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Sustainability
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Reimagining Sustainability: Green Mobility, Natural Ventilation and Artificial Intelligence-Driven Design
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Reimagining Sustainability: Green Mobility, Natural Ventilation and Artificial Intelligence-Driven Design

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1739

Special Issue Editors

Dr. Perk Lin Chong

E-Mail Website
Guest Editor
School of Computing, Engineering & Digital Technologies, Department of Engineering, Teesside University, Middlesbrough TS1 3BX, UK
Interests: sustainable energy; finite element analysis; CADCAM; light weighting; automotive design; electric vehicle
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pengyan Guo

E-Mail Website
Guest Editor
School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Interests: proton exchange membrane fuel cell; electric vehicle; vehicle dynamics; automotive design
Dr. Lip Kean Moey

E-Mail Website
Guest Editor
Centre for Modelling and Simulation, Faculty of Engineering, Built Environment & Information Technology, SEGi University, Selangor 47810, Malaysia
Interests: renewable energy; building ventilation; optimization
Dr. Foad Faraji

E-Mail Website
Guest Editor
School of Computing, Engineering & Digital Technologies, Department of Engineering, Teesside University, Middlesbrough TS13BX, UK
Interests: engineering application of machine learning; finite element methods; computational fluid dynamics; statistical modelling; sustainable materials

Special Issue Information

Dear Colleagues,

Sustainability is a global priority driving innovation across industries. This Special Issue highlights research advancing sustainable development, focusing on green mobility, energy-efficient systems, and artificial intelligence (AI)-driven solutions. Both experimental and simulation-based studies are encouraged, reflecting an interdisciplinary approach.

Green mobility topics include electric vehicles, fuel cells, lightweighting, and sustainable materials, emphasizing carbon reduction and energy efficiency. Energy-efficient building systems, such as natural ventilation and thermal management, reduce consumption and enhance environmental performance.

AI technologies have revolutionized sustainability by optimizing designs, improving system efficiency, and tackling complex challenges, fostering smarter, greener solutions across diverse applications.

This Special Issue invites contributions on topics including, but not limited to, the following:

  • Finite element analysis for lightweighting;
  • Multi-scale simulations for material and structural innovations;
  • Structural design for sustainable development;
  • Fuel cell technologies;
  • Lightweight materials;
  • Metamaterial design;
  • Aerodynamic analysis and optimization of sustainable vehicles;
  • Building energy systems and ventilation;
  • Thermal comfort analysis in sustainable architecture;
  • Optimization of Heating, Ventilation, and Air – Conditioning Systems;
  • AI-driven design optimization for sustainable development;
  • AI applications in renewable energy;
  • Data-driven approaches for energy-efficient system management.

Dr. Perk Lin Chong
Prof. Dr. Pengyan Guo
Dr. Lip Kean Moey
Dr. Foad Faraji
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainability
  • green mobility
  • energy efficiency
  • artificial intelligence-driven solutions
  • lightweight materials

