Method and system for predicting bed temperature of circulating fluidized bed municipal solid waste incineration boiler

Abstract

The invention discloses a method and system for predicting the bed temperature of a circulating fluidized bed municipal solid waste incineration boiler. Based on tacit knowledge in operation mechanisms and operation history data of the circulating fluidized bed municipal solid waste incineration boiler, a modeling method is integrated by a Gamma Test algorithm, a PSO (particle swarm optimization) algorithm, a subtractive clustering algorithm and an ANFIS (adaptive neuro-fuzzy inference system) algorithm for predicting a bed temperature of the boiler in real time to avoid from complex mechanism modeling work. The whole modeling process is clear in logic, few in set parameters, high in modeling automation degree and easy to master and popularize. Meanwhile, an excellently trained ANFIS bed temperature diction model can serve control algorithms based on the model to provide help for ACC (automatic combustion control) system actuation of the circulating fluidized bed municipal solid waste incineration boiler.

Description

technical field [0001] The invention relates to the field of energy engineering, in particular to a method and system for predicting the bed temperature of a circulating fluidized bed domestic waste incineration boiler. Background technique [0002] Since waste incineration can well realize volume reduction, volume reduction, harmlessness and resource utilization of waste treatment technology, in the past ten years, under the guidance of relevant national industrial policies, the domestic waste incineration industry has achieved vigorous development. Since the 1990s, a number of domestic scientific research institutions have conducted a large number of in-depth studies on the combustion mechanism of municipal solid waste (MSW) in China, and have mastered the combustion characteristics of mixed collection, high moisture, and complex MSW. Based on my country's experience in the development of coal, coal gangue and other inferior fuel circulating fluidized bed (Circulating Fluid...

AI Technical Summary

Problems solved by technology

However, the design of the ANFIS bed temperature prediction model faces problems such as difficult selection of model input variables, difficulty in confirming the initial structure of the model, and difficulty in determining the size of the training sample.

Too many redundant input variables will increase the complexity of the model on the one hand, and on the other hand will introduce data noise, and even submerge the effective information in the data, degrading the performance of the model; too few input variables will not form a complete bed temperature model. express the model, resulting in underfitting

However, to confirm the initial structure of the ANFIS model, it is necessary to analyze and extract fuzzy rules from a large number of training samples. The complexity and difficulty of the modeling process

Too many training samples will greatly prolong the training time of the model, and may introduce noise data, resulting in overfitting phenomenon; too few training samples, the model cannot obtain sufficient information support, and the generalization performance of the model will decline

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