Application of Methods of Artificial Intelligence for Sustainable Production of Manufacturing Companies

Martina Willenbacher (), Christian Kunisch () and Volker Wohlgemuth ()
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Martina Willenbacher: HTW Berlin, Department Engineering-Technology and Life, Environmental Informatics
Christian Kunisch: HTW Berlin, Department Engineering-Technology and Life, Environmental Informatics
Volker Wohlgemuth: HTW Berlin, Department Engineering-Technology and Life, Environmental Informatics

A chapter in From Science to Society, 2018, pp 225-236 from Springer

Abstract: Abstract An energy- and resource-friendly production is an important key performance indicator for industrial companies to work economically and thus remain competitive. For this software systems are necessary for analysis, evaluation, diagnosis and planning. Thanks to intensive research efforts in the field of artificial intelligence (AI) a number of AI based techniques such as machine learning, deep learning and artificial neural networks (ANN) have already been established in industry, business and society. In this paper, we address the problem of energy- and resource efficiency in production processes of manufacturing companies. We present an approach to improve energy- and resource efficiency by methods of AI. We propose an in-progress idea to extend the possibilities of using methods of AI for optimizing material and energy flows. In addition to processing the process data, an integrated database of measures is designed to support sustainable production. The investigations are carried out prototypically using an ANN in combination with fuzzy logic and evolutionary algorithms (EA).

Keywords: Artificial intelligence; Neural network; Machine learning; Energy efficiency; Material- and energy flows (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-319-65687-8_20

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DOI: 10.1007/978-3-319-65687-8_20

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