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.
作者
袁鹏涛
王然风
付翔
YUAN Pengtao;WANG Ranfeng;FU Xiang(College of Mining Engineering,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《工矿自动化》
北大核心
2019年第7期43-47,共5页
Journal Of Mine Automation
基金
山西省科技计划研究项目(201801D221358)
关键词
选煤厂
配煤入选
原煤配比
精煤灰分
精煤硫分
最小二乘支持向量机
粒子群优化算法
coal preparation plant
blended coal preparation
raw coal ratio
ash content of clean coal
sulfur content of clean coal
least squares support vector machine
particle swarm optimization algorithm
