A Carnot-Based Pre-Screening Method to Support High-Temperature Heat Pump Integration in Industries
Integrating high-temperature heat pumps (HTHPs) into manufacturing industries represents a promising pathway toward industrial decarbonization. However, traditional design approaches often rely on detailed modeling of working fluids and cycle configurations, making the process slow, complex, and difficult to scale. Moreover, design decisions such as sizing and temperature-level selection are frequently based on engineering intuition, which can lead to suboptimal solutions. This work introduces a flexible and automated pre-screening methodology based on Carnot heat pump principles, aimed at rapidly identifying optimal HTHP integration strategies. The method begins by generating Pinch-based profiles from process stream data to represent heating and cooling demands across different temperature levels. The resulting optimization problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem with strong nonconvexities and multiple local optima. To efficiently explore this complex design space, evolutionary algorithms such as the Genetic Algorithm (GA) are employed. The objective is to determine the number, size, temperature levels, and allocation of HTHPs that achieve the best trade-off between energy efficiency, expressed as the Coefficient of Performance (COP), and total cost. Solutions are evaluated using ideal Carnot performance to ensure thermodynamic feasibility and general applicability. This pre-screening step significantly narrows the design space for real-system optimization, enabling more computationally efficient exploration of detailed configurations. The model also allows rapid recalculations under varying energy or equipment prices, offering insights into cost-efficiency trade-offs. While increasing the number of heat pumps typically improves COP, it also raises capital cost; the proposed method identifies the optimal balance. A case study from the paper industry demonstrates that this approach can generate actionable results within seconds, providing a scalable foundation for subsequent optimization and real-world HTHP deployment.