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Open AccessEditorial

Statement of Peer Review

by Charaf Laghlimi, Younes Ziat, Zakaryaa Zarhri, Noureddine Lakouari, Hamza Belkhanchi and Abdelaziz Moutcine

Eng. Proc. 2026, 144(1), 16; https://doi.org/10.3390/engproc2026144016 - 17 Aug 2026

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Abstract

In submitting conference proceedings for the 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment (STR2E 2026) to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in [...] Read more.

In submitting conference proceedings for the 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment (STR2E 2026) to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors strictly following the policies announced on the conference website [...] Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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28 pages, 71479 KB

Open AccessEditorial

Preface: Second International Conference on Sciences and Techniques for Renewable Energy and the Environment

by Charaf Laghlimi, Younes Ziat, Zakaryaa Zarhri, Noureddine Lakouari, Hamza Belkhanchi and Abdelaziz Moutcine

Eng. Proc. 2026, 144(1), 17; https://doi.org/10.3390/engproc2026144017 - 20 Aug 2026

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Abstract

The STR2E conference (International Conference on Sciences and Techniques for Renewable Energy and the Environment), organized by the research team focused on chemistry, computer science and artificial intelligence (ERCI2A) at the Faculty of Sciences and Techniques-Al Hoceima, the Moroccan Association of Science and [...] Read more.

The STR2E conference (International Conference on Sciences and Techniques for Renewable Energy and the Environment), organized by the research team focused on chemistry, computer science and artificial intelligence (ERCI2A) at the Faculty of Sciences and Techniques-Al Hoceima, the Moroccan Association of Science and Technology for Sustainable Development (MASTSD) and Abdelmalek Essaadi University, Tétouan, Morocco, aims to promote scientific exchange and interdisciplinary collaboration in all fields of engineering sciences and techniques related to renewable energy and the environment [...] Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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Other

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10 pages, 3507 KB

Open AccessProceeding Paper

Ozone-Based Pretreatment of Waste Sludge for Enhanced Anaerobic Digestion and Biogas Yield

by Safaa Alqudah and Ramiro Martins

Eng. Proc. 2026, 144(1), 1; https://doi.org/10.3390/engproc2026144001 - 18 Jun 2026

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Abstract

Anaerobic digestion of municipal wastewater sludge is often limited by slow hydrolysis rates. This study evaluated the effects of ozone pretreatment on methane production during mesophilic batch digestion. Ozone was applied at 0–10% for 30–90 s, with inoculum-to-substrate ratios of 1.0–2.0. Methane production [...] Read more.

Anaerobic digestion of municipal wastewater sludge is often limited by slow hydrolysis rates. This study evaluated the effects of ozone pretreatment on methane production during mesophilic batch digestion. Ozone was applied at 0–10% for 30–90 s, with inoculum-to-substrate ratios of 1.0–2.0. Methane production was monitored using the AMPTS II system. The maximum methane yield (736 NmL CH₄ g⁻¹ VS; 1381 NmL total) was obtained at 10% ozone for 30 s and I/S = 1.5. Kinetic modelling showed enhanced methane production rates and reduced lag phases, with the Gompertz and Logistic models providing the best fit. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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9 pages, 870 KB

Open AccessProceeding Paper

Comparative Review of Solar Radiation Models for Hourly Solar Intensity Estimation in the Indonesian Tropical Region

by Muhammad Arif Budiyanto

Eng. Proc. 2026, 144(1), 2; https://doi.org/10.3390/engproc2026144002 - 18 Jun 2026

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Abstract

Indonesia, located along the equatorial belt, has consistently high solar irradiance, offering strong potential for renewable energy development. However, limited availability of high-resolution solar radiation data constrains accurate system design. This study aims to evaluate eight empirical models for estimating hourly solar radiation [...] Read more.

