Industrial-scale penicillin simulation (IndPenSim) is a first principles mathematical model of a Penicillium chrysogenum fermentation. The simulation was developed in Matlab and is freely available to download. The development of the simulation is discussed in the paper titled "The development of an industrial-scale fed-batch fermentation simulation" currently available to download: here (Journal of Biotechnology). The paper has recently been extended which include the addition of a simulated Raman spectroscopy device for the purpose of developing, evaluating and implementation of advanced and innovative control solutions applicable to biotechnology facilities. The capabilities of IndPenSim are demonstrated through the implementation of a QbD methodology utilising the three stages of the PAT framework. Additionally, IndPenSim evaluated a fault detection algorithm to detect process faults occurring on different batches recorded throughout a yearly campaign. Details of this work can be found in the paper titled: 'Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process' which can be found here: published by the Computers and Chemical Engineering Journal :

Schematic of the 100,000 L bioreactor used by the simulation »

Industrial-Scale Penicillin Simulation: IndPenSim

  • The simulation was developed using a mechanistic model and validated using historical data collected from an industrial-scale penicillin fermentation process (the batch data is available for download). Each batch was carried out in a 100,000 litre bioreactor using an industrial strain of Penicillium chrysogenum, a schematic of the bioreactor is given above.
  • A summary of the main inputs and outputs from the simulation is displayed below. All the variables highlighted in blue represent the variables that were recorded in the batch records and available for this study. The variables in black are those that were approximated by the simulation.
  • Inputs_outputs

Project in collaboration with..

Project supported by the Engineering Doctorate programme at the Biopharmaceutical Bioprocessing Technology Centre at Newcastle University, in part funded by Perceptive Engineering Limited and affiliated with the University of Manchester and University College London.