基于遗传算法的温度传感器布置优化

doi:10.16180/j.cnki.issn1007-7820.2021.09.004

Abstract

Abstract:

The temperature field distribution information reflects the internal operating state of industrial equipment in industrial applications, which helps to formulate control strategies and ensure the safety of industrial equipment operation. In view of the limitation of UDD section temperature field detection of chain grate in the iron and steel smelting equipment, a method is proposed to solve the problem of the optimal position and number of temperature sensor layout. A mathematical model of the UDD section is established using heat transfer theory, and the theoretical temperature field of the UDD section is obtained through computational fluid dynamics. An objective function for evaluating the location of sensor placement is designed and calculated using genetic algorithm to obtain the optimal solution for the number and location of temperature sensors. The effect of the scheme is verified by numerical simulation. The results show that when the radial basis function is used to convert the temperature field, the temperature field after optimization is increased by 30.90% when compared with the average before optimization, and the model hit rate is increased from 99.88% to 99.99 %.

Key words: pellet chain grate, updraft drying section, sensor location optimization, temperature field reconstruction, genetic algorithm, radial basis function

CLC Number:

TP301.6

XIU Xiaobo,LI Boquan,ZHOU Feng. Optimization of Temperature Sensor Location Based on Genetic Algorithm[J].Electronic Science and Technology, 2021, 34(9): 17-23.

Figures/Tables 8

Table 1

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References 23

[1]	Carvalho M M O, Faria D G, Pérez M G, et al. Review on mathematical models for travelling-grate iron oxide pellet induration furnaces[J]. Energy Procedia, 2017(120):588-595.
[2]	Wong G, Fan X, Gan M, et al. Improvement on the thermal cracking performance of pellets prepared from ultrafine iron ore[J]. Powder Technology, 2018(342):873-879.
[3]	Cavalcanti P P, De Carvalho R M, Andersom S, et al. Surface breakage of fired iron ore pellets by impact[J]. Powder Technology, 2019(342):735-743.
[4]	Elsheikh A H, Guo J, Huang Y, et al. Temperature field sensing of a thin-wall component during machining: Numerical and experimental investigations[J]. International Journal of Heat and Mass Transfer, 2018, 126(8):935-945. doi: 10.1016/j.ijheatmasstransfer.2018.06.006
[5]	Wang Z Z, Kamimoto T, Deguchi Y, et al. Two dimensional temperature measurement characteristics in pulverized coal combustion field by computed tomography-tunable diode laser absorption spectroscopy[J]. Applied Thermal Engineering, 2020(171):115066-115073.
[6]	Yang G M, Fan X H, Chen X L, et al. Intelligent control of grate-kiln-cooler process of iron ore pellets using a combination of expert system approach and takagi-sugeno fuzzy model[J]. Journal of Iron & Steel Research International, 2016, 23(5):434-441.
[7]	余航. 计算机数学建模中改进遗传算法与最小二乘法应用[J]. 电子设计工程, 2020, 28(1):15-18.
	Yu Hang. Application of improved genetic algorithm and least square method in computer mathematical modeling[J]. Electronic Design Engineering, 2020, 28(1):15-18.
[8]	Markovic S, Bryan J L, Turakhanov A, et al. In-situ heavy oil viscosity prediction at high temperatures using low-field NMR relaxometry and nonlinear least squares[J]. Fuel, 2020(260):116328-116339.
[9]	Liu Y, Peng J, Wang Y. Application of partial least squares regression in detecting the important landscape indicators determining urban land surface temperature variation[J]. Landscape Ecology, 2018(33):1133-1145.
[10]	Jia R, Xiong Q, Xu G, et al. A method for two-dimensional temperature field distribution reconstruction[J]. Applied Thermal Engineering, 2017(111):961-967.
[11]	Zhou X Z, Dong C L, Zhao C P, et al. Temperature-field reconstruction algorithm based on reflected sigmoidal radial basis function and QR decomposition[J]. Applied Thermal Engineering, 2020(171):114987-114999.
[12]	Wang X D, Wang G J, Chen H, et al. Real-time temperature field reconstruction of boiler drum based on fuzzy adaptive Kalman filter and order reduction[J]. International Journal of Thermal Sciences, 2017(113):145-153.
[13]	Ma T, Liu Y, Cao C. Neural networks for 3D temperature field reconstruction via acoustic signals[J]. Mechanical Systems and Signal Processing, 2019, 126(3):392-406. doi: 10.1016/j.ymssp.2019.02.037
[14]	Liu Y Z, Chen J, Lü Y Z, et al. Temperature simulation of greenhouse with CFD methods and optimal sensor placement[J]. Sensors & Transducers, 2014(26):40-44.
[15]	Fu Y, Sha M, Wu C, et al. Thermal modeling for a HVAC controlled real-life auditorium[C]. Madrid:International Conference on Distributed Computing Systems,IEEE, 2014.
[16]	Jiang C, Soh Y C, Li H. Sensor and CFD data fusion for airflow field estimation[J]. Applied Thermal Engineering, 2015, 92(5):149-161. doi: 10.1016/j.applthermaleng.2015.09.078
[17]	Jo T, Koo B, Kim H, et al. Effective sensor placement in a steam reformer using gappy proper orthogonal decomposition[J]. Applied Thermal Engineering, 2019, 154(3):419-432. doi: 10.1016/j.applthermaleng.2019.03.089
[18]	李洋. 球团链篦机鼓风干燥段多物理场耦合机理研究[D]. 镇江:江苏大学, 2018.
	Li Yang. Study on multiphysics coupling mechanism in updraft drying section of the pellets grate[D]. Zhenjiang:Jiangsu University, 2018.
[19]	蓝机满. 基于径向基神经网络的粒子群表面缺陷识别算法[J]. 电子科技, 2019, 32(5):92-95.
	Lan Jiman. Particle swarm optimization surface defect recognition algorithm based on radial basis neural network[J]. Electronic Science and Technology, 2019, 32(5):92-95.
[20]	陈强, 刘瑾, 杨海马, 等. 基于改进遗传算法的配电网无功优化研究[J]. 电子科技, 2019, 32(5):11-15.
	Chen Qiang, Liu Jin, Yang Haima, et al. Research on reactive power optimization of power distribution network based on improved genetic algorithm[J]. Electronic Science and Technology, 2019, 32(5):11-15.
[21]	吴延凯, 张伟, 马盈满, 等. 基于GA-PSO融合算法的而自由度PID参数优化[J]. 电子科技, 2019, 32(10):54-59.
	Wu Yankai, Zhang Wei, Ma Yingman, et al. Two degree of freedom PID parameter optimization based on GA-PSO fusion algorithm[J]. Electronic Science and Technology, 2019, 32(10):54-59.
[22]	何兴. 球团矿热态参数指标机理—神经网络混合建模的研究[D]. 沈阳:东北大学, 2011.
	He Xing. Mixed modelling for the thermal parameter of pellet mine based on mechanism-neural networks[D]. Shenyang:Northeastern University, 2011.
[23]	崔频, 王敏. 一种虚拟力导向遗传算法的无线传感器网络优化部署策略[J]. 电子设计工程, 2017, 25(7):87-91.
	Cui Pin, Wang Min. An optimal deployment strategy for wireless sensor networks based on vortual face oriented genetic algorithm[J]. Electronic Design Engineering, 2017, 25(7):87-91.

参数	数值
染色体代表	传感器坐标组合
最大代数	1 000
变异概率	0.05
种群大小	100

Optimization of Temperature Sensor Location Based on Genetic Algorithm

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