A Novel Application of Evolutionary Computing in Process Systems Engineering

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• 作者：Jessica Andrea Carballido ; Ignacio Ponzoni ; N¨¦lida Beatriz Brignole
• 关键词：Combinatorial Optimization Problem ; PSE ; Process ; Plant Instrumentation Design ; Multi ; Objective Genetic Algorithm ; Observability Analysis
• 刊名：Lecture Notes in Computer Science
• 出版年：2005
• 出版时间：2005
• 年：2005
• 卷：3448
• 期：1
• 页码：p.12
• 全文大小：136 KB

文摘

In this article we present a Multi-Objective Genetic Algorithm for Initialization (MOGAI) that finds a starting sensor configuration for Observability Analysis (OA), this study being a crucial stage in the design and revamp of process-plant instrumentation. The MOGAI is a binary-coded genetic algorithm with a three-objective fitness function based on cost, reliability and observability metrics. MOGAI’s special features are: dynamic adaptive bit-flip mutation and guided generation of the initial population, both giving a special treatment to non-feasible individuals, and an adaptive genotypic convergence criterion to stop the algorithm. The algorithmic behavior was evaluated through the analysis of the mathematical model that represents an ammonia synthesis plant. Its efficacy was assessed by comparing the performance of the OA algorithm with and without MOGAI initialization. The genetic algorithm proved to be advantageous because it led to a significant reduction in the number of iterations required by the OA algorithm.