Optimized waste heat utilization in the steel industry with industrial heat pump and low-temperature distribution system
The research project by Anton Beck and his colleagues at the Austrian Institute of Technology aimed at assessing how feasible, both technically and economically, the use of such waste heat sources would be in the steel industry.
Tests were conducted in four facilities using mathematical design optimization to find the best way to implement a steam-driven low-temperature distribution system for more efficiency and more economic advantages.
Future costs of energy and industrial heat pumps were approximately calculated using three possible scenarios. The results show that using direct recovery heat technology the waste heat source can be used to reduce the steam output between 20 and 80%. By using an industrial heat pump it is possible to increase the amount of waste heat used up by 30 to 100%. With the inter-plant low-temperature distribution system it is possible to have a 100% increase. The payback time is estimated between 1 and 4 years.
In "Optimized waste heat utilization in the steel industry with industrial heat pump and low-temperature distribution system", opportunities for the substitution of mostly natural gas fired steam supply in steel processing industry are analyzed. Heat recovery from flue gas and other waste heat sources is evaluated using a systematic approach including pinch analysis and mathematical optimization. A basic analysis shows potentials for direct heat recovery and heat pump integration. The analysis further shows that the remaining surplus of waste heat from one of the facilities could be transferred to the other facilities. A mathematical design optimization model including optimal heat-exchanger network synthesis, heat pump integration and the implementation of a steam driven low temperature distribution system is used to identify economically feasible efficiency measures. Possible future development of costs for energy and industrial heat pumps were considered through three different scenarios with parameter variations as input for the following optimization.
The full paper is available here.
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