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

被引:33
作者
Goldrick, Stephen [1 ]
Duran-Villalobos, Carlos A. [2 ]
Jankauskas, Karolis [1 ]
Lovett, David [3 ]
Farid, Suzanne S. [1 ]
Lennox, Barry [2 ]
机构
[1] UCL, Adv Ctr Biochem Engn, Dept Biochem Engn, Gordon St, London WC1H 0AH, England
[2] Univ Manchester, Sch Elect & Elect Engn, Manchester, Lancs, England
[3] Percept Engn Ltd, Daresbury, Cheshire, England
基金
英国工程与自然科学研究理事会;
关键词
Modelling; Control; Process analytic technology (PAT); Quality by Design (QbD); Biopharmaceutical; Raman spectroscopy; Fault detection; RAMAN; PENICILLIN; BIOREACTOR;
D O I
10.1016/j.compchemeng.2019.05.037
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper outlines real-world control challenges faced by modern-day biopharmaceutical facilities through the extension of a previously developed industrial-scale penicillin fermentation simulation (IndPenSim). The extensions 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. IndPenSim can be operated in fixed or operator controlled mode and generates all the available on-line, off-line and Raman spectra for each batch. The capabilities of IndPenSim were initially demonstrated through the implementation of a QbD methodology utilising the three stages of the PAT framework. Furthermore, IndPenSim evaluated a fault detection algorithm to detect process faults occurring on different batches recorded throughout a yearly campaign. The simulator and all data presented here are available to download at www.industrialpenicillinsimulation.com and acts as a benchmark for researchers to analyse, improve and optimise the current control strategy implemented on this facility. Additionally, a highly valuable data resource containing 100 batches with all available process and Raman spectroscopy measurements is freely available to download. 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. (C) 2019 Published by Elsevier Ltd.
引用
收藏
页数:14
相关论文
共 42 条
[1]   A Plant-Wide Dynamic Model of a Continuous Pharmaceutical Process [J].
Benyahia, Brahim ;
Lakerveld, Richard ;
Barton, Paul I. .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2012, 51 (47) :15393-15412
[2]   A modular simulation package for fed-batch fermentation:: penicillin production [J].
Birol, G ;
Ündey, C ;
Çinar, A .
COMPUTERS & CHEMICAL ENGINEERING, 2002, 26 (11) :1553-1565
[3]   How to pre-process Raman spectra for reliable and stable models? [J].
Bocklitz, Thomas ;
Walter, Angela ;
Hartmann, Katharina ;
Roesch, Petra ;
Popp, Juergen .
ANALYTICA CHIMICA ACTA, 2011, 704 (1-2) :47-56
[4]   Industrial experiences with multivariate statistical analysis of batch process data [J].
Chiang, LH ;
Leardi, R ;
Pell, RJ ;
Seasholtz, MB .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2006, 81 (02) :109-119
[5]   Rapid monitoring of antibiotics using Raman and surface enhanced Raman spectroscopy [J].
Clarke, SJ ;
Littleford, RE ;
Smith, WE ;
Goodacre, R .
ANALYST, 2005, 130 (07) :1019-1026
[6]   A PLANT-WIDE INDUSTRIAL-PROCESS CONTROL PROBLEM [J].
DOWNS, JJ ;
VOGEL, EF .
COMPUTERS & CHEMICAL ENGINEERING, 1993, 17 (03) :245-255
[7]  
Ethiopian Food and Drug Administration (EFDA), 2017, GUID IND PAT FRAM IN
[8]  
Food and Drug Administration, 2009, GUID IND Q82 PHARM D
[9]   A model-based systems approach to pharmaceutical product-process design and analysis [J].
Gernaey, Krist V. ;
Gani, Rafiqul .
CHEMICAL ENGINEERING SCIENCE, 2010, 65 (21) :5757-5769
[10]  
Goldrick S., 2014, IFAC P VOLUMES, V47, P6222, DOI [10.3182/20140824-6-ZA-1003.02589, DOI 10.3182/20140824-6-ZA-1003.02589]