Indonesia, located along the equatorial belt, has consistently high solar irradiance, offering strong potential for renewable energy development. However, limited availability of high-resolution solar radiation data constrains accurate system design. This study aims to evaluate eight empirical models for estimating hourly solar radiation under tropical conditions and identify the most suitable approach for data-scarce regions. The novelty lies in a comparative assessment tailored to Indonesia’s tropical climate and its application to sustainable energy planning. Model performance is assessed using MBE, RMSE, and R² against measured data. The results identify the most accurate model, which serves as the basis for developing a modified model that better represents local atmospheric characteristics. The proposed model improves estimation accuracy and supports more reliable solar resource assessment for sustainable energy applications in tropical regions. These findings support improved solar resource assessment and contribute to more reliable and sustainable solar energy system development in tropical regions. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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10 pages, 2797 KB

Open AccessProceeding Paper

Application of Machine Learning for the Prediction of Coulombic Efficiency in Lithium Metal Batteries

by Sergio Rubén Ocampo-Pérez, Noureddine Lakouari and Outmane Oubram

Eng. Proc. 2026, 144(1), 3; https://doi.org/10.3390/engproc2026144003 - 23 Jun 2026

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Abstract

The commercialization of lithium metal batteries, a key technology for high-density energy storage, is hindered by issues with coulombic efficiency, which dictates battery stability and life. In this paper, we propose a machine learning framework to forecast liquid electrolyte efficiency, where two experimental [...] Read more.

The commercialization of lithium metal batteries, a key technology for high-density energy storage, is hindered by issues with coulombic efficiency, which dictates battery stability and life. In this paper, we propose a machine learning framework to forecast liquid electrolyte efficiency, where two experimental data sources were combined to create a curated dataset of 283 records. In addition, to assess several ensemble learning algorithms, thirteen chemical descriptors were used, as well as interpretability analysis and Bayesian optimization to guarantee physicochemical consistency. We found that the optimized CatBoost model achieved a coefficient of determination (R2) of 0.61 on the test set and a mean squared error (MSE) of 0.0924, representing a significant improvement in predictive accuracy compared to previous standards. Furthermore, these results demonstrate that regulating oxygen levels in solvent environments is a key component of high-density energy storage. These results can serve as a virtual screening tool in order to discover high-performance electrolytes with the minimum experimental costs. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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10 pages, 985 KB

Open AccessProceeding Paper

Forecasting Energy Consumption Using a Hybrid LSTM-XGBoost Model

by Youssef Sadik, Ali Nejmi, Lahoucine Oumiguil and Mohamed Baite

Eng. Proc. 2026, 144(1), 4; https://doi.org/10.3390/engproc2026144004 - 23 Jun 2026

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Abstract

Accurate short-term forecasting for energy consumption is crucial in modern energy network management, especially for cities such as Tetouan, where considerable climate variability and diverse usage patterns present significant challenges when it comes to making short-term forecasts. This paper proposes a hybrid residual [...] Read more.

Accurate short-term forecasting for energy consumption is crucial in modern energy network management, especially for cities such as Tetouan, where considerable climate variability and diverse usage patterns present significant challenges when it comes to making short-term forecasts. This paper proposes a hybrid residual learning framework that combines a long short-term memory (LSTM) network with eXtreme Gradient Boosting (XGBoost) to improve short-term load forecasting for the Tetouan electricity network. The novelty of the proposed approach lies in coupling temporal sequence modeling with residual error correction driven by exogenous meteorological and calendar-related information. The proposed model is validated using real electricity consumption data from Zone 2 of Tetouan City, with further validation across all three available zones confirming the model’s generalizability. The proposed model achieves a coefficient of determination (R²) of 0.984, an RMSE of 687.21 kWh, and a MAPE of 2.41%, representing a 121.3 kWh RMSE improvement over the standalone LSTM baseline. These results confirm that the hybrid model is better at tracking periods of high demand compared to conventional machine learning approaches and standalone deep learning models. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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7 pages, 1076 KB

