Optimal control method for reaction regeneration system based on qpso-dmpc
A QPSO-DMPC, reaction regeneration technology, applied in data processing applications, forecasting, instruments, etc., can solve the problems of heavy hardware burden and low control precision, and achieve the goal of reducing hardware burden, high randomness, and optimal operating variable parameters Effect
Active Publication Date: 2020-08-25
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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[0003] In view of the above problems, the purpose of the present invention is to provide a QPSO-DMPC-based reaction regeneration system optimization control method to solve the problems of low control accuracy and large hardware burden of the existing nonlinear reaction regeneration system
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[0015] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident, however, that these embodiments may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more embodiments.
[0016] Glossary
[0017] DMPC: The Double-layerd Model Predictive Control, double-layer model predictive control.
[0018] QPSO: Quantum-behaved Particle Swarm Optimization algorithm, quantum particle swarm optimization algorithm.
[0019] figure 1 The flow chart of the QPSO-DMPC-based reaction regeneration system optimization control method according to the present invention is shown.
[0020] Such as figure 1 As shown, the QPSO-DMPC-based reaction regeneration system optimization control method provided by the invention comprises the following steps: ...
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The invention discloses a reaction regeneration system optimization control method based on QPSO-DMPC, the method comprises following steps; S1, a transfer function model of a reaction regeneration system is converted as a step response model; S2, a DMPC model is established and comprises an open loop prediction module, a steady state target calculation module and a dynamic matrix control module; S3, strong randomness of particles in a search space in a QPSO algorithm is utilized, in a condition of no relaxing constraint conditions, an economic optimization function is solved in a larger area than before; S4, an output set value of the reaction regeneration system is obtained through the result obtained by solving the economic optimization function according to the QPSO algorithm, a bias between the output set value and actual output serves as a target error function, the QPSO algorithm is utilized to solve the target error function, and an optimal variation of an operating variable is obtained. According to the invention, the RRS hardware burden can be reduced, better operating variable parameters can be obtained, and optimization control is performed on RRS based on ensuring the economic benefits.
Description
technical field [0001] The invention belongs to the technical field of petrochemical industry, and relates to a QPSO-DMPC-based optimization control method for a reaction regeneration system. Background technique [0002] The petrochemical industry occupies a pivotal position in my country's national economy and bears the burden of providing various energy sources for our country. Conventional catalytic cracking unit consists of three parts, including reaction regeneration system, fractionation system and absorption stabilization system. As the core part of catalytic cracking, the reaction regeneration system (Reaction regeneration system, RRS) processes crude oil to produce various light oil products. However, the existing reaction regeneration system is a nonlinear reaction regeneration system, which has the problems of low control precision and heavy hardware burden. Contents of the invention [0003] In view of the above problems, the purpose of the present invention...
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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06
CPCG06Q10/04G06Q10/067G06Q50/06
Inventor 陈雪梅吴凌白竣仁周伟陈实易军
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY