Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics

Algorithms

Mercedes Carnero¹  José L. Hernández¹ 

[1] ¹Dpto. de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de Río Cuarto, Campus Universitario, (5800) Río Cuarto, Argentina ²Planta Piloto de Ingeniería Química (UNS-CONICET), Camino La Carrindanga Km 7, (8000) Bahía Blanca, Argentina

关键词: Sensor location;    Stochastic optimization;    Tabu search;    Scatter search;    Population based incremental learning algorithms;   

DOI  :  10.3390/a2010259

来源: mdpi

【 摘 要 】

In this work the optimal design of sensor networks for chemical plants is addressed using stochastic optimization strategies. The problem consists in selecting the type, number and location of new sensors that provide the required quantity and quality of process information. Ad-hoc strategies based on Tabu Search, Scatter Search and Population Based Incremental Learning Algorithms are proposed. Regarding Tabu Search, the intensification and diversification capabilities of the technique are enhanced using Path Relinking. The strategies are applied for solving minimum cost design problems subject to quality constraints on variable estimates, and their performances are compared.

【 授权许可】

CC BY   
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.