Determination of Multi-Objective Optimization Control Variable Domain Value in Coordinated Control System of Coal-Fired Unit

2011 ◽  
Vol 282-283 ◽  
pp. 726-730
Author(s):  
Jun Jie Gu ◽  
Zhi Yang ◽  
Yan Ling Ren
Keyword(s):  
Control System ◽  
Fitness Function ◽  
Control Variable ◽  
Physical Significance ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Control Value ◽  
Optimization Control ◽  
System Variables

The accuracy of the variables variation scope in fitness function of the multi-objective optimization have an important influence to multi-objective optimization results. Take a 300 MW coal-fired unit as an example, according to the system mechanism builds a boiler-turbine dynamic model. And put forward a method, in this paper, which is using the iteration way and observing its physical significance to determine control system variables scope. The simplified model uses fuel value, turbine value and feedwater value as the inputs, and uses power, feedwater flow and absorbed heat of water wall as the outputs, to get the boundary of the pressure and the control value of the inputs during 50%-100% load.

scholarly journals Modelling and Multi-Objective Optimization of the Sulphur Dioxide Oxidation Process

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1072
Author(s):  
Mohammad Reza Zaker ◽  
Clémence Fauteux-Lefebvre ◽  
Jules Thibault
Keyword(s):  
Sulphuric Acid ◽  
Sulphur Dioxide ◽  
Variable Number ◽  
Inlet Temperature ◽  
Multi Objective Optimization ◽  
Absorption Column ◽  
Maximum Reaction Rate ◽  
Multi Objective ◽  
Maximum Reaction

Sulphuric acid (H2SO4) is one of the most produced chemicals in the world. The critical step of the sulphuric acid production is the oxidation of sulphur dioxide (SO2) to sulphur trioxide (SO3) which takes place in a multi catalytic bed reactor. In this study, a representative kinetic rate equation was rigorously selected to develop a mathematical model to perform the multi-objective optimization (MOO) of the reactor. The objectives of the MOO were the SO2 conversion, SO3 productivity, and catalyst weight, whereas the decisions variables were the inlet temperature and the length of each catalytic bed. MOO studies were performed for various design scenarios involving a variable number of catalytic beds and different reactor configurations. The MOO process was mainly comprised of two steps: (1) the determination of Pareto domain via the determination a large number of non-dominated solutions, and (2) the ranking of the Pareto-optimal solutions based on preferences of a decision maker. Results show that a reactor comprised of four catalytic beds with an intermediate absorption column provides higher SO2 conversion, marginally superior to four catalytic beds without an intermediate SO3 absorption column. Both scenarios are close to the ideal optimum, where the reactor temperature would be adjusted to always be at the maximum reaction rate. Results clearly highlight the compromise existing between conversion, productivity and catalyst weight.

Design of asymmetric space optical remote sensor active thermal control system by multi-objective optimization

2016 ◽  
Vol 9 (4) ◽  
pp. 463-471
Author(s):  
张帆 ZHANG Fan ◽  
李景林 LI Jing-lin ◽  
孙斌 SUN Bin ◽  
张军 ZHANG Jun ◽  
王书新 WANG Shu-xin
Keyword(s):  
Control System ◽  
Thermal Control ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Thermal Control System ◽  
Remote Sensor

Cooperative Genetic Multi-Objective Optimization Algorithm and Application

2012 ◽  
Vol 220-223 ◽  
pp. 2814-2817
Author(s):  
Li Gao ◽  
Dan Kong
Keyword(s):  
Optimization Algorithm ◽  
Pareto Front ◽  
Fitness Function ◽  
Multi Objective Optimization ◽  
Basic Algorithm ◽  
Multi Objective ◽  
Complicated System ◽  
Present Solution

It is very difficult to find out the best solution for some complicated system problems frequently appear. These problems are mostly of multi-objective. The present solution, however, is short of communication. Based on CO, one of MDO method, this paper gives a new simple kind of multi-objective framework, which will be suitable to multi-subject problems. It can not only organize each disciplinary effectively, but gives the inter-influence between disciplinaries by fitness function as well. Meanwhile, the perfect NSGAⅡ is used as be the basic algorithm, prematurity can be avoided and Pareto front with good distribution is obtained. Micro machined accelerometer example validates the correctness of the framework.

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