Optimization of a crude distillation unit using a combination of wavelet neural network and line-up competition algorithm

doi:10.1016/j.cjche.2017.03.035

Abstract

Abstract: The modeling and optimization of an industrial-scale crude distillation unit (CDU) are addressed. The main specifications and base conditions of CDU are taken from a crude oil refinery in Wuhan, China. For modeling of a complicated CDU, an improved wavelet neural network (WNN) is presented to model the complicated CDU, in which novel parametric updating laws are developed to precisely capture the characteristics of CDU. To address CDU in an economically optimal manner, an economic optimization algorithm under prescribed constraints is presented. By using a combination of WNN-based optimization model and line-up competition algorithm (LCA), the superior performance of the proposed approach is verified. Compared with the base operating condition, it is validated that the increments of products including kerosene and diesel are up to 20% at least by increasing less than 5% duties of intermediate coolers such as second pump-around (PA2) and third pump-around (PA3).

Key words: Crude oil distillation, Wavelet neural network, Line-up competition algorithm, Optimization

摘要： The modeling and optimization of an industrial-scale crude distillation unit (CDU) are addressed. The main specifications and base conditions of CDU are taken from a crude oil refinery in Wuhan, China. For modeling of a complicated CDU, an improved wavelet neural network (WNN) is presented to model the complicated CDU, in which novel parametric updating laws are developed to precisely capture the characteristics of CDU. To address CDU in an economically optimal manner, an economic optimization algorithm under prescribed constraints is presented. By using a combination of WNN-based optimization model and line-up competition algorithm (LCA), the superior performance of the proposed approach is verified. Compared with the base operating condition, it is validated that the increments of products including kerosene and diesel are up to 20% at least by increasing less than 5% duties of intermediate coolers such as second pump-around (PA2) and third pump-around (PA3).

关键词: Crude oil distillation, Wavelet neural network, Line-up competition algorithm, Optimization

Bin Shi, Xu Yang, Liexiang Yan. Optimization of a crude distillation unit using a combination of wavelet neural network and line-up competition algorithm[J]. , 2017, 25(8): 1013-1021.

References

[1] A. Mizoguchi, T.E. Marlin, A.N. Hrymak, Operations optimization and control design for a petroleum distillation process, Can. J. Chem. Eng. 73(1995) 896-907.
[2] S.V. Inamdar, S.K. Gupta, D.N. Saraf, Multi-objective optimization of an industrial crude distillation unit using the elitist non-dominated sorting genetic algorithm, Chem. Eng. Res. Des. 82(2004) 611-623.
[3] R.K. More, V.K. Bulasara, R. Uppaluri, V.R. Banjara, Optimization of crude distillation system using aspen plus:effect of binary feed selection on grass-root design, Chem. Eng. Res. Des. 88(2010) 121-134.
[4] J.W. Seo, M. Oh, T.H. Lee, Design optimization of a crude oil distillation process, Chem. Eng. Technol. 23(2000) 157-164.
[5] J. Chen, D.S.H. Wong, S.S. Jang, S.L. Yang, Product and process development using artificial neural-network model and information analysis, AIChE J. 44(1998) 876-887.
[6] L.C.K. Liau, T.C.K. Yang, M.T. Tsai, Expert system of a crude oil distillation unit for process optimization using neural networks, Expert Syst. Appl. 26(2004) 247-255.
[7] S. Motlaghi, F. Jalali, M.N. Ahmadabadi, An expert system design for a crude oil distillation column with the neural networks model and the process optimization using genetic algorithm framework, Expert Syst. Appl. 35(2008) 1540-1545.
[8] H. Yao, J. Chu, Operational optimization of a simulated atmospheric distillation column using support vector regression models and information analysis, Chem. Eng. Res. Des. 90(2012) 2247-2261.
[9] L.M. Ochoa-Estopier, M. Jobson, R. Smith, Operational optimization of crude oil distillation systems using artificial neural networks, Comput. Chem. Eng. 59(2013) 178-185.
[10] K. Basak, K.S. Abhilash, S. Ganguly, D.N. Saraf, On-line optimization of a crude distillation unit with constraints on product properties, Ind. Eng. Chem. Res. 41(2002) 1557-1568.
[11] J.C.M. Hartmann, Determine the optimum crude intake level:A case history, Hydrocarb. Process. 80(2001) 77-84.
[12] H. Chitsaz, N. Amjady, H. Zareipour, Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm, Energy Convers. Manag. 89(2015) 588-598.
[13] Q. Zhang, A. Benveniste, Wavelet networks, IEEE Trans. Neural Netw. 3(1992) 889-898.
[14] L.X. Yan, D.X. Ma, Global optimization of non-convex nonlinear programs using lineup competition algorithm, Comput. Chem. Eng. 25(2001) 1601-1610.
[15] J. Zhang, G.G. Walter, Y. Miao, W.N.W. Lee, Wavelet neural networks for function learning, IEEE Trans. Signal Process. 43(1995) 1485-1497.
[16] S. Billings, H.L. Wei, A new class of wavelet networks for nonlinear system identification, IEEE Trans. Neural Netw. 16(2005) 862-874.
[17] I. Daubechies, Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, Philadelphia, 1992.
[18] S. Chi, Character recognition based on wavelet neural network optimized with PSO algorithm, Appl. Mech. Mater. 602(2014) 1834-1837.
[19] Y. Lu, N. Zeng, Y. Liu, N. Zhang, A hybrid Wavelet Neural Network and Switching Particle Swarm Optimization algorithm for face direction recognition, Neurocomputing 155(2015) 219-224.
[20] L.X. Yan, Solving combinatorial optimization problems with line-up competition algorithm, Comput. Chem. Eng. 27(2003) 251-258.
[21] L.X. Yan, K. Shen, S. Hu, Solving mixed integer nonlinear programming problems with line-up competition algorithm, Comput. Chem. Eng. 28(2004) 2647-2657.