Open AccessProceeding Paper

Valorization of a New Benzimidazole-Based Green Inhibitor: Theoretical DFT Studies

by Oualid Sokkah, Hicham Essassaoui, Aziz Ihammi, Darifa Addichi, Saliha Loughmari, Mohamed Ellouz and Mohammed Chigr

Eng. Proc. 2026, 144(1), 5; https://doi.org/10.3390/engproc2026144005 - 23 Jun 2026

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Abstract

Benzimidazole derivatives are widely used as a drug. In this work, we valorized the electrochemical activities of our new Benzimidazole-derived compound, 2-(4-chlorophenyl)-4-nitro-1H-benzimidazole (OS1), as a green inhibitor that can be used in environmental applications, which gives rise to a tautomeric equilibrium (A and [...] Read more.

Benzimidazole derivatives are widely used as a drug. In this work, we valorized the electrochemical activities of our new Benzimidazole-derived compound, 2-(4-chlorophenyl)-4-nitro-1H-benzimidazole (OS1), as a green inhibitor that can be used in environmental applications, which gives rise to a tautomeric equilibrium (A and B), applied to mild steel in a 1 M HCl solution. The inhibitory effect of 2-(4-chlorophenyl)-4-nitro-1H-benzimidazole (OS1) on carbon steel (CS) corrosion in 1 M HCl was evaluated with DFT studies. The value ΔN = 0.019 for A and 0.057 is positive and less than 3.6, indicating that the molecule functions as an electron donor towards the iron substrate, creating a shielding layer that prevents corrosion. DFT results identified the nucleophilic that C(4,6,24) N(12) O(15) and Cl(27) in A and C(4,5,6,32) O(14,15) and Cl(26) in B and electrophilic C(6) N(12) and O(15) in A and O(14,15) in B attack sites. Notably, these findings established that Tautomer A is the most stable. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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10 pages, 1566 KB

Open AccessProceeding Paper

Field-Validated Predictive Maintenance Framework for Desert PV Systems Using Machine Learning and Reinforcement Learning

by Amjad Ech-Charqaouy, Sidi Salah Ech-Charqaouy, Abdelkader Boulezhar, Nizar Ech-Charqaouy and Redouane Mihramane

Eng. Proc. 2026, 144(1), 6; https://doi.org/10.3390/engproc2026144006 - 25 Jun 2026

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Abstract

Photovoltaic systems in desert environments face severe degradation due to heat and dust, making predictive maintenance essential. Unlike existing approaches limited to fault diagnosis, this work proposes a fully integrated closed-loop framework combining IoT monitoring, machine learning diagnostics, and reinforcement learning decision-making. The [...] Read more.

Photovoltaic systems in desert environments face severe degradation due to heat and dust, making predictive maintenance essential. Unlike existing approaches limited to fault diagnosis, this work proposes a fully integrated closed-loop framework combining IoT monitoring, machine learning diagnostics, and reinforcement learning decision-making. The approach is validated using six months of real data from a 20 MW PV plant in Boujdour, Morocco (500,000 records). Results show 96.3% diagnostic accuracy, 22% downtime reduction, 18% fewer unnecessary interventions, and a 12% performance ratio improvement, demonstrating enhanced reliability and economic efficiency. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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8 pages, 1257 KB

Open AccessProceeding Paper

Enhancing Methane Production from Crude Glycerol Through Ultrasound Pretreatment

by Ramiro Martins and Safaa Alqudah

Eng. Proc. 2026, 144(1), 7; https://doi.org/10.3390/engproc2026144007 - 25 Jun 2026

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Abstract

As energy demand continues to increase, the environmental impact of conventional petroleum-based sources has become a growing concern. Biofuels offer a sustainable alternative, with crude glycerol from biodiesel production showing promise for methane production via anaerobic digestion. To optimize methane production, the application [...] Read more.

