Efficiency and NOx emission optimization by genetic algorithm of a coal-fired steam generator modeled with artificial neural networks

da Rocha, B.P., de Assis Brasil Weber, N., Smith Schneider, P., Hunt, J. ORCID: https://orcid.org/0000-0002-1840-7277, & Mairesse Siluk, J.C. (2022). Efficiency and NOx emission optimization by genetic algorithm of a coal-fired steam generator modeled with artificial neural networks. Journal of the Brazilian Society of Mechanical Sciences and Engineering 44 (5) e218. 10.1007/s40430-022-03481-3.

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Abstract

High level of efficiency is pursued whenever an energy convertion system is operated, such as steam generators, but emission contents must be kept within legal and reasonable values at the same time. The interdependence of these two factors often limit the system performance, and optimization procedures allow for finding combinations of parameters that may satisfy that anthagonism and help technical teams to operate actual systems. This study aims to find operating points of an actual superheated steam generator that combine high efficiency with low NOx emissions, based on the system data analysis. The novelty presented in the paper comes from the assessment of a power plant placed on a hot, humid and seasonal wheather, witch differs from previous works. The steam generator efficiency and NOx emission are represented by two independent Artificial Neural Networks (ANNs), based on a common database. The influence of the ANNs selected input paremeters are assessed with the aid of Design of Experiment, which points out that 2 out of 10 inputs to efficiency can be removed, but none to NOx emissions. The objective function aims to find the combination of input values that allow to operate the steam generator with the highest efficiency and the lowest NOx emission, covering three weighting combinations: 50 to 50, 75 to 25 and 90 to 10 in percentage of efficiency in respect to NOx emission. The genetic algorithm optimization procedure identified input values that guaranty 97.95% efficiency and 222.28 mg/mN3 of NOx emission based on the reference values of 98% and 220.00 mg/mN3, when assuming the 90 to 10 ponderation.

Item Type:	Article
Research Programs:	Energy, Climate, and Environment (ECE) Energy, Climate, and Environment (ECE) > Sustainable Service Systems (S3)
Depositing User:	Luke Kirwan
Date Deposited:	11 May 2022 09:57
Last Modified:	11 May 2022 09:57
URI:	https://pure.iiasa.ac.at/17998

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