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A machine learning-based methodology for multi-parametric solution of chemical processes operation optimization under uncertainty
- Source :
- Chemical Engineering Journal, Chemical Engineering Journal, 2021, 425, pp.131632. ⟨10.1016/j.cej.2021.131632⟩, Chemical Engineering Journal, Elsevier, 2021, 425, pp.131632. ⟨10.1016/j.cej.2021.131632⟩, UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC)
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
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).
- Subjects :
- Chemical process
0209 industrial biotechnology
Optimization problem
Computer science
General Chemical Engineering
Chemical processes
Bilinear interpolation
02 engineering and technology
Machine learning
computer.software_genre
Industrial and Manufacturing Engineering
[SHS]Humanities and Social Sciences
Reduction (complexity)
Multiparametric programming
020901 industrial engineering & automation
Enginyeria química [Àrees temàtiques de la UPC]
020401 chemical engineering
Robustness (computer science)
Environmental Chemistry
[SHS.GEST-RISQ]Humanities and Social Sciences/domain_shs.gest-risq
0204 chemical engineering
ComputingMilieux_MISCELLANEOUS
Continuous optimization
Processos químics
business.industry
Uncertainty
General Chemistry
Nonlinear system
Kriging
Operation optimization
Benchmark (computing)
Artificial intelligence
business
computer
Gaussian process regression
Subjects
Details
- Language :
- English
- ISSN :
- 13858947
- Database :
- OpenAIRE
- Journal :
- Chemical Engineering Journal, Chemical Engineering Journal, 2021, 425, pp.131632. ⟨10.1016/j.cej.2021.131632⟩, Chemical Engineering Journal, Elsevier, 2021, 425, pp.131632. ⟨10.1016/j.cej.2021.131632⟩, UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC)
- Accession number :
- edsair.doi.dedup.....87c449b0e0d882a173feda7bfd601ddb