| Autor: |
Miguel Caldas, Luisa Caldas, Viriato Semiao |
| Jazyk: |
angličtina |
| Rok vydání: |
2005 |
| Předmět: |
|
| Zdroj: |
Journal of Systemics, Cybernetics and Informatics, Vol 3, Iss 2, Pp 69-74 (2005) |
| Druh dokumentu: |
article |
| ISSN: |
1690-4524 |
| Popis: |
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 a 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 vary in composition from shipment to shipment, and from lot to lot. If operating conditions are kept constant this could result in lose of efficiency. This paper presents an application of Genetic Algorithms to optimize the operating parameters of a gasifier processing a given fuel, so that the system achieves maximum efficiency for each particular fuel composition. A Pareto multiobjective optimization method, combined with a Genetic Algorithm, is applied to the simultaneous maximization of two different objective functions: Cold Gas Efficiency and Hydrogen Contents of the syngas. Results show that the optimization method developed is fast and simple enough to be used for on-line adjustment of the gasification operating parameters for each fuel composition and aim of gasification, thus improving overall performance of the industrial process. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
