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A machine learning-based methodology for multi-parametric solution of chemical processes operation optimization under uncertainty

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10.1016/j.cej.2021.131632
 
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hdl:2117/360198

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Shokry Abdelaleem Taha Zied, Ahmed
Medina González, Sergio
Baraldi, Piero
Zio, Enrico
Moulines, Eric François Victor
Espuña Camarasa, AntonioMés informacióMés informacióMés informació
Document typeArticle
Defense date2021-12-01
PublisherElsevier
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Chemical process operation optimization aims at obtaining the optimal operating set-points by real-time solution of an optimization problem that embeds a steady-state model of the process. This task is challenged by unavoidable Uncertain Parameters (UPs) variations. MultiParametric Programming (MPP) is an approach for solving this challenge, where the optimal set-points must be updated online, reacting to sudden changes in the UPs. MPP provides algebraic functions describing the optimal solution as a function of the UPs, which allows alleviating large computational cost required for solving the optimization problem each time the UPs values vary. However, MPP applicability requires a well-constructed mathematical model of the process, which is not suited for process operation optimization, where complex, highly nonlinear and/or black-box models are usually used. To tackle this issue, this paper proposes a machine learning-based methodology for multiparametric solution of continuous optimization problems. The methodology relies on the offline development of data-driven models that accurately approximate the multiparametric behavior of the optimal solution over the UPs space. The models are developed using data generated by running the optimization using the original complex process model under different UPs values. The models are, then, used online to, quickly, predict the optimal solutions in response to UPs variation. The methodology is applied to benchmark examples and two case studies of process operation optimization. The results demonstrate the methodology effectiveness in terms of high prediction accuracy (less than 1% of NRMSE, in most cases), robustness to deal with problems of different natures (linear, bilinear, quadratic, nonlinear and/or black boxes) and significant reduction in the complexity of the solution procedure compared to traditional approaches (a minimum of 67% reduction in the optimization time).
CitationShokry, A. [et al.]. A machine learning-based methodology for multi-parametric solution of chemical processes operation optimization under uncertainty. "Chemical engineering journal", 1 Desembre 2021, vol. 425, p. 131632. 
URIhttp://hdl.handle.net/2117/360198
DOI10.1016/j.cej.2021.131632
ISSN0300-9467
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S1385894721032137
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  • CEPIMA - Center for Process and Environment Engineering - Articles de revista [128]
  • Departament d'Enginyeria Química - Articles de revista [2.066]
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