Methodology for ranking controllable parameters to enhance operation of a steam generator with a combined Artificial Neural Network and Design of Experiments approach

• Published in: Energy and AI Vol. 3; p. 100040
• Main Authors: Vieira, Lara Werncke, Marques, Augusto Delavald, Schneider, Paulo Smith, José da Silva Neto, Antônio, Viana, Felipe Antonio Chegury, Abdel-jawad, Madhat, Hunt, Julian David, Siluk, Julio Cezar Mairesse
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2021; Elsevier
• Subjects: Artificial Neural Network; Coal-fired power plant; Design of Experiments; Response surface methodology; Steam generator; Coal-fired power plant; Design of Experiments; Response surface methodology; Artificial Neural Network; Steam generator
• ISSN: 2666-5468; 2666-5468
• DOI: 10.1016/j.egyai.2020.100040

•Modeling of an existing coal-fired power plant with 360 MW in Brazil using real data.•A combined approach of power plant design with artificial neural networks (ANN).•Identification of the most relevant process parameters of the steam generator.•Two Design of Experiment models are applied to compare the performance.•Definition of the best operating ranges using Response Surface Methodology (RSM). [Display omitted] The operation of complex systems can drift away from the initial design conditions, due to environmental conditions, equipment wear or specific restrictions. Steam generators are complex equipment and their proper operation relies on the identification of their most relevant parameters. An approach to rank the operational parameters of a subcritical steam generator of an actual 360 MW power plant is presented. An Artificial Neural Network - ANN delivers a model to estimate the steam generator efficiency, electric power generation and flue gas outlet temperature as a function of seven input parameters. The ANN is trained with a two-year long database, with training errors of 0.2015 and 0.2741 (mean absolute and square error) and validation errors of 0.32% and 2.350 (mean percent and square error). That ANN model is explored by means of a combination of situations proposed by a Design of Experiment - DoE approach. All seven controlled parameters showed to be relevant to express both steam generator efficiency and electric power generation, while primary air flow rate and speed of the dynamic classifier can be neglected to calculate flue gas temperature as they are not statistically significant. DoE also shows the prominence of the primary air pressure in respect to the steam generator efficiency, electric power generation and the coal mass flow rate for the calculation of the flue gas outlet temperature. The ANN and DoE combined methodology shows to be promising to enhance complex system efficiency and helpful whenever a biased behavior must be brought back to stable operation.