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

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能源与人工智能(英文) 2021年第1期3卷 9-21页

作者： lara werncke vieira Augusto Delavald Marques Paulo Smith Schneider Antônio Joseda Silva Neto Felipe Antonio Chegury Viana Madhat Abdel-jawad Julian David Hunt Julio Cezar Mairesse Siluk Federal University of Rio Grande do Sul-UFRGS Porto AlegreBrazil State University of Rio de Janeiro-UERJ Nova FriburgoBrazil University of Central Florida OrlandoUCFUnited States Advanced Analysis Australia-AAA PerthAustralia International Institute for Applied Systems Analysis-IIASA ViennaAustria Federal University of Santa Maria-UFSM Santa MariaBrazil

The operation of complex systems can drift away from the initial design conditions,due to environmental condi-tions,equipment wear or specific *** generators are complex equipment and their proper opera-tion relies on the identification of their most relevant *** approach to rank the operational parameters of a subcritical steam generator of an actual 360 MW power plant is *** 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 *** 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 *** 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 *** 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 *** 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.

关键词： Coal-fired power plant Artificial Neural Network Design of Experiments Response surface methodology Steam generator

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Increasing power plant efficiency with clustering methods and Variable Importance Index assessment

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能源与人工智能(英文) 2021年第3期5卷 82-90页

作者： Jéssica Duarte lara werncke vieira Augusto Delavald Marques Paulo Smith Schneider Guilherme Pumi Taiane Schaedler Prass Department of Mechanical Engineering Federal University of Rio Grande do SulPorto AlegreBrazil Graduate Program in Statistics Federal University of Rio Grande do SulPorto AlegreBrazil

Power plant performance can decrease along with its life span,and move away from the design and commissioning *** issues,operational practices,market restrictions,and financial objectives may lead to that behavior,and the knowledge of appropriate actions could support the system to retake its original operational *** paper applies unsupervised machine learning techniques to identify operating patterns based on the power plant’s historical data which leads to the identification of appropriate steam generator efficiency *** selected operational variables are evaluated in respect to their impact on the system performance,quantified by the Variable Importance *** metric is proposed to identify the variables among a much wide set of monitored data whose variation impacts the overall power plant operation,and should be controlled with more *** Component Analysis(PCA)and k-means++clustering techniques are used to identify suitable operational conditions from a one-year-long data set with 27 recorded variables from a steam generator of a 360MW thermal power *** adequate number of clusters is identified by the average Silhouette coefficient and the Variable Importance Index sorts nine variables as the most relevant ones,to finally group recommended settings to achieve the target *** show performance gains in respect to the average historical values of 73.5%and the lowest efficiency condition records of 68%,to the target steam generator efficiency of 76%.

关键词： Thermal power plant performance enhancement Operating patterns identification K-means clustering Principal component analysis Unsupervised machine learning

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