AI-Driven Thermal Systems for Energy Efficiency and Environmental Sustainability

Closes: 29 Mar 2027

Journal: Technological Sustainability

Guest editor(s)

Ibrahim Ademola Fetuga, Dr. Temitope Olubanjo Kehinde, Ifeoluwa Awotunde, Seyi Rufus Oluwadare

Introduction

Thermal systems are central to the environmental performance of modern engineering. Heating, cooling, refrigeration, heat pumps, thermal storage, waste heat recovery, heat exchangers and industrial process heat consume a large share of global energy and are directly linked to emissions, resource use and operating cost. As climate conditions become more severe and energy systems move toward low-carbon supply, the design and operation of thermal technologies must become more adaptive, efficient and measurable. Artificial intelligence can support this transition when it is applied as an engineering tool rather than as a generic digital label.

This special issue focuses on AI-driven thermal systems from the perspective of energy efficiency and environmental sustainability. It seeks technically rigorous papers that show how machine learning, optimization, control, digital twins, physics-informed modelling, data analytics and intelligent monitoring can improve thermal performance, reduce energy consumption, support low-carbon operation and extend equipment life. The emphasis is on real contribution to engineering knowledge: validated models, experimentally supported findings, transparent datasets, physically meaningful performance indicators and clear sustainability outcomes. The Special Issue also welcomes critical and comparative studies that examine the limitations, trade-offs, failure cases, computational energy costs, data-quality challenges, validation gaps, rebound effects, and situations where AI does not lead to meaningful sustainability gains.

The issue invites studies on buildings, industrial processes, refrigeration and cold chains, heat pumps, HVAC, thermal energy storage, renewable thermal systems, waste heat recovery and advanced thermal management. Contributions may be computational, experimental, analytical or application-oriented, provided they demonstrate a clear link between AI-based methods and measurable environmental benefit.By bringing together researchers in energy systems, heat transfer, control and sustainable technology, this issue will highlight how intelligent thermal systems can contribute to reduced emissions, better resource efficiency and practical pathways for environmentally responsible engineering.

List of topic areas

• AI-based modelling, control and optimization of HVAC, heat pumps and refrigeration systems.
• Physics-informed machine learning and digital twins for heat transfer and thermal-system operation.
• Predictive maintenance, fault detection and diagnostics for thermal equipment.
• Energy-efficient and low-emission industrial thermal processes.
• AI-assisted design of heat exchangers, waste heat recovery systems and thermal networks.
• Thermal energy storage, phase-change materials and AI-enabled charging/discharging strategies.
• Renewable thermal energy integration, solar thermal systems and hybrid thermal-electric systems.
• Sustainable cold-chain technologies and low-GWP refrigeration.
• Life-cycle, exergy, energy and emissions assessment of AI-optimized thermal systems.
• Experimental validation, benchmark datasets and reproducible AI workflows for thermal engineering.
• Climate-adaptive thermal management for buildings, equipment and infrastructure.
• * Critical assessments of AI-driven thermal systems, including limitations, failure cases, computational energy use, validation gaps, rebound effects, and negative or inconclusive sustainability outcomes.

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available at: https://mc.manuscriptcentral.com/techs 

Author guidelines must be strictly followed. Please see: https://www.emeraldgrouppublishing.com/journal/techs#author-guidelines 

Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to ““Please select the issue you are submitting to”.

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key deadlines

Opening date for manuscripts submissions: 03/08/2026

Closing date for manuscripts submission: 29/03/2027

Email for submissions: ibfet1@morgan.edu