Industrial Symbiosis (IS) is an emerging business tool with a systemic and collaborative approach to optimize and close cycles of materials and energy by identifying synergies and fostering cross-sectoral cooperation among economic actors. The major facilitator of revealing IS opportunities for organizations is both analyzing the status quo with quantitative methods and connecting the supply and demand of the entities involved through an adequate Information Communication Technology (ICT) solution. This study analyzed the extant body of literature and the corresponding ICT tools of IS in order to design a preliminary concept of an Information Technology (IT) supported IS tool that supports the identification and assessment of IS potentials, providing more transparency among market players and proposing potential cooperation partners according to selectable criteria (e.g. geographical radius, material properties, material quality, purchase quantity, delivery period), bringing synergy partners together.
In manufacturing companies, especially in SMEs, the optimization of processes in terms of resource consumption, waste minimization, and pollutant emissions is becoming increasingly important. Another important driver is digitalization and the associated increase in the volume of data. These data, from a multitude of devices and systems, offer enormous potential, which increases the need for intelligent, dynamic analysis models even in smaller companies. This article presents the results of an investigation into whether and to what extent machine learning processes can contribute to optimizing energy consumption and reducing incorrectly produced plastic parts in plastic processing SMEs. For this purpose, the machine data were recorded in a plastics-producing company for the automotive industry and analyzed with regard to the material and energy flows. Machine learning methods were used to train these data in order to uncover optimization potential. Another problem that was addressed in the project was the analysis of manufacturing processes characterized by strong non-linearities and time-invariant behavior with Big Data methods and self-learning controls. Machine learning is suitable for this if sufficient training data are available. Due to the high material throughput in the production of the SMEs’ plastic parts, these requirements for the development of suitable learning methods were met. In response to the increasing importance of current information technologies in industrial production processes, the project aimed to use these technologies for sustainable digitalization in order to reduce the industry’s environmental impact and increase efficiency.
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