针对锅炉蒸汽温度(汽温)模型预测控制在机组大范围负荷变动工况下的适应性问题，通过机理性分析和锅炉换热设备的特性建立了汽温对象的多机理模型，利用粒子群算法对所建模型进行修正，并引入了模糊切换功能，使模型能够更加精确地描述实际气温变化过程，提高了其自适应性和预测控制的可靠性。%In view of the challenge in controlling temperature when the boiler experiences load changes in a large scope of ramp rates,this paper presents a practical solution when modular predictive control was ap-plied for tight control.By theoretical analysis and based on the heat exchangers'performance,the multi-mechanism model for steam temperature was established.Moreover,the particle swarm optimization (PSO) algorithm was adopted to revise the established model.Furthermore,the fuzzy switch function was intro-duced to make the multi-mechanism model can describe the actual condition more accurately.Thus,the a-daptability of the built model can be enhanced and its predictive control function can be improved.
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