Principal Investigator
← Research projects
Ongoing project
Development of a Smart Waste-Heat Recovery and Thermal-Energy Storage System for Industrial Energy Efficiency
Summary
Industries such as breweries, food-processing plants, foundries, cement and agro-processing facilities release significant amounts of recoverable heat from exhaust gases, furnaces, boilers, dryers, compressors, and hot wastewater. This waste increases fuel consumption, operating costs, and emissions.
To develop and demonstrate an AI-enabled waste-heat recovery and thermal-energy storage system that improves industrial energy efficiency and reduces fuel consumption.
The project will identify and characterize industrial waste-heat sources, design a heat exchanger and thermal-storage unit using sensible or phase-change materials, and integrate IoT sensors for monitoring temperature, flow, recovered energy, and thermal demand. AI models will predict waste-heat availability and demand and optimize heat recovery, storage, and utilization. A laboratory/industrial-scale prototype will be evaluated using water or air heating as the useful thermal load.
The system integrates waste-heat recovery, thermal-energy storage, IoT sensing, AI prediction, and intelligent control to dynamically recover, store, and supply heat according to industrial demand.
The project will deliver a functional prototype, thermal-storage module, IoT monitoring platform, AI prediction and optimization model, waste-heat database, and techno-economic and environmental assessments. Performance targets include >50% heat-recovery efficiency, >80% storage efficiency, 15–30% fuel savings, and AI prediction error below 10%.
The project will reduce industrial energy losses, fuel consumption, operating costs, and CO₂ emissions while providing a scalable platform for sustainable industrial energy management and Industry 4.0 applications. |
To develop and demonstrate an AI-enabled waste-heat recovery and thermal-energy storage system that improves industrial energy efficiency and reduces fuel consumption.
The project will identify and characterize industrial waste-heat sources, design a heat exchanger and thermal-storage unit using sensible or phase-change materials, and integrate IoT sensors for monitoring temperature, flow, recovered energy, and thermal demand. AI models will predict waste-heat availability and demand and optimize heat recovery, storage, and utilization. A laboratory/industrial-scale prototype will be evaluated using water or air heating as the useful thermal load.
The system integrates waste-heat recovery, thermal-energy storage, IoT sensing, AI prediction, and intelligent control to dynamically recover, store, and supply heat according to industrial demand.
The project will deliver a functional prototype, thermal-storage module, IoT monitoring platform, AI prediction and optimization model, waste-heat database, and techno-economic and environmental assessments. Performance targets include >50% heat-recovery efficiency, >80% storage efficiency, 15–30% fuel savings, and AI prediction error below 10%.
The project will reduce industrial energy losses, fuel consumption, operating costs, and CO₂ emissions while providing a scalable platform for sustainable industrial energy management and Industry 4.0 applications. |