随着我国采煤机械化程度的提高和开采深度的增加， 煤炭开采过程中的质量信息化 管理程度也要随之提升。对于粗放型煤炭行业中的煤化工企业来说，入厂原煤的煤炭质 量管理涉及不同地区的不同管理部门，纯人工完成对煤质数据的处理，已经无法满足新 时期煤炭企业的发展需要。 本文首先分析气化用煤煤质管理的整体业务流程， 重点研究气化用煤煤质管理涉及 到的煤层煤，过程煤及入厂原煤等煤种的煤质信息化管理过程，同时完成煤化工基地气 化用供应原料煤的煤炭质量特性需求及煤质管理信息化目标研究。 其次，在分析入厂原煤煤质管理流程的基础上，分别构建基于回归理论的一元线性 回归方法和 BP 神经网络的预测模型，分析对比两种预测方法各自的特点。接着在 MATLAB 编程环境中对基于一元线性回归理论的预测模型求解，并通过煤层煤质中的 硫分，灰分，发热量指标的实验数据对两种预测方法的结果比较和分析。 最后，采用开源框架 Structs 2.0，Spring 3.0，MyBaties 和 FreeMarker 技术，设计并 实现基于浏览器服务器的气化用煤煤炭质量信息化管理系统，并采用 Java 与 MATLAB 混合编程技术将非线性的 BP 神经网络预测方法应用到系统中。 研究成果应用在某煤炭企业煤化工基地气化用煤煤质管理业务流程中，结果表明： 采用基于 SSM 框架的信息化技术实现气化用煤煤质管理过程，解决入厂原煤数据信息 共享不及时问题，采用非线性煤质预测方法实现煤质预测预报数据，降低了气化设备因 煤质不明确而产生的故障率，很好的满足煤化工基地管理层对煤质数据信息的需求。有 效的提高了入厂原煤质量管理精确度以及煤化工企业的经济效益。

With the improvement of China's coal mining mechanization degree and the increase of mining depth， the quality information management in the process of coal mining level will also increase. For extensive coal chemical enterprise in the coal industry， the incoming raw coal of coal quality management involved in different parts of the different management departments，purely human complete handling of coal quality data，has been unable to meet the needs of the development of coal enterprises in the new period. This paper analyzed the coal gasification with coal quality management in the whole business process， focuses on gasification coal seam with coal and coal quality management involves， process and incoming raw coal， coal quality information management process， at the same time complete the coal chemical industry base of coal quality characteristics of coal gasification in supply of raw materials demand and coal quality management informatization research objectives. Secondly， on the basis of analyzing the incoming raw coal of coal quality management process， build unary linear regression method based on the theory of the regression and BP neural network prediction model， analyzed the characteristics of two kinds of prediction methods. Then in the MATLAB programming environment based on the theory of unary linear regression prediction model， and through the sulfur content in coal seam coal， ash content， calorific value index of the experimental data for comparison and analysis of the results of two kinds of forecasting methods. Finally， using the user-defined Structs open source framework 2.0， Spring 3.0， MyBatis and FreeMarker technology， designed and implemented based on the browser server coal gasification and coal quality information management system， and USES the Java mixed with the MATLAB programming technology， the application of the BP neural network forecasting method of nonlinear into the system. Research results of application in a coal chemical industry base of gasification coal enterprises coal quality management business process， the results show that the gasification is realized by using information technology based on the framework of SSM coal quality management process， solve the incoming raw coal quality information sharing degree is not enough ， the coal is realized by using the nonlinear prediction method of coal quality prediction data， good to meet demand for management information system of coal chemical industry base management level， reduces the gasification equipment failure rate due to coal quality is not clear. Effectively improve the precision incoming raw coal quality management and the economic benefits of coal chemical industry enterprises