Biochemical Engineering


Bioprocess Systems Engineering

Course Code
Level MSc
Credits 15 credits
Module Tutor
Dr Sofia Simaria
Three-hour written examination (65%)
Three case study reports (35%)


This course is designed to provide students with skills in advanced modelling, optimisation and statistical techniques such that they are adequately equipped to address problems related to evaluating the cost-effectiveness and robustness of alternative bioprocess design strategies.

Learning Outcomes

Following completion of the course, students will have an understanding of:

  • use a set of software tools for discrete-event simulation, Monte Carlo simulation
  • linear and mixed-integer programming and genetic algorithms
  • implement automation in Excel by programming VBA macros
  • formulate decision problems related with bioprocessing design in a structured way and select appropriate methods to solve them
  • build simulation models, optimise key decision variables and critically analyse output results
  • conduct advanced research in Bioprocess Systems Engineering
  • take the acquired expertise into industry to work as developers of simulation/optimisation/process economics models in real biomanufacturing companies.

Learning Hours


Lectures: 40h
Computer lab sessions: 20h


Discrete-event simulation Introduction to queuing systems; Basic concepts of discrete-event simulation; Use of simulators.
Uncertainty analysis Identification of sources of uncertainty and variability in bioprocessing; Monte Carlo simulation to address uncertainty
Multi-criteria decision-making Basic concepts; Weighted sum method; Generation of non-dominated solutions/Pareto front
Optimisation: mathematical programming Linear programming (LP) formulation; Graphical, simplex method and sensitivity analysis; Mixed integer linear programming (MILP) formulation and branch-and-bound method.
Optimisation: meta-heuristic approaches Combinatorial optimisation: typical problems and bioprocess-related examples; Constructive heuristics, local search and local optimality; Meta-heuristics: overview of most popular methods; Application of genetic algorithms to bioprocessing problems
Research showcase Presentation of current (or past) research within the Decisional Tools group directly linked with the subjects taught.