Real-time intelligent fuel system for water transport coal-fired power plant

Abstract

The invention discloses a real-time intelligent fuel system for a water transport coal-fired power plant. The hardware equipment of the system includes a bucket wheel stacker-reclaimer automatic control system, a real-time coal check system, a coal temperature detecting device, a server, and the like; the hardware equipment of the system includes a system platform, an intelligent coal blending combustion system, a fuel intelligent dispatching management system, a fuel intelligent operation management system, a boiler real-time operation management system, a data analysis system and the like. Under the framework of an intelligent power plant, the data gaps of the whole process of fuel management are bridged through the fully automatic bucket wheel capable of accurate three-dimension positioning; intelligent coal stacking and in-boiler coal intelligent blending and combustion are realized through a combustion characteristic prediction-based intelligent coal blending technology; the automatic correlation of the types and quality of coal which is combusted real time in a boiler can be realized through a coal bunker intelligent layering technology; and an automatic coal blending technology, a plurality of metering devices used in a whole process, and an in-boiler coal type-based sampling technology are adopted to realize the intelligent balanced analysis of the coal quantity and coal quality in the whole process of fuel management.

Description

technical field [0001] The invention relates to an intelligent management and control system for coal-fired thermal power generation, in particular to a real-time intelligent fuel system for a waterborne coal-fired power plant. Background technique [0002] Fuel costs account for 60-70% of the operating costs of coal-fired power plants. Coal combustion has a decisive impact on the safe, economical and environmentally-friendly operation of power plant units and power plant operations. How to effectively and efficiently use coal blending and blending to solve the problems of tight coal supply in thermal power plants, complex and changeable coal types, and poor operating performance, and to improve the operation and operating efficiency of power plants, is a major domestic power generation group that has been studying and solving topic. [0003] Due to the lack of application technology and management means, coal-fired power plants have adopted extensive fuel management method...

AI Technical Summary

Problems solved by technology

[0004] Insufficient automation and intelligence of coal conveying equipment: The coal conveying system of the power plant has built a coal conveying program control, which realizes the remote control function for the coal conveying belt and auxiliary equipment, but in terms of coal combustion control, the key equipment, the bucket of the coal yard Wheel stackers and reclaimers are still operated manually or semi-automatically, which makes it impossible to upload real-time data of many stacking and reclaiming coal operations, and operation instructions can only be transmitted and executed manually

[0005] Coal-fired management and control platform is not unified: the main and auxiliary machines of the power plant have basically realized the unified monitoring platform of SIS, but for coal-fired management, due to the low degree of automation and intelligence of the above-mentioned main coal-handling equipment, coal-fired related It is difficult to realize unified real-time management of data. Coal transportation program-controlled data, coal quality data, and coal yard inventory data belong to different management platforms, which cannot be shared.

The management and control of operation scheduling is also mainly manual, and it is impossible to realize real scheduling intelligence and automation

[0006] The coal blending and blending process is simple: due to the lack of a unified coal combustion data platform, effective real-time coal combustion data cannot be obtained, and the coal blending and blending system of power plants is mainly based on manual experience, and a few have realized the "coal blending and blending system" The power plant realizes simple coal blending and blending combustion, and predicts the blended coal quality through the simple weighted average of coal quality indicators, but there is no scientific prediction method for the combustion characteristics of the blended coal, so the actual combustion effect of the boiler will inevitably be affected. Unable to maximize economical

[0007] Insufficient utilization of coal combustion data: Due to the above situation, it is difficult for power plants to exert real effectiveness and real-time performance in the processing and utilization of coal combustion data

In general, coal combustion data can only be used for post-event evaluation and analysis, and its effectiveness requires a lot of manpower. It cannot be used for pre-prediction and analysis, and effective real-time guidance cannot be realized for power generation production processes such as coal blending and blending decisions.

In a small number of power plants that have implemented the "coal blending and blending system", the coal type, coal quantity, coal quality and other data in the coal yard are mainly entered or imported manually. process, and the process of entering data is prone to errors

[0008] Lack of economic evaluation of fuel management: Since fuel mainly relies on manual management, various data lack unified and effective mining and analysis

The loss of coal during storage in the coal yard, including the loss of coal volume and calorific value, due to the lack of monitoring and analysis methods, its economic loss is difficult to assess

At the same time, in the combination of various coal blending and coal burning, the coal consumption of power generation, plant power consumption, coal transportation energy consumption, environmental protection costs, procurement costs, transportation costs, fuel consumption, etc. of various coal blending and burning schemes under different loads Comprehensive economic assessments are difficult to achieve due to lack of data or difficulty in aggregating data

Such factory-level business decision-making lacks strong technical support

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