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%)
Prerequisites Project Appraisal for Bioprocesses (BENG 3006), Computer Aided Bioprocess Engineering (BENG 3009), Fermentation and Bioreactor Engineering (BENG 2010), Bioprocess Recovery and Purification (BENG 2011) or courses with similar content.


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.