Imagine a super-powered heat pump that can take lukewarm waste heat and boost it to temperatures hot enough for industrial cooking or cleaning. It also includes a thermal battery that saves this heat for later, so factories don't have to rely on the sun shining or waste heat being available at that exact moment. An AI brain manages everything to make sure the energy is used as efficiently as possible.
Industrial sectors rely on fossil fuels for high-temperature heat, contributing to 552 Mt/a of CO2 emissions. Current renewable options often cannot reach the 150-250 °C required for many processes.
A 200 kWth pilot system featuring a Stirling-based high-temperature heat pump, a bio-inspired phase change material storage system, and an AI-driven digital twin for energy control.
If you are a dairy processor dealing with high energy costs for heating processes — this project developed a Stirling-based heat pump and storage system that provides heat up to 250 °C. This allows you to reuse waste heat and solar energy to reduce CO2 emissions. It targets a COP of up to 2.8 for temperature ratios of 1.2.
If you are a textile manufacturer dealing with heavy reliance on fossil fuels for high-temperature steam — this project developed a heat upgrade system that reaches 150-250 °C. By integrating bio-inspired storage, you can decouple heat production from availability. This helps reduce GHG emissions up to 145 gCO2/kWh.
If you are a petrochemical plant dealing with intermittent renewable energy sources — this project developed a Control & Integration Twin (CIT) and thermal storage. This ensures a reliable heat delivery even when solar or waste heat is low. It aims for an annual reduction of 20 million tons of CO2 across the sector.
Based on available project data, specific pricing or cost per unit is not provided as the project is currently at the validation stage.
The project is validating a 200 kWth pilot to emulate industrial needs. It aims to extend findings to sectors like Pulp & Paper and Basic Metals to prove scalability.
Based on available project data, there is no specific mention of licensing terms or patent ownership in the provided summary.
Integration is managed via a Digital Twin (CIT) and AI-driven control systems that optimize energy management and provide self-assessment tools for industrial sites.
The project runs from May 2023 to April 2027, focusing on reaching TRL 5 validation during this period.
The consortium is heavily weighted toward commercial application, with 15 partners across 11 countries. Notably, 60% of the partners are from industry, and 9 are SMEs, indicating a strong focus on market viability and practical implementation rather than purely academic research.
Contact Universidad Nacional de Educación a Distancia (UNED) in Spain
Contact us to find a partner for TRL 5 pilot validation in the food or textile sector.
