbial Growth and Product Formation Kinetics Bioreactor Design and Operation: Mixing, Aeration, and Heat Transfer Optimization Techniques: Response Surface Methodology (RSM), Multi-Objective Optimization (MOO) for Balancing Trade-Offs, Artificial Neural Networks (ANN) for Predictive Optimization
Module 4: Advanced Process Control and Real-Time Optimization
Process Monitoring and Adaptive Control Strategies Advanced Sensors and Data Integration in Bioprocessing Optimization Techniques: Model Predictive Control (MPC) for Dynamic Optimization, Machine Learning Algorithms for Process Control, Online Optimization Using Digital Twins
Module 5: Sustainability and Emerging Techniques in Bioprocess Optimization
Green Bioprocessing and Circular Economy Approaches, Life Cycle Assessment (LCA) for Sustainable Bioprocess Development, Optimization Techniques: Multi-Criteria Decision Analysis (MCDA) for Sustainability Metrics, Big Data Analytics for Process Optimization, Advanced Evolutionary Algorithms for Complex Bioprocesses

Understand Optimization Principles in Bioprocessing

Apply Advanced Mathematical and Computational Techniques

Integrate Bioreactor and Downstream Process Optimization

Adopt Emerging Technologies and Sustainable Practices

CO1. Analyze and Optimize Bioprocess Parameters
CO2. Model and Simulate Bioprocess Systems
CO3. Implement Advanced Control and Monitoring Techniques
CO4. Optimize Bioreactor and Downstream Operations
CO5. Incorporate Emerging Technologies and Sustainable Solutions

R.H. Myers, D.C. Montgomery, C.M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Wiley , 2015

T.T. Panda, R. Theodore, A. Kumar, Statistical Optimization of Biological Systems, CRC Press , 2015

D.C. Montgomery, Design and Analysis of Experiments, Wiley , 4th Edition

P. M. Doran, Bioprocess Engineering principles, Academic Press , 2002

Bioprocess and Biosystems Engineering, Springer Verlag, Germany.

AMI annual International Conference