选煤厂原煤智能配比控制系统设计
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摘要
针对选煤厂配煤入选过程中通过理论计算的原煤定值配比难以满足现场生产要求,而依据人工经验调节原煤配比随机性大、精煤质量难以保证、人工劳动强度大等问题,设计了一种选煤厂原煤智能配比控制系统。利用最小二乘支持向量机建立原煤智能配比预测模型,并采用粒子群优化算法进行模型参数优化。该系统以原煤灰分实测值、原煤硫分实测值、每小时原煤平均入选量、分选密度、精煤灰分实测值、精煤灰分目标值、精煤硫分实测值和精煤硫分目标值作为模型输入变量,经模型计算得出相应的原煤配比预测值;通过胶带秤测量给煤机的给煤量得到原煤配比实测值,并与预测值比较得出原煤配比偏差量;PID控制器根据偏差量控制给煤机的变频器频率,实现原煤配比精准调节。该系统应用后精煤灰分和硫分波动范围明显减小,精煤质量稳定性良好;精煤灰分与灰分目标值差值控制在±0.2%,精煤硫分与硫分目标值差值控制在±0.15%,提高了精煤质量。
It is difficult to meet requirement of field production by theoretically calculating fixed ratio of raw coal in blended coal preparation process of coal preparation plant,and problems such as large randomness,low quality of clean coal and high labor intensity are existed in raw coal ratio adjustment according to manual experience.According to above problems,an intelligent ratio control system for raw coal in coal preparation plant was designed.Least squares support vector machine is used to establish a prediction model of intelligent raw coal ratio,and particle swarm optimization algorithm is adopted to optimize model parameters.The system takes measured ash content value of raw coal,measured sulfur content value of raw coal,average feed amount of raw coal per hour,separation density,measured ash content value of clean coal,ash content target value of clean coal,measured sulfur content value of clean coal and sulfur content target value of clean coal as input variables of the model,so as to obtain predicted value of raw coal ratio.Coal feed amount of coal feeder is measured by belt scale to calculate measured value of raw coal ratio,and deviation of raw coal ratio is obtained by comparing the measured value with the predicted value.PID controller controls frequency converter of the coal feeder according to the deviation amount to achieve accurate adjustment of raw coal ratio.The actual application results show that fluctuation range of ash and sulfur content of clean coal is significantly reduced and quality stability of clean coal is good.Difference between ash content of clean coal and ash content target value is controlled within±0.2%,and difference between sulfur content of clean coal and sulfur content target value is controlled within±0.15%,which verify quality improvement of clean coal.
It is difficult to meet requirement of field production by theoretically calculating fixed ratio of raw coal in blended coal preparation process of coal preparation plant,and problems such as large randomness,low quality of clean coal and high labor intensity are existed in raw coal ratio adjustment according to manual experience.According to above problems,an intelligent ratio control system for raw coal in coal preparation plant was designed.Least squares support vector machine is used to establish a prediction model of intelligent raw coal ratio,and particle swarm optimization algorithm is adopted to optimize model parameters.The system takes measured ash content value of raw coal,measured sulfur content value of raw coal,average feed amount of raw coal per hour,separation density,measured ash content value of clean coal,ash content target value of clean coal,measured sulfur content value of clean coal and sulfur content target value of clean coal as input variables of the model,so as to obtain predicted value of raw coal ratio.Coal feed amount of coal feeder is measured by belt scale to calculate measured value of raw coal ratio,and deviation of raw coal ratio is obtained by comparing the measured value with the predicted value.PID controller controls frequency converter of the coal feeder according to the deviation amount to achieve accurate adjustment of raw coal ratio.The actual application results show that fluctuation range of ash and sulfur content of clean coal is significantly reduced and quality stability of clean coal is good.Difference between ash content of clean coal and ash content target value is controlled within±0.2%,and difference between sulfur content of clean coal and sulfur content target value is controlled within±0.15%,which verify quality improvement of clean coal.
引文
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[14]杨柳,孙金华,冯仲科,等.基于PSO-LSSVM的森林地上生物量估测模型[J].农业机械学报,2016,47(8):273-279.YANG Liu,SUN Jinhua,FENG Zhongke,et al.Estimation model of forest above-ground biomass based on PSO-LSSVM[J].Transactions of the Chinese Society of Agricultural Machinery,2016,47(8):273-279.
