Reverse design device and method of gasification process

Abstract

A design method for a gasification process, comprising the steps of: determining raw material characteristics and design requirements, the design requirements being the range of design target values; according to the raw material characteristics and design requirements, giving assumed values ​​of design variables, using the The initial value of the design variable is obtained by calculating the above assumed value and empirical formula; according to the obtained initial value of the design variable, the optimal range of the design variable of the reverse design is determined; the individuals in the obtained initial population are sorted by the non-dominated fuzzy sorting method; The pedigree clustering method with fuzzy target weight coefficients clusters all non-dominated individuals, and finally uses the non-dominated fuzzy sorting method to sort different classes to obtain the design variables and design target intervals that best meet the design requirements. Reverse design has broader requirements for design goals, more comprehensive design variables, clearer direction in the design process, better design results in line with design requirements, greatly reduced workload, and does not require designers to have rich experience.

Description

technical field [0001] The invention relates to the field of gasification process design, in particular to a reverse design device and method for a gasification process. Background technique [0002] Gasification process design includes three main contents: design variables, design objectives and design methods. Design method is the core content of gasification process design, and traditional design methods can be roughly divided into two types: empirical method and trial and error method. [0003] The empirical method is the most commonly used method. It mainly assumes relevant parameters based on the experience of the designer and designs with relevant empirical formulas. Due to the complexity of the gasification process design problem, this method is difficult to consider multiple design objectives at the same time, and generally only The structural parameters of the gasifier can be designed, but the operating parameters cannot be designed. [0004] The trial and error ...

AI Technical Summary

Problems solved by technology

In the multi-objective genetic algorithm (MOGA), the fitness function and the sorting rules among population individuals are the core of the algorithm. The fitness function establishes the mapping relationship between the optimization variable and the optimization target. For the gasification process, it can correspond to the gasification simulation Model, the ordering among population individuals is the ordering of design schemes (including design variables and design objectives). Designers have different requirements and emphasis on multiple design objectives of the gasification process, so it corresponds to the ordering of population individuals in MOGA. Sorting rules are difficult to determine with a unified standard

The existing problems and limitations when the existing gasification simulation model is used in the preliminary design of the gasification process, and the determination of the individual ranking method in MOGA are the main problems in the application of intelligent optimization algorithms and the design of the gasification process

[0006] In the existing gasification process design method, multiple sets of design schemes obtained are independent of each other. Which set of design schemes to choose is a problem that designers must face. At the same time, during the production and operation of gasification equipment, production technology and equipment Due to the influence of performance, it is difficult for gasification equipment to be produced and operated accurately according to the design parameters. Therefore, it is necessary to consider the design margin in the process of gasification process design

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