Actions

Download Analytics Citations

Export to: EndNote  |  Zotero  |  Mendeley

Collections

This file is not currently in any collections.

Investigation on thermochemical energy network for efficient waste heat recovery [dataset] Open Access

The performance of a thermo-chemical fluid (TCF) based energy network is investigated to recover the waste heat for sustainable thermal management. Accordingly, an experimental setup of a TCF energy network is developed to investigate the effects of working fluids under three different waste heating profiles: Gaussian, steady, and regenerative thermal oxidiser (RTO). The profiles are emulated through the graphical user interface-based LabVIEW to mimic the actual heating profiles for analysing the network's performance. An artificial intelligence-based multi-layer perceptron simulator is also developed to map the TCF energy network performance under different thermal scenarios. Results demonstrate that higher air flow rates significantly enhanced the total energy recovery for a large spectrum of solution flow rates. At a solution mass flow rate of 0.03 kg/s, the network achieves the highest effectiveness for potential energy recovery to around 30%. Increasing the heating temperature significantly enhances the moisture recovery performance of the TCF network, while simultaneously diminishing the sensitivity of the network to variations in the liquid-to-gas flow rate (L/G) ratio. At 80°C, the network achieves peak δωₐ values up to 4.3 g/kgda, and the differences between L/G ratios become less pronounced. Across all profiles, the water removal to heat supplied (W/H ratio) decreases as the L/G ratio increases, indicating a consistent decline in performance at higher desiccant flow rates. The Gaussian heating profile offers the highest water removal to heat supplied (W/H ratio) at lower L/G ratios compared to steady and RTO heating profiles, with a peak of around 3.3 kg/kW at an L/G of around 0.2. Further, the simulator demonstrates strong predictive accuracy for the TCF-based energy network under lower L/G ratios and both Gaussian and steady heating profiles, with minimal errors (RMSE as low as 0.09 and relative error of 2.39%). These findings provide essential insights for operating the TCF energy network, emphasising the importance of optimising working fluid operating conditions and maintaining high operational temperatures.

Descriptions

Resource type

Dataset

Contributors

Creator: Giampieri, Alessandro ¹
Bhowmik, Mrinal ²
Ma, Zhiwei ¹
Roskilly, Anthony Paul ¹

¹ Durham University, UK
² National Institute of Technology Manipur, India

Funder

Engineering and Physical Sciences Research Council

Research methods

Experimental data collection and modelling

Other description

Keyword

Thermochemical energy network
Gaussian heating
Steady and regenerative thermal oxidiser profile
AI-based simulator

Subject

Heat engineering

Location

Language

Cited in

Identifier

ark:/32150/r2j96020698
doi:10.15128/r2j96020698

Rights

Creative Commons Attribution 4.0 International (CC BY)

Publisher

Durham University

Date Created

File Details

Depositor

A. Giampieri

Date Uploaded

10 December 2025, 15:12:29

Date Modified

12 December 2025, 11:12:03

Audit Status

Audits have not yet been run on this file.

Characterization

File format: zip (ZIP Format)

Mime type: application/zip

File size: 2004987

Last modified: 2025:12:10 15:07:48+00:00

Filename: Research Datasets for Investigation on thermochemical energy network for efficient waste heat recovery.zip

Original checksum: e4a2cbd6f6ad39b6cb3d951936b08d1b