Coal Bin Visualization System Based On NIRS-XRF Coal Quality Online Analysis

Quickly and directly obtaining the heat content and industrial index of incoming coal is of great significance for coal allocation guidance and blending optimization in thermal power plants.In the current situation of domestic coal supply shortage,power plants are generally mixed with different coal types,once the coal quality into the furnace and boiler design coal quality does not match,related problems such as causing boiler power shortage,increased coal consumption for power generation,increased pollutant emissions and other problems will arise.At the same time,power plants still generally use the traditional timeconsuming laboratory method,which is unable to understand the coal quality distribution of incoming coal in the coal bin in a timely and intuitive manner,making it difficult to adjust boiler operating parameters in a timely manner.Therefore,thermal power plants are in urgent need of new technologies for fast and real-time coal quality inspection and coal bin visualization.In this paper,the research and development of a coal bin visualization system based on near infrared spectroscopy(NIRS)-X-ray fluorescence spectroscopy(XRF)for online coal quality analysis is carried out.In this paper,the NIRS-XRF online coal quality analysis technology can not only analyze the organic groups in coal using NIRS stably,but also analyze the inorganic ash-forming elements in coal using XRF stably,so that the incoming and outgoing coal quality information can be predicted quickly and accurately.Based on the real-time incoming coal quality data,the coal bin visualization system has been developed to display the dynamic spatial distribution of the industrial indicators of the coal in the coal bins in graphical and color visualization,making it easy for the operator to visualize the current coal quality status of the coal bins and incoming coal.By displaying the fluctuation of incoming coal quality in real time,operators can quickly identify and solve coal quality abnormalities and adjust the front-end coal blending parameters accordingly to improve boiler power generation efficiency and achieve efficient use of coal resources.In addition,the system is equipped with historical query and statistical functions,which can optimize the coal quality management strategy of the enterprise by comparing the coal quality data and the change trend of incoming coal quality in different periods.The thesis is divided into four main sections as follows:1.Introduces the importance of coal in power production,emphasizes the diversity of coal types in China,the impact of coal quality changes on boiler combustion and the urgency of optimizing coal utilization,and reviews the current status of domestic and international research on online coal quality detection,coal bin visualization and front and back-end frameworks.2.Introduces the NIRS-XRF coal quality analysis experimental setup,the sample preparation method and the calibration of dual spectra.3.Introduces the predictive modelling method of NIRS-XRF coal quality analysis,including the pre-processing of spectra,modelling of coal calorific value and the model evaluation method.4.The overall architecture of the coal bin visualization system is described,and the method of constructing the coal bin visualization system and supporting database based on the NIRS-XRF coal quality online analysis data is proposed,and the test results of the system are evaluated.The innovations of this thesis are:A coal bin visualization system based on NIRS-XRF coal quality online analysis data is developed using Spring Boot and Vue,with real-time dynamic display of images of the spatial distribution of various coal industrial indicators in the coal bin and overrun warning functions,which helps to guide the optimal adjustment of coal blending and boiler combustion processes to improve coal utilization.In particular,the online coal quality analysis adopts a dual-spectrum method,using NIRS and XRF to analyze organic groups and inorganic ash-forming elements in coal in a highly stable manner to obtain accurate information on incoming coal quality.