Chemical process optimization: Evaluation of effectiveness of open-source software Scilab in solve constrained nonlinear programming problems

Authors

DOI:

https://doi.org/10.33448/rsd-v10i9.17866

Keywords:

IpOpt; Williams-Otto plants; Optimization; Fmincon; Scilab.

Abstract

Solution of nonlinear optimization problems with equality and inequality constraints is a hard task and tend to be more complex when involves a large number of variables. Energy and control systems and chemical plants are represented by this type of problem. With technological and computational progress, its necessary use effective tools that can handle with characteristics of this type of systems, effectively solving optimization problem. In this work, we solve two classical problems of nonlinear optimization with constraints of chemical process in free software Scilab, using the function fmincon, in order to evaluate the performance of the algorithm for solving constrained nonlinear programming problems.  Results are compared with optimal conditions obtained with Matlab. Free software shows a good performance, achieved effective results and finding points thata results in a increase of 16% in objective function to investiment return of Williams-Otto plant, showing itself an efficient alternative for the development of research and technology in this field.  

References

Baudin, M. (2010). Introduction to scilab. Consortium Scilab, 34–56.

Bhaskar, V., Gupta, S. K., & Ray, A. K. (2000). Applications of multiobjective optimization in chemical engineering. Reviews in Chemical Engineering, 16(1), 1–54.

Biegler, L. T. (2018). New directions for nonlinear process optimization. Current Opinion in Chemical Engineering, 21, 32–40.

Cardoso, A. O. (2016). Otimização Descentralizada Coordenada aliada a estratégias de Controle Plantwide para o Controle de Processos Químicos. Universidade Federal de São Carlos (UFSCar).

Cardoso, A. O., & Kwong, W. H. (2020). Estrutura de controle plantwide simplificada para a planta de Williams-Otto: otimização descentralizada coordenada com IBMF. Research, Society and Development, 9(9), 1–21.

Chaudhary, M. N. R. (2009). Real Time Optimization of Chemical Processes. Curtin University of Technology.

Costa, E. P., Figueiredo, M., Politano, P. R., & Kwong, W. H. (2019). O impacto na programação da produção devido à integração das camdas de controle avançado e de scheduling na indústria de processos. Brazilian Journal of Business, 1(3), 1715–1727.

Delgado, J. A. (2021). Método primal-dual de pontos interiores-penalidade e o problema de fluxo de potência ótimo reativo com variáveis de controle discretas. Universidade Estadual Paulista _Júlio de Mesquita Filho_.

Edgar, T. F., Himmelblau, D. M., & Lasdon, L. S. (2001). Optimization of Chemical Processes. McGraw-Hill.

Findeisen, W., Bailey, F. N., Brdys, M., Malinowski, K., Tatjewski, P., & Wãsniak, A. (1980). Control and Coordination in Hierarchical Systems. Jonh Wiley & Sons.

Gavira, M. de O. (2003). Simulação como ferramenta computacional de aquisição de conhecimento. UFSCar.

Govindarajan, L., & Karunanithi, T. (2005). Multiobjective optimization of process plant using genetic algorithm. International Journal of Computational Intelligence and Applications, 5(04), 425–437.

Guardia, L. E. T. (2005). Método dos Pontos Interiores para programação não-linear em redes. XXXVII Simpósio Brasileiro de Pesquisa Operacional, 1930–1936.

Inalhan, G., Stipanović, D. M., & Tomlin, C. J. (2002). Decentralized optimization, with application to multiple aircraft coordination. Proceedings of the IEEE Conference on Decision and Control, 1, 1147–1155. https://doi.org/10.1109/cdc.2002.1184667

Jin, X. (1996). Approaching sustainability in engineering design with multiple criteria decision analysis. Oklahoma State University.

Jung, B. S., Miroshi, W., & Ray, W. H. (1971). Large Scale Process Optimization Techniques Applied to Chemical Petroleum Processes. The Canadian Journal of Chemical Engineering, 844–852.

Kwong, W. H. (1992). Otimizaç{ão}de Plantas Industriais Complexas. Escola Politécnica da Universidade de S{ão}Paulo (USP).

Liu, N., & Qin, S. (2019). A neurodynamic approach to nonlinear optimization problems with affine equality and convex inequality constraints. Neural Networks, 109, 147–158. https://doi.org/10.1016/j.neunet.2018.10.010

Miller, D. C., Siirola, J. D., Agarwal, D., Burgard, A. P., Lee, A., Eslick, J. C., Nicholson, B., Laird, C., Biegler, L. T., Bhattacharyya, D., Sahinidis, N. V., Ignacio E. Grossmann, Gounaris, C. E., & Gunter, D. (2018). Next Generation Multi-Scale Process Systems Engineering Framework. Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018, 2209–2214.

Patil, B. P., Maia, E., & Ricardez-Sandoval, L. A. (2015). Integration of Scheduling, Design and Congtrol of Multiproduct ChemicalProcesses under Uncertainty. AIChE Journal, 61(8), 2456–2470.

Pereira, A. S., Shitsuka, D. M., Parreira, F. J., & Shitsuka, R. (2018). Metodologia da pesquisa científica [recurso eletrônico] (1a edição). NTE/UFSM. https://repositorio.ufsm.br/bitstream/handle/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1

Rangaiah, G. P., & Kariwala, V. (2012). Plantwide Control: recent developments and Applications. Jonh Wiley & Sons.

Rao, S. S. (2009). Engineering Optimization: Theory and Pratice (John Wiley and Sons (ed.) (4a ed.).

Ryoo, H. S., & Sahinidis, N. V. (1995). Global optimization of nonconvex NLPs and MINLPs with applications in process design. Computers and Chemical Engineering, 19(5), 551–566. https://doi.org/10.1016/0098-1354(94)00097-2

Santos, T. J. G. dos. (1998). Um novo algoritmo de penalização hiperbólica para resolução de problema de programação não-linear com restrições de igualdade.

Thakur, S. S., Ojasvi, Kumar, V., & Nitin, K. (2017). Continuous diisobutylene manufacturing: Conceptual process design and plantwide control. Computers and Chemical Engineering, 97(59–75).

Vinante, C., & Valladares, E. (1985). Application of the method of multipliers to the optimization of chemical processes. Computers & Chemical Engineering, 9(1), 83–87.

Willians, T. J., & Otto, R. E. (1960). A Generalized Chemical Processing Model for the Investigation of Computer Control. Transactions of the American Institute of Electrical Engineers, Part I: Communication and Electronics, 79(5), 458–473.

Zhou, M., Cai, Y., Su, H., Wozny, G., & Pan, H. (2018). A survey on applications of optimization-based integrating process design and control for chemical processes. Chemical Engineering Communications, 205(10), 1365–1383.

Williams, T. J., & Otto, R. E. (1960). A generalized chemical processing model for the investigation of computer control. Transactions of the American Institute of Electrical Engineers. Part I: Communication and Electronics, 79(5), 458-473.

Jung, B. S., Mirosh, W., & Ray, W. H. (1971). Large scale process optimization techniques applied to chemical and petroleum processes. The Canadian Journal of Chemical Engineering, 49(6), 844-852.

Published

28/07/2021

How to Cite

SILVA, B. K. .; KWONG, W. H. .; CARDOSO, A. de O. Chemical process optimization: Evaluation of effectiveness of open-source software Scilab in solve constrained nonlinear programming problems . Research, Society and Development, [S. l.], v. 10, n. 9, p. e39110917866, 2021. DOI: 10.33448/rsd-v10i9.17866. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/17866. Acesso em: 18 apr. 2024.

Issue

Section

Engineerings