Optimization control system for nitrifying process

2011 ◽  
Author(s):  
Wenbo Na
Keyword(s):  
Control System ◽  
Optimization Control

An intelligent optimization control method for the electrode current of submerged arc furnaces based on case reasoning

2020 ◽  
Vol 234 (10) ◽  
pp. 1088-1100
Author(s):  
Jianjun He ◽  
Chang Wang ◽  
Qi Zhu ◽  
Ling Shen ◽  
Mande Jing ◽  
...  
Keyword(s):  
Control System ◽  
Control Method ◽  
Arc Furnace ◽  
Intelligent Optimization ◽  
Electrode Current ◽  
Three Phase ◽  
Optimization Control ◽  
Submerged Arc Furnace ◽  
Case Reasoning ◽  
Submerged Arc

Due to the nonlinearity, strong coupling, and time-varying characteristics of three-phase electrode lift system of submerged arc furnace, the existing manual operation has the problems of electrode control hysteresis, poor balance of three-phase electrode current, and blindness of electrode current target setting. An intelligent optimization control method for the electrode current of submerged arc furnace based on case reasoning is proposed in this article, which is used to realize the automatic control of the electrode control system of the submerged arc furnace. First, the optimization model of electrode current setting value of the submerged arc furnace is established by the case-based reasoning method, and the corresponding electrode current value is calculated to maximize the yield in the safe power range of the furnace. Next, a three-phase electrode current decoupling controller is designed based on fuzzy rules. Finally, an intelligent optimization control system of three-phase electrode current of submerged arc furnace is designed and its superiority is verified by comparison with the proportional–integral–derivative controller. The designed control system has been applied to the smelting production of submerged arc furnace in a domestic smelter. The simulation and industrial operation results show that the system realizes automatic balance adjustment of electrode current of submerged arc furnace under normal working conditions, which greatly reduces the labor intensity of the operator, increases the smelting yield, reduces the unit energy consumption, and brings significant economic and social benefits to the enterprise.

Water plant optimization control system based on machine learning

2021 ◽  
Vol 222 ◽  
pp. 168-181
Author(s):  
Dongsheng Wang ◽  
Yan Wang ◽  
Rui Zhou ◽  
Yong Cao ◽  
Fuchun Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Water Plant ◽  
Optimization Control ◽  
Plant Optimization
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