面向过程经济性能优化的最优性条件在线预测与实现  

An On-line Prediction and Implementation of Necessary Conditions of Optimality towards Process Economic Optimization

作　　者：沈俊鹏 黄静雯[1] 李宏光[1] 刘操[1] 

机构地区：[1]北京化工大学,北京100029

出　　处：《信息与控制》2015年第4期449-454,共6页Information and Control

基　　金：中央高校基本科研业务费专项基金资助项目(YS1404);国家质检总局科技计划资助项目(2015IK048)

摘　　要：选择包含系统的约束条件和梯度信息的最优性条件(necessary conditions of optimality,NCO)作为控制系统的实现目标,可以从结构设计角度有效地提高生产过程的经济性能.针对NCO在线不易直接测量、离线估计不及时和不准确等问题,提出了一类基于高斯过程的NCO建模、预测及控制方法.通过生产过程实时数据在线动态更新高斯过程模型,实时准确预测NCO的值,以决策下一时刻的最优控制率,实现生产过程对经济性能最优值的跟踪.对一个化工过程实例进行了仿真,结果验证了该方法的有效性.The economic performance of plants can be effectively improved in terms of control system structure opti- mization through the selection of necessary conditions of optimality （NCOs）, including constraint and gradient information, as the implementation targets of control systems. In response to the difficulties in direct online measurements and in timely and accurate offline estimations of NCOs, a novel approach to NCO modeling, prediction, and control based on a Gaussian process is proposed. To process real-time data, the proposed process can update the Gaussian process model dynamically online and the NCOs can be accurately predicted to enable the best decision of the optimal control rate for the next moment, thereby enabling process economic performance to achieve optimality. The proposed approaches are applied to an exemplary chemical process simulation, and the results show the effectiveness of the proposed method.

关 键 词：高斯过程 经济性能 在线预测 优化控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]