催化裂化装置非线性预测控制器的设计与应用

发布时间：2018-01-06 01:19

本文关键词：催化裂化装置非线性预测控制器的设计与应用　出处：《重庆科技学院》2017年硕士论文　论文类型：学位论文

【摘要】：石油化工工业在我国国民经济中占有举足轻重的地位,承担着为我国提供各种能源的重担,对整个农业、工业、交通运输业以及国防科技的发展有着极其重大的影响。同时,由于资源有限,石化行业面临着减少能耗、提高石油资源利用率的挑战。目前,绝大多数石化企业都引入了自动化技术手段,先进的过程控制技术对提高石油化工产量、降低能耗、稳定生产过程有着重要的作用,能够产生明显的经济效益。催化裂化是石油化工过程的重要工艺之一,主要包括原料油催化裂化、催化剂再生、产品分离三个过程,目的是在高温高压的条件下,使得原油和催化剂发生反应,生成一系列轻质油产品。常规的催化裂化装置由三个部分组成,包含反应再生系统、分馏系统以及吸收稳定系统。作为催化裂化的核心部分,反应再生系统(Reaction regeneration system,RRS)将原油经过加工,生成各种各样的轻质油产品。因此,采用先进的科学的RRS控制技术,对于提高催化裂化轻质油产品质量、提高炼油企业的经济效益至关重要。本文的主要研究内容如下:(1)RRS的反应流程以及控制技术。本章首先对RRS的应用现状、面临的问题进行了研究分析,然后介绍了RRS的工艺流程,在分析提高反应再生系统经济方法的基础上,对RRS的各种控制技术进行比较和分析。(2)模型预测控制(Model predictive control,MPC)以及双层模型预测控制(Doublelayed model predictive control,DMPC)对RRS的优化控制。本章对传统经典的模型预测控制进行介绍,并在单层预测控制的基础上,考虑各变量优先级以及约束条件的情况下,引入了多优先级稳态目标计算,形成了DMPC,利用双层预测控制方法实现了对RRS进行优化控制,最后对其优缺点进行了分析。(3)基于粒子群算法的双层预测控制(the double-layered model predictive control based on particle swarm optimization,PSO-DMPC)对RRS的优化控制。本章在DMPC的基础上,为了避免放松操作变量和被控变量约束条件带来的不良影响,采用粒子群算法(Particle swarm optimization,PSO)对DMPC中的经济优化目标函数和动态矩阵控制阶段的目标函数进行求解,并将该控制方法用于对反应再生系统的优化控制,结果表明,基于本方法的RRS能对被控变量和操作变量进行跟踪。(4)基于量子粒子群的双层预测控制对RRS的优化控制。本章针对传统粒子群算法不能保证全局收敛等缺点,提出了基于量子粒子群的双层预测控制(the double-layered model predictive control based on quantum behaved particle swarm optimization,QPSODMPC),量子粒子群算法(Quantum behaved particle swarm optimization,QPSO)在求解目标函数的过程中能保证全局收敛,且计算速度较快。将本方法用于RRS的优化控制中,并与PSO-DMPC和QGA-DMPC(the double-layered model predictive control based on quantum genetic algorithm,QGA-DMPC)进行对比。结果表明,QPSO-DMPC的控制跟踪效果比同类方法好。(5)基于多目标量子粒子群的双层预测控制对RRS的优化控制。本章在结合工程实际的基本问题上,将多目标考虑在RRS的优化控制中,提出了基于多目标量子粒子群的双层预测控制方法(the double-layered model predictive control based on multi objective quantum behaved particle swarm optimization,MQPSO-DMPC),在建立RRS的多目标函数的基础上,采用多目标量子粒子群算法(Multi objective quantum behaved particle swarm optimization,MQPSO)对其进行求解,并将求解结果应用到DMPC中,以此完成对RRS的多目标稳态优化控制。结果表明,基于本方法的双层预测控制能对RRS的操作变量和被控变量进行跟踪。
[Abstract]:The petroleum chemical industry occupies a pivotal position in China's national economy, bear the burden of providing all kinds of energy in our country, the agricultural industry, and it plays an important role in the transportation industry and the development of national defense technology. At the same time, due to limited resources, the petrochemical industry faces to reduce energy consumption and improve the utilization of petroleum resources the challenge. At present, the vast majority of petrochemical enterprises have introduced automation technology, advanced process control technology to reduce energy consumption to improve the petrochemical production, plays an important role in the stable production process, can produce significant economic benefits. Catalytic cracking is one of the important process of petrochemical process, mainly including catalytic cracking, raw materials oil catalyst regeneration, three separate products, aimed at high temperature and high pressure conditions, the crude oil and catalyst reaction to generate a series of light oil products. The conventional catalytic cracking unit is composed of three parts, including the reaction regeneration system, fractionation system and absorption stabilization system. As the core part of a catalytic cracking reaction regeneration system (Reaction regeneration, system, RRS) after processing the crude oil, light oil to produce a wide variety of products. Therefore, the use of science and advanced RRS control technology, to improve the catalytic cracking light oil product quality, improve the economic benefits of essential