当前主流在线煤质检测装置需要进行核射线及其他能束的探测，设备构造复杂，使用和维护成本较高，在我国火电领域普遍未能实现入炉煤质的在线检测。由于煤质波动导致的发电过程控制效率欠佳普遍存在，近十几年来入炉煤质的在线软测量技术开始受到关注和应用。煤质软测量技术以电厂DCS系统联入的分布式测点为依托，从机理分析和数据学习出发驱动煤质的在线辨识，而无需复杂设备的投入，迎合火电生产控制的需求。然而由于燃煤品质参数较多，不同品质参数的软测量技术方法不同，入炉煤质软测量缺乏系统的技术体系论证。论述了入炉煤质参数的软测量技术分类和原理，对煤质在线软测量进行误差分析，最后对未来两类入炉煤质软测量技术的发展应用提出建议。按照技术特点将入炉煤质软测量技术分成基于机理分析的软测量和基于机器学习的软测量两大类。煤质在线软测量技术更易在火电生产中推广应用，针对实际入炉煤质进行实时跟踪，可实现火电生产过程的有效调控。燃煤全品质是多参数体系，基于机理分析的煤质软测量技术，可对制粉、燃烧、传热、做功过程进行分析，建立燃煤品质参数与现场分布式测点间的机理模型，形成不同技术路线;该体系理论依据明确，但分析过程影响因素复杂难以进行精准建模，尤其对于变负荷工况下的入炉煤质辨识难度较大。基于机器学习的煤质软测量技术克服了机理分析过程的困难，其有效应用对于样本的采集处理、智能建模过程有较高要求。

At present，most industrial online coal analyzers utilize intense energy beam like nuclear radiation scanning，the test utilities are complex，the use and maintenance cost is high，so the on-line detection of incoming coal quality is generally not realized in the field of thermal power in China. Due to the coal quality fluctuation control inefficiency of power generation process caused by coal quality fluctuation，and soft measurement technology for coal quality monitoring begins attracting attention. Utilizing distributed-sensors in the DCS system，soft-measurement of fuel quality of coal fed into the boiler are realized through mechanism analysis and data learning，which meets application requirements for coal-fired power generation with few additional investigation cost. However，coal quality has multiple indexes，iden tification of different quality index applies different approaches，the soft sensing of incoming coal is lack of systematic technical system demonstration. In this study，the classification and fundamental basis of soft-measurement for coal quality parameters were summarized，and the error analysis of online soft sensing of coal quality was analysed. At last，suggestions of future technological developments of soft-measurement of coal quality were proposed. According to methodological character，soft measurement technologies for coal quality are divided into two types，one type based on mechanism analysis and the other based on machine learning. Soft-measurement of coal quality is more fitted for power generation application. The real-time tracking of the actual coal quality can realize effective regulation of thermal power production process. Coal quality is constructively multiple system，soft-measurement of coal quality based on mechanism analysis builds fundamental models through process analysis of pulverizing，burning，heat and energy transfer，different technical trends are formed. For such technology，explicit models are offered，but the analysis process is easily effected by multiple factors，it is hard to reach accurate results especially for frequent load regulation condition. Coal quality measurement based on machine learning avoids complex mechanism analysis，while on the other hand，sample data set preparing and intelligent modelling facing high requirements for goal of efficient application.

0 引言

1 燃煤品质参数及软测量技术分类

2 基于机理模型分析法的煤质在线软测量

   2.1 基于机理分析的水分软测量

   2.2 基于机理分析的元素成分软测量

   2.3 基于机理分析的燃煤发热量软测量

   2.4 基于机理分析的灰分软测量

3 基于机器学习的煤质软测量技术

   3.1 机器学习与智能建模

   3.2 技术要点及局限性

4 煤质在线软测量误差分析

   4.1 基于机理分析的煤质软测量误差分析

   4.2 基于机器学习的煤质软测量误差分析

   4.3 煤质在线软测量技术与在线煤质分析仪测量精度比较

5 结语与建议