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

23 pages, 4029 KB  
Article
Simulation-Based Optimization of HVAC Systems in Aging Educational Facilities: Addressing IAQ Challenges Through Retrofitting
by Cihan Turhan, Yousif Abed Saleh Saleh and Burcu Turhan
Sustainability 2026, 18(6), 3079; https://doi.org/10.3390/su18063079 - 20 Mar 2026
Viewed by 623
Abstract
Indoor air quality (IAQ) in educational buildings plays a critical role in the health, cognitive performance, and well-being of occupants. Aging university facilities often rely on outdated ventilation systems that are not designed to meet current demands or respond to dynamic occupancy levels. [...] Read more.
Indoor air quality (IAQ) in educational buildings plays a critical role in the health, cognitive performance, and well-being of occupants. Aging university facilities often rely on outdated ventilation systems that are not designed to meet current demands or respond to dynamic occupancy levels. This study investigates the performance and feasibility of various advanced ventilation strategies in comparison to an existing balanced mechanical ventilation (BMV) system in a university classroom accommodating 100 students. Using a Dynamic Building Energy Simulation Program, simulations were conducted to evaluate IAQ (using CO2 levels), energy consumption, and thermal comfort under three retrofitting scenarios: BMV, demand-controlled ventilation (DCV), and hybrid ventilation combining natural and mechanical airflow. The simulations indicate that DCV cuts annual HVAC energy use by 33% relative to the baseline, while the hybrid strategy achieves the greatest reduction of 42% and maintains CO2 levels and thermal comfort within recommended limits. Although hybrid systems provide seasonal advantages, their complexity may limit applicability. In addition to technical analysis, this study also explores the financial and tax-related challenges associated with retrofitting ventilation systems in university buildings. Investment payback periods, operational costs, and potential tax incentives are discussed to evaluate economic viability. Overall, the endorse hybrid ventilation as the most cost-effective strategy where mixed-mode control is feasible, and DCV as a practical alternative for buildings unable to employ natural ventilation. Full article
(This article belongs to the Special Issue Reimagining Sustainability: Green Mobility, Natural Ventilation and Artificial Intelligence-Driven Design)
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23 pages, 1687 KB  
Article
Machine Learning-Based Dry Gas Reservoirs Z-Factor Prediction for Sustainable Energy Transitions to Net Zero
by Progress Bougha, Foad Faraji, Parisa Khalili Nejad, Niloufar Zarei, Perk Lin Chong, Sajid Abdullah, Pengyan Guo and Lip Kean Moey
Sustainability 2026, 18(4), 1742; https://doi.org/10.3390/su18041742 - 8 Feb 2026
Viewed by 561
Abstract
Dry gas reservoirs play a pivotal transitional role in meeting the net-zero target worldwide. Accurate modelling and simulation of this energy source require fast and reliable prediction of the gas compressibility factor (Z-factor). The experimental measurements of Z-factor are the most reliable source; [...] Read more.
Dry gas reservoirs play a pivotal transitional role in meeting the net-zero target worldwide. Accurate modelling and simulation of this energy source require fast and reliable prediction of the gas compressibility factor (Z-factor). The experimental measurements of Z-factor are the most reliable source; however, they are expensive and time-consuming. This makes developing accurate predictive models essential. Traditional methods, such as empirical correlations and Equations of States (EoSs), often lack accuracy and computational efficiency. This study aims to address these limitations by leveraging the predictive power of machine learning (ML) techniques. Hence in this study three ML models of Artificial Neural Network (ANN), Group Method of Data Handling (GMDH), and Genetic Programming (GP) were developed. These models were trained on a comprehensive dataset comprising 1079 samples where pseudo-reduced pressure (Ppr) and pseudo-reduced temperature (Tpr) served as input and experimentally measured Z-factors as output. The performance of the developed ML models was benchmarked against two cubic EoSs of Peng–Robinson (PR) and van der Waals (vdW), and two semi-empirical correlations of Dranchuk-Abou-Kassem (DAK) and Hall and Yarborough (HY), and recent developed ML based models, using statistical metrics of Mean Squared Error (MSE), coefficient of determination (R2), and Average Absolute Relative Deviation Percentage (AARD%). The proposed ANN model reduces average prediction error by approximately 70% relative to the PR equation of state and by over 35% compared with the DAK correlation, while maintaining robust performance across the full Ppr and Tpr of dry gas systems. Additionally paired t-tests and Wilcoxon signed-rank tests performed on the ML results confirmed that the ANN model achieved statistically significant improvements over the other models. Moreover, two physical equations using the white-box models of GMDH and GP were proposed as a function of Ppr and Tpr for prediction of the dry gas Z-factor. The sensitivity analysis of the data shows that the Ppr has the highest positive effect of 88% on Z-factor while Tpr has a moderate effect of 12%. This study presents the first unified, statistically validated comparison of ANN, GMDH, and GP models for accurate and interpretable Z-factor prediction. The developed models can be used as an alternative tool to bridge the limitation of cubic EoSs and limited accuracy and applicability of empirical models. Full article
(This article belongs to the Special Issue Reimagining Sustainability: Green Mobility, Natural Ventilation and Artificial Intelligence-Driven Design)
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