氧化铝晶种分解温度的分散自适应模型预测控制

摘要

氧化铝晶种分解过程是一个具有多级串联结构和强烈不可测干扰的大规模复杂过程，分解温度是其关键工艺参数。为精确控制分解温度，根据该过程的结构特点，将其分成多个子系统，并综合机理分析、参数辨识和时间序列分析方法建立基于不可测扰动预测的子系统自适应预测控制模型，并将前级子系统的状态作为可测扰动引入本级子系统模型，分别求解各子系统的优化控制目标，获取优化操作变量。基于实际生产过程数据的仿真结果表明，所提出的分散型自适应模型预测控制方法具有较强的抗干扰能力，能准确跟踪分解温度设定值，满足晶种分解生产过程中对分解终止温度、分解始末温差和降温速度的控制要求。本方法对于具有串联结构和不可测强干扰的非线性大规模复杂过程的模型预测控制具有显著的实用价值。%The alumina seed precipitation process is a complicated large-scale process with multi-stage tandem structure and strong unmeasured disturbances, in which the decomposition temperature is a key technological parameter. In order to control the decomposition temperature precisely, the process was divided into several subsystems according to its structural characteristics, and the adaptive predictive model of each subsystem based on unmeasured disturbance prediction was built by mechanism analysis, parameter estimation and time series analysis method. The front-end subsystem state as a measurable disturbance was introduced into the corresponding subsystem model, and the optimal operational variables were obtained by respectively solving the optimization objectives of each subsystem. The simulation results based on actual process data show that the proposed decentralized adaptive model predictive control (MPC) method has a strong capacity of resisting disturbances and a good following of the set point, and can meet the control requirements of terminal decomposition temperature, decomposition temperature range and the cooling rate for the alumina precipitation process. The proposed method can be applied to the nonlinear complicated large-scale process with multi-stage tandem structure and strong unmeasured disturbances, and is of remarkable practical value.