Characterization and visualization of industrial excess heat for different levels of on-site process heat recovery
Journal article, 2019
Increased utilization of industrial excess heat (or waste heat) can reduce primary energy use and thereby contribute to reaching energy and climate targets. To estimate the potential availability of industrial excess heat, it is necessary to capture the significant heterogeneity of the industrial sector. This requires the development of methodologies based on case study assessments of individual plants, adopting a systematic approach and consistent assumptions. Since the recovery of excess heat for power generation or off-site delivery competes with internal recovery for on-site fuel savings, a well-founded approach should enable a comparison of the excess heat availability at different levels of internal process heat recovery. To determine the best solution for excess heat utilization for a given process, there is a need for easy screening of various options, while considering that some techniques require heat at a constant temperature while others can exploit a nonisothermal heat supply. This paper presents a new tool, the excess heat temperature (XHT) signature, for exploring the potential heat availability and trade-offs for excess heat utilization by weighting the heat according to predefined temperature levels and ranges. A set of reference conditions are defined, and an energy targeting approach is proposed that can be used for characterizing the Theoretical XHT signature, which represents the unavoidable excess heat that can be recovered after maximized internal process heat recovery and ideal integration of a power generation steam cycle. The Theoretical XHT signature is contrasted with the Process Cooling XHT signature, which represents the excess heat that can be recovered given the current design and operation of the process and its utility system. The XHT signature curves provide a consistent representation of the excess heat, enabling comparison between sites and aggregation of results from different case studies.