Advances in industrial heat recovery and heat pump integration : steady-state and time-resolved approaches

Decarbonizing the industrial sector requires efficient use and electrification of process heat. Industrial heat pumps offer an efficient way to electrify process heat of temperatures up to 450 °C depending on the process and economic boundary conditions. However, mere substitution of conventional boiler systems with heat pumps is non trivial, as the efficiency of a heat pump strongly depends on the temperature levels it operates between and consequently its integration point. Conventional heat pump integration provides thermodynamic guidance, though it does not assess economic- or ecological optimal integration. Furthermore, the existing methods typically consider static or semi-static process data, although most industrial processes experience temperature and heat load fluctuations in the process streams. The current thesis explores optimal heat pump integration and the incorporation of time-resolved process data in Process Integration methodologies. To establish the foundations of heat pump integration, an analytical equation for the optimal heat pump heat sink temperature is derived. The equation reveals key parameters influencing heat pump integration and their respective impact. While the analytical equation determines the optimal heat pump integration for stationary process data, real world heat pump integration is influenced by process fluctuations. These fluctuations can impact heat transfer, affecting the efficiency of both heat recovery and heat pumps. To account for process fluctuations in heat exchanger network design, hourly process data is clustered into a representative dataset. Consequently, a heat exchanger network is designed and evaluated through dynamic simulation. An annual heat recovery benchmark, derived from hourly heat recovery potentials, serves as a reference. For heat pump integration with time-resolved process data, a method utilizing mathematical programming is introduced. To quantify the benefits, the method is put into comparison with conventional approaches as the Time-Average-Model. The method was further extended to assess the impact and requirement for heat pump load flexibility by introducing a reduced-order part load heat pump model. The analytical equation revealed that the electricity-to-reference fuel price ratio is the most influential parameter for heat pump adoption in industry, thus offering valuable insight for policy development. Despite the added complexity of time-resolved process data, the work demonstrated that accounting for hourly process demands provides significant economic and environmental benefits while improving technical feasibility. This highlights the potential of leveraging time-resolved data in process design to improve both economic performance and decarbonization.