Authors:
Miguel Caldas
;
Luisa G. Caldas
and
Viriato Semião
Affiliation:
IST, Technical University of Lisbon, Portugal
Keyword(s):
Gasification, Genetic Algorithms, Optimisation
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Computational Intelligence
;
Evolutionary Computing
;
Expert Systems
;
Genetic Algorithms
;
Health Information Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Optimization Algorithms
;
Soft Computing
;
Symbolic Systems
Abstract:
Gasification is a well-known technology that allows for a combustible gas to be obtained from a
carbonaceous fuel by a partial oxidation process (POX). The resulting gas (synthesis gas or syngas) can be
used either as a fuel or as feedstock for chemical production. Recently, gasification has also received a great deal of attention concerning power production possibilities through IGCC process (Integrated Gasification Combined Cycle), which is currently the most environmentally friendly and efficient method for the production of electricity. Gasification allows for low grade fuels, or dirty fuels, to be used in an
environmental acceptable way. Amongst these fuels are wastes from the petrochemical and other industries, which may vary in composition from shipment to shipment, and from lot to lot. If operating conditions are kept constant, this could result in lost of efficiency. This paper presents an application of Genetic Algorithms to optimise the operating parameters of a gasifier p
rocessing a given fuel. Two different objective functions are used: one to be used if hydrogen production is the main goal of gasification; other to be used when power/heat production is the aim of the process. Results show that the optimisation method developed is fast and simple enough to be used for on-line adjustment of the gasification operating parameters, for each fuel composition and gasification aim, thus improving the overall performance of the industrial process.
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