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oapen-20.500.12657-493842021-11-23T14:04:10Z Chapter Machine Learning Models for Industrial Applications Enislay, Ramentol Tomas, Olsson Shaibal, Barua machine learning, prediction, regression methods, maintenance, degradation prediction bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBC Engineering: general More and more industries are aspiring to achieve a successful production using the known artificial intelligence. Machine learning (ML) stands as a powerful tool for making very accurate predictions, concept classification, intelligent control, maintenance predictions, and even fault and anomaly detection in real time. The use of machine learning models in industry means an increase in efficiency: energy savings, human resources efficiency, increase in product quality, decrease in environmental pollution, and many other advantages. In this chapter, we will present two industrial applications of machine learning. In all cases we achieve interesting results that in practice can be translated as an increase in production efficiency. The solutions described cover areas such as prediction of production quality in an oil and gas refinery and predictive maintenance for micro gas turbines. The results of the experiments carried out show the viability of the solutions. 2021-06-02T10:13:42Z 2021-06-02T10:13:42Z 2021 chapter ONIX_20210602_10.5772/intechopen.93043_498 https://library.oapen.org/handle/20.500.12657/49384 eng application/pdf n/a 72763.pdf InTechOpen 10.5772/intechopen.93043 10.5772/intechopen.93043 09f6769d-48ed-467d-b150-4cf2680656a1 H2020-SPIRE-2016 723523 open access
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More and more industries are aspiring to achieve a successful production using the known artificial intelligence. Machine learning (ML) stands as a powerful tool for making very accurate predictions, concept classification, intelligent control, maintenance predictions, and even fault and anomaly detection in real time. The use of machine learning models in industry means an increase in efficiency: energy savings, human resources efficiency, increase in product quality, decrease in environmental pollution, and many other advantages. In this chapter, we will present two industrial applications of machine learning. In all cases we achieve interesting results that in practice can be translated as an increase in production efficiency. The solutions described cover areas such as prediction of production quality in an oil and gas refinery and predictive maintenance for micro gas turbines. The results of the experiments carried out show the viability of the solutions.
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