基于实时变量的重介质分选过程参数预测研究

项目名称： 基于实时变量的重介质分选过程参数预测研究

项目编号： No.51304195

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 矿业工程

项目作者： 王章国

作者单位： 中国矿业大学

项目金额： 25万元

中文摘要： 重介选煤是我国的主要选煤方法，其分选效率的高低对选煤生产影响巨大，目前由于介质密度的控制不能随入洗原煤煤质同步变化，控制滞后问题较突出，导致分选精度和效率较低。本项目研究根据入洗原煤煤质变化实时预测分选密度等工艺参数的工作机理和算法，并建立相应数学模型，提高重介分选过程的控制水平和分选效率。 通过在传统预测方法中引入实时变量，将在线测灰仪测量的灰分通过算法校准，作为可以代替化验灰分的指标，以此为基准预测原煤密度组成和产品密度组成，寻找可实时拟合可选性曲线和分配曲线的拟合模型和最优拟合算法，根据要求的产品指标预测分选密度、产品产率、数量效率等工艺参数，建立预测模型并通过实验对其进行实验验证和优化，为重介选煤的实时精确控制提供参考依据。

中文关键词： 重介质分选；选煤；工艺参数；实时预测；参数优化

英文摘要： Dense Medium Coal Separation acts as the main coal preparation method in China and the level of separation efficiency has a crucial effect on coal preparation production. At present, the control lag problem is prominent because the control of medium density cannot change synchronously with the feed raw coal quality, leading to a low separation accuracy and efficiency. The project researches on principles and algorithms of real-time prediction of process parameter such as separation density according to changes of feed raw coal quality, building homologous mathematical models, and improving controlling level of dense medium separation process and separation efficiency. On condition that real-time variables be introduced into traditional prediction methods, the ash data that have been tested by online ash testing instrument can repalce the data be obtained through testing in laboratory after being corrected, then act as the basic data to pridict density compositions of raw coal and products and to find the optimal mathematical models and algorithms that can fit washability curves and distribution curve. As a result, process parameters such as separation density, product yield and quantity efficiency can be predicted and can meet the requirements. Besides, prediction models can be built, verified and optimized expe

英文关键词： dense medium separation；coal prearation；process parameters；real-time forecast；parameter optimization