A Computationally Efficient Heat Pump Model for Quick and Reliable Identification of Energy-Efficient Distillation Configurations

Abstract

For the decarbonization of energy-intensive distillation processes, that consume more than 40% of the energy in the chemical and refining industries, the availability of carbon-free electricity incentivizes implementation of energy-efficient heat-pumped distillation configurations. However, a significant impediment towards that goal is the vast search space of heat-pumped configurations, with large differences in energy consumption, and the absence of a method that systematically examines all options, and identifies the energy-efficient and cost-effective alternatives. The compressors in heat pump loops add considerable capital cost, so the challenge has been to perform an optimal tradeoff between the number of compressors and energy consumption. Here, we introduce a tractable and precise heat pump model that can be integrated into optimization frameworks, enabling the exploration of design alternatives and/or process parameters to reliably identify lucrative heat pump-assisted systems. Built on the theoretical foundation of differential Carnot heat pumps, latent heat transformations, and temperature tracking equations, the model can identify key locations for heat pump integration within a process that offers maximum energy savings while supporting an efficient capital cost transition strategy. The accuracy of the model is validated against detailed process simulations. To demonstrate its utility, the model is embedded within an optimization framework to identify the optimal configuration from hundreds of alternatives for separating a four-component aromatic mixture using a vapor recompression heat pump. The results demonstrate the model’s high fidelity in predicting energy consumption for various configurations, enabling the efficient search for systems with minimal energy consumption, carbon footprint and cost.