基于粒子群优化的生物质燃烧过程环保经济预测控制  

Particle swarm optimization based ecological economic predictive control of biomass combustion processes

作　　者：何德峰[1] 岑江晖 陈龙 王秀丽 HE Defeng;CEN Jianghui;CHEN Long;WANG Xiuli(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023)

机构地区：[1]浙江工业大学信息工程学院,杭州310023

出　　处：《高技术通讯》2023年第12期1333-1342,共10页Chinese High Technology Letters

基　　金：国家自然科学基金(62173303)资助项目。

摘　　要：考虑包含状态约束与控制约束的生物质非线性循环流化床锅炉(CFBB)燃烧过程的多目标控制问题,提出一种环保经济模型预测控制(EMPC)算法。采用机理建模方法建立约束生物质非线性燃烧过程模型。为了在系统稳定的前提下,优化生物质燃烧过程经济性能和环保性能,结合字典序方法与收缩约束关联优化问题,通过粒子群优化算法(PSO)求解优化问题,并结合滚动时域控制原理,设计CFBB燃烧过程环保型经济模型预测控制算法。最后仿真验证本文控制算法的有效性与优越性。Considering the multi-objective control problem of biomass nonlinear circulating fluidized bed boiler(CFBB)combustion process with state and control constraints,an ecological economic model predictive control(EMPC)strategy is proposed.Based on the mechanism modeling method,a constrained biomass nonlinear combustion process model is established.In order to optimize the economic and ecological performance of the biomass combustion process and ensure the stability of the process system,an ecological EMPC controller for the biomass combustion process is designed by combining lexicographic algorithm with a contraction constraint,particle swarm optimization algorithm(PSO)and receding horizon control method.Finally,the simulation results verify the effectiveness and superiority of the control strategy.

关 键 词：生物质循环流化床锅炉(CFBB) 模型预测控制(MPC) 多目标控制 粒子群优化算法(PSO) 经济最优 

分 类 号：TK6[动力工程及工程热物理—生物能] TP18[自动化与计算机技术—控制理论与控制工程]