As energy demand continues to increase, the environmental impact of conventional petroleum-based sources has become a growing concern. Biofuels offer a sustainable alternative, with crude glycerol from biodiesel production showing promise for methane production via anaerobic digestion. To optimize methane production, the application of ultrasound as a pretreatment method has been investigated. This study introduces the novel use of ultrasound pretreatment to enhance methane yield from crude glycerol and improve anaerobic digestion efficiency. This work explores the relationship between ultrasound-pretreated crude glycerol and methane production while also assessing the role of reactor operational parameters in determining the final generated volume. The main purpose of this study is to determine how ultrasound duration and process conditions affect biogas performance and to identify an optimal strategy for maximizing methane output from this biodiesel by-product. Chemical oxygen demand (COD) increased from 29.1 to 45.1 g L⁻¹ after 30 min of ultrasound, representing a 55% rise due to enhanced organic matter disintegration. Methane generation improved markedly with pretreatment duration, increasing from 520 mL (10 min) to 1440 mL (15 min) and reaching 13,185 mL after 30 min in the laboratory reactor. The methane volume obtained in 22 days from glycerol subjected to a 30 min ultrasound pretreatment using a 1% glycerol mixture reached an impressive 16,224 mL. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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13 pages, 4626 KB

Open AccessProceeding Paper

Physics-Informed Deep Reinforcement Learning for Compact VBT Farms: Integration, Power Quality, and Economics

by Nizar Ech-Charqaouy, Sidi Salah Ech-Charqaouy, Abdelkader Boulezhar, Amjad Ech-Charqaouy and Redouane Mihramane

Eng. Proc. 2026, 144(1), 8; https://doi.org/10.3390/engproc2026144008 - 25 Jun 2026

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Abstract

This paper presents a physics-informed Deep Q-Network (DQN) framework for optimizing the deployment of 100 vortex bladeless turbines (VBTs) in a Saharan microgrid. The proposed approach integrates wake interaction modeling, land-use constraints, techno-economic factors, and power quality (PQ) indicators at the point of [...] Read more.

This paper presents a physics-informed Deep Q-Network (DQN) framework for optimizing the deployment of 100 vortex bladeless turbines (VBTs) in a Saharan microgrid. The proposed approach integrates wake interaction modeling, land-use constraints, techno-economic factors, and power quality (PQ) indicators at the point of common coupling. The novelty lies in coupling aerodynamic modeling with reinforcement learning and grid constraints. Results show that dense layouts (≤400 m²) yield up to 41% gains but degrade PQ (Pst > 1.0, THD > 5%). An optimal range of 500–800 m² achieves stable performance with moderate gains (6–9%) and acceptable PQ. Larger surfaces (>1000 m²) show limited benefits (<4%). The framework supports efficient and sustainable wind deployment in constrained environments. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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12 pages, 1888 KB

Open AccessProceeding Paper

Physics-Constrained Multi-Agent Deep Reinforcement Learning for Real-Time Energy Management of a Saharan Hybrid Microgrid

by Redouane Mihramane, S. Salah Ech-Charqaouy, Abdelkader Boulezhar, Amjad Ech-Charqaouy and Nizar Ech-Charqaouy

Eng. Proc. 2026, 144(1), 9; https://doi.org/10.3390/engproc2026144009 - 25 Jun 2026

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Abstract

This paper addresses the challenge of ensuring physically feasible and reliable real-time control of hybrid microgrids in harsh desert environments. A physics-constrained multi-agent Deep Q-Network (MA-DQN) is proposed for energy management of a grid-interactive microgrid in the Moroccan Sahara. The method embeds operational [...] Read more.

