Data for: Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process

Published: 8 Oct 2019 | Version 2 | DOI: 10.17632/pdnjz7zz5x.2

Description of this data

This data was generated using an advanced mathematical simulation of a 100,000 litre penicillin fermentation system referenced as IndPenSim. All details describing the simulation are available on the following website: IndPenSim is the first simulation to include a realistic simulated Raman spectroscopy device for the purpose of developing, evaluating and implementation of advanced and innovative control solutions applicable to biotechnology facilities. This data set generated by IndPenSim represents the biggest data set available for advanced data analytics and contains 100 batches with all available process and Raman spectroscopy measurements (~2.5 GB). This data is highly suitable for the development of big data analytics, machine learning (ML) or artificial intelligence (AI) algorithms applicable to the biopharmaceutical industry. The 100 batches are controlled using different control strategies and different batch lengths representing a typical Biopharmaceutical manufacturing facility:

Batches 1-30: Controlled by recipe driven approach
Batches 31-60: Controlled by operators
Batches 61:90: Controlled by an Advanced Process Control (APC) solution using the Raman spectroscopy
Batches 91:100: Contain faults resulting in process deviations.

A Jupyter-notebook can be found here that downloads and plots the data:

Batch reference of data is found under: 2-PAT control(PAT_ref:PAT ref)

Please reference:

Goldrick S., Stefan, A., Lovett D., Montague G., Lennox B. (2015) The development of an industrial-scale fed-batch fermentation simulation Journal of Biotechnology, 193:70-82.
Goldrick S., Duran-Villalobos C., K. Jankauskas, Lovett D., Farid S. S, Lennox B., (2019) Modern day control challenges for industrial-scale fermentation processes. Computers and Chemical Engineering.

Additionally help publicise this work on the following platforms:
Twitter: @Stephen_Goldric
Github: StephenGoldie
LinkenIn: Stephen Goldrick - Post Doc @UCL Biochemical Socitey

Experiment data files

Steps to reproduce

Further details can be found at

Related links

Latest version

  • Version 2


    Published: 2019-10-08

    DOI: 10.17632/pdnjz7zz5x.2

    Cite this dataset

    Goldrick, Stephen (2019), “Data for: Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process”, Mendeley Data, v2


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The University of Manchester, University College London


Statistics, Machine Learning, Biopharmaceuticals, Big Data


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