首页|New Findings in Machine Learning Described from Universidad de Oriente (Machine learning regression algorithms to predict emissions from steam boilers)
New Findings in Machine Learning Described from Universidad de Oriente (Machine learning regression algorithms to predict emissions from steam boilers)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on artificial intelligence are discussed in a new report. According to news originating from Santiago de Cuba, Cuba, by NewsRx editors, the research stated, "Currently, the modeling of comple x chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers usin g machine learning (ML) techniques are vital to increase the efficiency with whi ch this equipment operates and reduce the pollution load they contribute to the environment." Our news journalists obtained a quote from the research from Universidad de Orie nte: "This work aims to predict the emissions of CO, CO2, NOx, and the temperatu re of the exhaust gases of industrial boilers from real data. Different ML algor ithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel us ed in around 20 industrial boilers. Each boiler's emission data was collected us ing a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning a pproach using the Gradient Boosting Regression algorithm, showed better performa nce in the predictions made on the test data, outperforming all other models stu died. It was achieved with predicted values showing a mean absolute error of 0.5 1 and a coefficient of determination of 99.80%. Different regressio n models (DNN, MLR, RFR, GBR) were compared to select the most optimal."
Universidad de OrienteSantiago de CubaCubaAlgorithmsCyborgsEmerging TechnologiesMachine Learning
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