Abstract
The reduction of CO2-emissions in the chemical industry is essential to meet European climate targets. Particularly, the reliance on fossil fuels for process heat supply is a key factor for CO2-emissions. Electrically driven compression heat pumps are a promising option to reduce fossil fuel consumption by upgrading low-temperature waste heat to a higher temperature level, provided that low-carbon electricity is available. However, the integration of heat pumps into chemical utility systems remains a challenge due to economic constraints and the high complexity associated with site-wide heat integration and retrofit of existing structures. This work presents a mixed-integer linear programming (MILP) approach for the optimization of utility systems with integrated heat pumps. To address computational complexity, candidate utility temperature levels are pre-selected, and feasible heat pump coefficients of performance (COP) are precomputed. The framework is applied to both greenfield and retrofit scenarios for a synthetic case study consisting of 400 process streams. In the greenfield scenario, optimal utility temperature levels and heat pump integration configurations are identified. For the retrofit scenario, temperature levels of an existing utility system are modified to reduce total annual costs (TAC). Additionally, sensitivity analysis is conducted to assess the influence of key economic and environmental parameters. The presented case studies demonstrate short solution times, highlighting the suitability of the proposed framework for screening studies and systematic sensitivity analyses in early-stage design and retrofit applications.