(1. 中南大学 信息科学与工程学院，长沙 410083；
2. 湖南第一师范学院 信息科学与工程系，长沙 410205；
3. 内蒙古鑫旺再生资源有限公司，鄂尔多斯 014300)
摘 要: 分解温度是氧化铝晶种分解工序中的关键工艺参数。为精确控制分解温度，运用机理分析与参数辨识相结合的方法建立带板式换热器种分槽系统的非线性动态模型，并利用实际生产过程数据验证模型的正确性。提出一种基于不可测干扰预测的非线性模型预测控制(DP-NMPC)方法，利用时间序列分析方法建立系统中不可测扰动的自适应预测模型，并以此模型对分解温度预测模型进行校正。基于实际生产过程数据的仿真研究表明，相比常规NMPC，该方法提高了预测模型的精度，使控制系统能快速跟踪系统设定值，更好地抑制超调，因而其抗干扰能力更强，能对晶种分解温度进行有效控制。由于该方法适用于具有不可测非白噪声强干扰过程的模型预测控制，具有显著的实用价值。
关键字: 氧化铝；晶种分解；扰动预测；非线性预测控制
（1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. Department of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China;
3. Inner Mongolia Xinwang Renewable Resources Co., Ltd., Erdos 014300, China）
Abstract:The decomposition temperature is the key technological parameter in alumina seed precipitation process. In order to control the decomposition temperature precisely, a nonlinear dynamic model of the precipitator equipped with a plate heat exchanger in alumina tri-hydrate precipitation was built by mechanism analysis and parameter estimation, and the accuracy of the model was proved by the simulation with actual process data. A nonlinear model predictive control (DP-NMPC) method based on the unmeasured disturbances prediction was proposed, which applies the analysis of time series to build an adaptive predictive model of unmeasured disturbances in the precipitator system, and then revises the decomposition temperature predictive model. Comparing with the common NMPC, the proposed method is more effective in controlling decomposition temperature, which improves the accuracy of the predictive model, performs a quick following of set point changes, and has a better reduction of overshoot and a stronger rejection of disturbances. That method can be applied to the process with strong unmeasured nonwhite disturbances, and has remarkable practical value.
Key words: alumina; seed precipitation; disturbances prediction; nonlinear model predictive control
