Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

doi:10.1016/j.cjche.2023.02.027

Chinese Journal of Chemical Engineering ›› 2023, Vol. 61 ›› Issue (9): 43-57.DOI: 10.1016/j.cjche.2023.02.027

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Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Jian Long^1,2, Kai Deng¹, Renchu He^1,2

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China;
2. Engineering Research Center of Process System Engineering, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2022-09-19 Revised:2023-02-20 Online:2023-12-14 Published:2023-09-28
Contact: Renchu He,E-mail:renchuhe@ecust.edu.cn
Supported by:
This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101), Shanghai Committee of Science and Technology (22DZ1101500), the National Natural Science Foundation of China (61973124, 62073142) and Fundamental Research Funds for the Central Universities.

Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Jian Long^1,2, Kai Deng¹, Renchu He^1,2

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China;
2. Engineering Research Center of Process System Engineering, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

通讯作者: Renchu He,E-mail:renchuhe@ecust.edu.cn
基金资助:
This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101), Shanghai Committee of Science and Technology (22DZ1101500), the National Natural Science Foundation of China (61973124, 62073142) and Fundamental Research Funds for the Central Universities.

Abstract

Abstract: Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries. However, the optimization model is complex and difficult to build, which is a typical mixed integer nonlinear programming (MINLP) problem. Considering the large scale of the MINLP model, in order to improve the efficiency of the solution, the mixed integer linear programming - nonlinear programming (MILP-NLP) strategy is used to solve the problem. This paper uses the linear blending rules plus the blending effect correction to build the gasoline blending model, and a relaxed MILP model is constructed on this basis. The particle swarm optimization algorithm with niche technology (NPSO) is proposed to optimize the solution, and the high-precision soft-sensor method is used to calculate the deviation of gasoline attributes, the blending effect is dynamically corrected to ensure the accuracy of the blending effect and optimization results, thus forming a prediction-verification-reprediction closed-loop scheduling optimization strategy suitable for engineering applications. The optimization result of the MILP model provides a good initial point. By fixing the integer variables to the MILP-optimal value, the approximate MINLP optimal solution can be obtained through a NLP solution. The above solution strategy has been successfully applied to the actual gasoline production case of a refinery (3.5 million tons per year), and the results show that the strategy is effective and feasible. The optimization results based on the closed-loop scheduling optimization strategy have higher reliability. Compared with the standard particle swarm optimization algorithm, NPSO algorithm improves the optimization ability and efficiency to a certain extent, effectively reduces the blending cost while ensuring the convergence speed.

Key words: Blend, Optimization algorithm, Neural networks, Particle swarm optimization, Mixed integer programming

摘要： Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries. However, the optimization model is complex and difficult to build, which is a typical mixed integer nonlinear programming (MINLP) problem. Considering the large scale of the MINLP model, in order to improve the efficiency of the solution, the mixed integer linear programming - nonlinear programming (MILP-NLP) strategy is used to solve the problem. This paper uses the linear blending rules plus the blending effect correction to build the gasoline blending model, and a relaxed MILP model is constructed on this basis. The particle swarm optimization algorithm with niche technology (NPSO) is proposed to optimize the solution, and the high-precision soft-sensor method is used to calculate the deviation of gasoline attributes, the blending effect is dynamically corrected to ensure the accuracy of the blending effect and optimization results, thus forming a prediction-verification-reprediction closed-loop scheduling optimization strategy suitable for engineering applications. The optimization result of the MILP model provides a good initial point. By fixing the integer variables to the MILP-optimal value, the approximate MINLP optimal solution can be obtained through a NLP solution. The above solution strategy has been successfully applied to the actual gasoline production case of a refinery (3.5 million tons per year), and the results show that the strategy is effective and feasible. The optimization results based on the closed-loop scheduling optimization strategy have higher reliability. Compared with the standard particle swarm optimization algorithm, NPSO algorithm improves the optimization ability and efficiency to a certain extent, effectively reduces the blending cost while ensuring the convergence speed.

关键词: Blend, Optimization algorithm, Neural networks, Particle swarm optimization, Mixed integer programming

Jian Long, Kai Deng, Renchu He. Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process[J]. Chinese Journal of Chemical Engineering, 2023, 61(9): 43-57.

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Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

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