A quantitative risk analysis approach to a process sequence under uncertainty – A case study. (12th July 2019)

• Record Type: Journal Article
• Authors: Johnson, David B.; Bogle, I. David L.
• Is Part Of: Computers & chemical engineering. Volume 126(2019)
• Page Start: 1
• Page End: 21
• Publication Date: 2019-07-12
• Subjects: Pharmaceutical processing -- Risk analysis -- Uncertainty -- Design; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2019.03.039
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Pharmaceutical process design involves much uncertainty in the data on which it is based. The paper presents a quantitative approach to risk management for design using a stochastic approach which identifies key data required and allows for its systematic incorporation. Expected performance was improved by considering the propagation of uncertainty over the whole process. In this case study it was found that below a certain value in the feed purity 'perfect' knowledge had a very significant impact. Abstract: Process plants for the manufacture of pharmaceutical products often need to be designed and built quickly to make the most of available patent life which necessitates using uncertain or unavailable data. It is common that pilot plant equipment and data are available and new data can be generated if they are important. We present a model based approach to risk analysis to aid design for pharmaceutical processes which combines systematic modelling procedures with Hammersley sampling based uncertainty analysis and sensitivity analysis used to quantify predicted performance uncertainty and to identify key uncertainty contributions. The main contribution of the paper is the demonstration of the methodology on an industrial case study where the process flowsheet was fixed and some pilot data was available. Expected performance was improved by considering the propagation of uncertainty over the whole process. The case study results indicate the importance of considering uncertainty systematically and quantitatively. The methodology showed the opportunity to improve process performance potential through considering uncertainty systematically.