Contributors:Laura Marcano, Anis Yazidi, Davide Manca, Tiina M Komulainen
The data was generated randomly, based on different combinations of possible actions in the dynamic simulator K-Spice from Kongsberg Digital. In case study 1, the aim was to increase +10 % of the oil production with respect to the initial condition value. In case study 2, the aim was to decrease -10 % of the gas production with respect to the initial condition value. The data show examples of possible correct and incorrect paths that a trainee could follow trying to solve the scenarios.
1. Description of supplementary material
2. Excel files containing mGBD algorithm (zipped)
3. Aspen Plus Version 8.8 files (zipped)
Contributors:Stephen Goldrick, Karolis Jankauskas, Barry Lennox, Suzanne Farid, David Lovett, Carlos A. Duran-Villalobos
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: www.industrialpenicillinsimulation.com. 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.
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., (2018) Modern day control challenges for industrial-scale fermentation processes. Computers and Chemical Engineering.
Additionally help publicise this work on the following platforms:
LinkenIn: Stephen Goldrick - Post Doc @UCL Biochemical Socitey
Contributors:Rodolfo Romanach, Kim Esbensen, Nobel O. Sierra-Vega, Rafael Mendez, Vanessa Cardenas Espitia, Adriluz Sanchez Paternina
Contributors:Nishanth Chemmangattuvalappil, Suchithra Thangalazhy-Gopakumar, Jie Qi Neoh, Hon Huin Chin, Omar Anas Aboagwa, Angel Xin Yee Mah
Phase stability analysis, matlab codes and lingo code