Neural Networks for Energy Optimization of Production Processes in Small and Medium Sized Enterprises

Martina Willenbacher (), Volker Wohlgemuth and Lisa Risch
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Martina Willenbacher: Leuphana University Luneburg
Volker Wohlgemuth: HTW Berlin University of Applied Sciences
Lisa Risch: HTW Berlin University of Applied Sciences

A chapter in Advances and New Trends in Environmental Informatics, 2023, pp 129-145 from Springer

Abstract: Abstract Due to the highly dynamic development processes of manufacturing companies (economic, demographic, sociological, ecological-biological processes), there are high requirements to find scientific answers to environmentally specific questions, considering the profitability and ensuring ongoing operation, and to integrate the developed models to improve the efficiency of the use of materials for energy reduction into the process flow. Decision-making is thus hampered on the one hand by the achievement of solutions in shortened innovation and production cycles and on the other hand by the complexity of the systems and processes of the environmental sector. Furthermore, there are often organizational obstacles and personnel difficulties in the introduction of intelligent algorithms in SMEs. This article describes the conception and development of an artificial neural network for the optimization of production processes regarding the reduction of energy under the aspect of quality assurance for manufacturing SMEs. It describes the development and implementation of the model for the analysis and adaptation of parameter settings to machines in the production process, which determines the ideal configuration to reduce energy consumption and improve quality. In the test of the model on four machines of a plastic-producing SME, it was proven that a total annual energy saving of 50,000 kWh can be achieved.

Keywords: Artificial neural networks; Environmental informatics; Resource efficiency (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-18311-9_8

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DOI: 10.1007/978-3-031-18311-9_8

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