[15]彭娟,程健,韩仿仿,等.基于粒子群算法的自动配煤系统多目标优化[J].工矿自动化,2009,35(10):25-28.PENG Juan,CHENG Jian,HAN Fangfang,et al.Multi-target optimization for automatic blending coal system based on PSO algorithm[J].Industry and Mine Automation,2009,35(10):25-28.
[2]程子曌.我国煤炭洗选加工和煤质现状及“十三五”展望[J].煤炭加工与综合利用,2017(5):17-20.
[3]徐虎彪.我国炼焦煤洗选工艺现状与展望[J].选煤技术,2017(6):87-90.XU Hubiao. State-of-the-art and development prospect of China's coking coal cleaning technology[J].Coal Preparation Technology,2017(6):87-90.
[4]郭冬梅.天宏选煤厂丁戊庚组煤配煤入选的探索与应用[J].煤炭工程,2018,50(2):84-86.GUO Dongmei.Application of IV-V-VII group blended coal preparation technology in Tianhong Coal Preparation Plant[J].Coal Engineering,2008,50(2):84-86.
[5]员文娥.屯兰矿选煤厂2号与8号原煤最佳配煤方案研究[J].煤炭科学技术,2013,41(增刊2):409-411.YUAN Wene.Study on the optimum coal blending plan with No.2and No.8coal in Tunlan Mine Coal Preparation Plant[J].Coal Science and Technology,2013,41(S2):409-411.
[6]孙景丹,杜新伟,李哲.基于线性规划法的配煤入选试验[J].洁净煤技术,2014,20(2):1-4.SUN Jingdan,DU Xinwei,LI Zhe.Coal blending experiment based on linear programming[J].Clean Coal Technology,2014,20(2):1-4.
[7]刘敏,闵凡飞.选配煤中心配煤入选提高低值煤经济效益的实践[J].选煤技术,2018(5):83-85.LIU Min,MIN Fanfei.Practice of separation of lowgrade coal through blending for improved economic result at the coal preparation &blending center[J].Coal Preparation Technology,2018(5):83-85.
[8]姜雪,刘思哲,李志强.数字PID控制器在配煤系统中的应用[J].山东科技大学学报(自然科学版),2006,25(1):47-48.JIANG Xue,LIU Sizhe,LI Zhiqiang.Application of digital PID controller in coal-blending system[J].Journal of Shandong University of Science and Technology(Natural Science),2006,25(1):47-48.
[9]王吉刚.基于PID算法的井下自动配煤系统研究[D].青岛:山东科技大学,2012.
[10]刘春梅.基于PID控制器的配煤控制系统设计与实现[J].通信技术,2013,46(2):51-54.LIU Chunmei.Design and implementation of coal blending control system based on PID controller[J].Communications Technology,2013,46(2):51-54.
[11]王安义,郭世坤.基于LS-SVM的矿井巷道场强预测[J].工矿自动化,2014,40(10):36-40.WANG Anyi,GUO Shikun.Prediction of field intensity in mine tunnel based on LS-SVM[J].Industry and Mine Automation,2014,40(10):36-40.
[12]王新,孟玲玲.基于EEMD-LSSVM的超短期负荷预测[J].电力系统保护与控制,2015,43(1):61-66.WANG Xin,MENG Lingling.Ultra-short-term load forecasting based on EEMD-LSSVM[J].Power System Protection and Control,2015,43(1):61-66.
[13]方必武,刘涤尘,王波,等.基于小波变换和改进萤火虫算法优化LSSVM的短期风速预测[J].电力系统保护与控制,2016,44(8):37-43.FANG Biwu,LIU Dichen,WANG Bo,et al.Shortterm wind speed forecasting based on WD-CFALSSVM model[J].Power System Protection and Control,2016,44(8):37-43.
[14]杨柳,孙金华,冯仲科,等.基于PSO-LSSVM的森林地上生物量估测模型[J].农业机械学报,2016,47(8):273-279.YANG Liu,SUN Jinhua,FENG Zhongke,et al.Estimation model of forest above-ground biomass based on PSO-LSSVM[J].Transactions of the Chinese Society of Agricultural Machinery,2016,47(8):273-279.
[15]彭娟,程健,韩仿仿,等.基于粒子群算法的自动配煤系统多目标优化[J].工矿自动化,2009,35(10):25-28.PENG Juan,CHENG Jian,HAN Fangfang,et al.Multi-target optimization for automatic blending coal system based on PSO algorithm[J].Industry and Mine Automation,2009,35(10):25-28.
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