Hybrid nonlinear model predictive control of a cooling water network

被引：11
|
作者：
Viljoen, J. H. [1 ]
Muller, C. J. [1 ]
Craig, I. K. [1 ]
机构：
[1] Univ Pretoria, Dept Elect Elect & Comp Engn, Pretoria, South Africa
来源：
CONTROL ENGINEERING PRACTICE | 2020年 / 97卷
基金：
新加坡国家研究基金会; 芬兰科学院;
关键词：
Nonlinear model predictive control; Cooling tower; Cooling water network; Optimisation; Gradient descent; Hybrid systems; Electricity consumption minimisation; AUTOMATIC DIFFERENTIATION; REAL-TIME; PERFORMANCE EVALUATION; TRAJECTORY TRACKING; OPTIMIZATION; SYSTEMS; TOWERS;
D O I：
10.1016/j.conengprac.2020.104319
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A Hybrid Nonlinear Model Predictive Control (HNMPC) strategy is developed for temperature control and power consumption minimisation of a cooling water network. The HNMPC uses a gradient descent optimisation algorithm for the continuous manipulated variables, and an enumerated tree traversal algorithm to control and optimise the Boolean manipulated variables. The HNMPC is subjected to disturbances similar to those experienced on a real plant, and its performance compared to a continuous Nonlinear Model Predictive Control (NMPC) and two base case scenarios. Power consumption is minimised, and process temperature disturbances are successfully rejected. Monetary benefits of the HNMPC control strategy are estimated.
引用
收藏
页数：18
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