A fuzzy temperature control method for waste incinerators using case-based reasoning and extraction rules

Abstract

The invention relates to a garbage incinerator temperature fuzzy control method using case reasoning to extract rules, and relates to the technical field of urban solid garbage incinerator temperature control. The garbage incinerator temperature fuzzy control method using case reasoning to extract rules includes the following steps: 1) using a similarity assessment method to cluster the similar data for the incinerator temperature deviation and rate of change, and the historical data output from a controller; 2) according to the maximum membership rule, generating an initial rule base; 3) using the similarity assessment method to delete the redundancy rule so as to implement reduction of the rule base; 4) calculating the similarity between the target rule and each rule in the reduction rule base to retrieve the rules; 5) according to the nearest neighbor method, reusing the rules so as to form a fuzzy control rule base; and 6) realizing stable and accurate control of the incinerator temperature through a temperature controller.

Description

technical field [0001] The invention relates to the technical field of temperature control of municipal solid waste incinerators, more specifically, the invention is a method for temperature fuzzy control of waste incinerators using case reasoning and extraction rules. Background technique [0002] The waste incineration process has the characteristics of multi-variable, strong coupling, nonlinear, large inertia, and large hysteresis. Traditional control methods are difficult to achieve stable and accurate control of the furnace temperature, which may cause the concentration of dioxins in the flue gas after incineration to exceed the standard. Cause serious damage to people and the environment. Therefore, stable and accurate furnace temperature control has important practical significance for reducing emissions. [0003] Fuzzy control is a control method with fuzzy control rules as the core. Compared with the traditional proportional-integral-differential (PID) control meth...

AI Technical Summary

Problems solved by technology

However, due to some inherent defects of these methods, their application is limited. For example, although the neural network can indirectly extract rules according to the sample data, it has high requirements for training samples and a long training time, which cannot meet those requirements. Higher real-time process requirements; the rules extracted from expert experience are often only applicable to relatively simple control system design, and when faced with multi-variable systems or too many fuzzy sets, the rationality of the rules is difficult to be satisfied; When the genetic algorithm extracts rules, it needs repeated training, which leads to low efficiency and is easy to fall into local optimum; although the Wang-Mendel algorithm is simple and practical, it does not have the ability of learning and reasoning, and its adaptability and robustness are poor in application

Based on the above, the process of extracting control rules by these methods has the disadvantage of a large amount of calculation, and the extracted rules have limitations of poor rationality. It is difficult to realize the occasions with high control requirements, and it is difficult to promote and apply in reality

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