Energy savings of inter-company heat integration: Tapping potentials with spatial analysis

Industry accounts for approximately 30 % of the final energy demand in Germany. 75 % of this is used to provide heat, of which 65 % is process heat. Thus options to improve the energy efficiency of heat generation in industry are of major relevance for energy policy in Germany. Inter-company heat integration is one option to increase energy efficiency in industry. This refers to integrating the heat supply of companies in close spatial proximity to each other. So far, the potential energy savings in Europe due to inter-company heat integration have only been estimated for the United Kingdom. The topic was a side issue in a study of the potentials for recovering and using waste heat from industry in 2014. This paper addresses the question of how to identify potentials for inter-company heat integration by providing and applying a general framework. First, we discuss how spatial data mining can be used to analyse industrial symbiosis potentials in general. Second, we apply a spatial data mining tool to analyse the co-location patterns of economic sectors based on the European Pollutant Release and Transfer Register (E-PRTR). To do so, we combine the tool to detect co-location patterns with information about the process temperatures typically applied in the affected industry sectors taken from the project 'Datenbasis Energieeffizienz'. This demonstrates how promising constellations of industrial production sites might be identified with regard to inter-company heat integration. Finally, we discuss how energy-saving potentials due to inter-company heat integration could be assessed for regions, combining spatial data mining with heat integration methodologies. Consequently, the paper gives insights into a) how analysing the co-location patterns of economic sectors can be used to identify promising production sites for inter-company heat integration, and b) how the potential energy savings due to inter-company heat integration can be estimated in a structured way for regions. Based on the results of the case study, it can be summarized that promising agglomerations of productions sites can be located geographically by combining spatial co-location mining with publicly available data. Further research is needed to validate the criteria applied for promising agglomerations.