oil refining enterprises. The main contents of this paper are as follows: (1) the reaction process of RRS and control technology. Firstly, the application situation of RRS, analyzed the problems, and then introduces the process of RRS, based on the analysis of economy method of reaction regeneration system on the improvement of RRS control technology are compared and analyzed. (2) the model predictive control (Model predictive control, MPC) and double mode Model predictive control (Doublelayed model predictive control, DMPC RRS) to control the optimization. This chapter of the classic model predictive control are introduced, and the single predictive control based on the consideration of each variable priority and constraint conditions, the introduction of multi priority steady-state target calculation, the formation of DMPC, the use of double predictive control method to realize the optimized control of RRS, finally analyzes its advantages and disadvantages. (3) double forecasting control based on particle swarm algorithm (the double-layered model predictive control based on particle swarm optimization, PSO-DMPC RRS) to control the optimization. This chapter on the basis of DMPC, in order to avoid adverse effects bring a relaxed operation and controlled variables constraints, using particle swarm algorithm (Particle swarm optimization, PSO) on economic optimization objective function and dynamic moment in DMPC The objective function for the array control stage, and this control method is used to control, optimization of reaction regeneration system. The results show that this method can track the RRS controlled variables and operating variables. (4) based on the control of optimal control of RRS double forecasting based on quantum particle swarm. This chapter focuses on the traditional particle swarm algorithm does not guarantee a global convergence, presents a double predictive control based on Quantum Particle Swarm (the double-layered model predictive control based on quantum behaved particle swarm optimization, QPSODMPC), quantum particle swarm algorithm (Quantum behaved particle swarm optimization, QPSO) can guarantee global convergence in the process of solving the objective function, and the calculation speed fast. This method was applied in the optimization of RRS control, and PSO-DMPC and QGA-DMPC (the double-layered model predictive control based on Qu Antum genetic algorithm, QGA-DMPC) were compared. The results show that the tracking control effect of QPSO-DMPC than other methods. (5) double forecasting multi-objective quantum particle swarm optimization control to control based on RRS. In this chapter, the basic problems of the engineering, the multi-objective control considered in the optimization of RRS, put forward the double control method of multi object forecasting based on Quantum Particle Swarm (the double-layered model predictive control based on multi objective quantum behaved particle swarm optimization, MQPSO-DMPC), based on the objective function of the RRS, a multi-objective quantum particle swarm algorithm (Multi objective quantum behaved particle swarm optimization, MQPSO) to solve it is applied to the results of DMPC, in order to achieve multi-objective steady state optimization control of RRS. The results show that the method is based on the double The layer predictive control can track the operating variables and the controlled variables of the RRS.

【学位授予单位】：重庆科技学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TE96

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