This paper addresses the challenge of ensuring physically feasible and reliable real-time control of hybrid microgrids in harsh desert environments. A physics-constrained multi-agent Deep Q-Network (MA-DQN) is proposed for energy management of a grid-interactive microgrid in the Moroccan Sahara. The method embeds operational constraints directly into learning through action filtering, penalty-aware rewards, and coordinated PCC control. The results show a reduction in operational cost from 1250 MAD to 1120 MAD (−10.4%) and CO₂ emissions from 318.9 kg to 272.5 kg (−14.6%), while maintaining voltage within ±10% limits and eliminating PCC oscillations. The framework delivers stable, reliable, and deployment-ready control. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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10 pages, 2105 KB

Open AccessProceeding Paper

Geometrically Nonlinear Dynamics of Cracked Beams with Rotational Flexibility

by Mohamed Janati, Lmokhtar Ikharrazne and Mustapha Lamine

Eng. Proc. 2026, 144(1), 10; https://doi.org/10.3390/engproc2026144010 - 3 Jul 2026

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Abstract

This study examines the geometrically nonlinear free vibration behaviour of a clamped–clamped Euler–Bernoulli beam weakened by an open transverse crack. The crack is modelled through an equivalent rotational flexibility formulation grounded in fracture mechanics, while geometric nonlinearity is incorporated by accounting for mid-plane [...] Read more.

This study examines the geometrically nonlinear free vibration behaviour of a clamped–clamped Euler–Bernoulli beam weakened by an open transverse crack. The crack is modelled through an equivalent rotational flexibility formulation grounded in fracture mechanics, while geometric nonlinearity is incorporated by accounting for mid-plane stretching associated with large vibration amplitudes. A reduced-order formulation is established using a Galerkin approach with eigenfunctions that explicitly depend on the presence of the crack, leading to a nonlinear eigenvalue problem in which the response is amplitude-dependent. The primary aim is to clarify how crack severity and vibration amplitude jointly influence the distribution of nonlinear bending stresses along the beam. Unlike much of the existing literature, which predominantly emphasises frequency–amplitude interactions, this work adopts a stress-oriented framework and offers a detailed characterisation of the spatial variation in the normalised stress field. The results indicate that bending stresses increase markedly as vibration amplitude grows, with the most pronounced effects occurring near the clamped ends where curvature is highest. Furthermore, deeper cracks significantly intensify stress concentrations due to the associated local reduction in stiffness. Overall, the findings provide enhanced physical understanding of the nonlinear dynamic behaviour of cracked beam structures and establish a useful foundation for evaluating structural integrity under conditions of large-amplitude vibration. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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8 pages, 594 KB

Open AccessProceeding Paper

Optimization of Energy Availability of Offshore Solar Photovoltaic Systems in the Middle East Considering Tilt Angle and Fouling Effects

by Muhammad Taufiq, Joko Waluyo and Nugroho Dewayanto

Eng. Proc. 2026, 144(1), 11; https://doi.org/10.3390/engproc2026144011 - 7 Jul 2026

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Abstract

The integration of solar photovoltaic (PV) systems on offshore platforms has emerged as a promising solution to reduce reliance on diesel-based power generation and associated with greenhouse gas emissions. However, the combined influence of environmental and installation factors, particularly tilt angle and fouling, [...] Read more.

The integration of solar photovoltaic (PV) systems on offshore platforms has emerged as a promising solution to reduce reliance on diesel-based power generation and associated with greenhouse gas emissions. However, the combined influence of environmental and installation factors, particularly tilt angle and fouling, remains insufficiently explored in offshore conditions. This study aims to evaluate the interaction between tilt angle and fouling on PV system performance under practical offshore constraints. A series of simulations was conducted using PVsyst by varying tilt angles (10°, 26°, and 40°) and fouling factors (0%, 5%, and 10%). The results indicate that fouling has a significantly greater impact on system performance than tilt angle variation. Increasing fouling from 0% to 10% leads to energy yield reductions of approximately 9%, while variations in tilt angle within the tested range result in differences of less than 3%. The highest energy yield was achieved at a tilt angle of 26°, reaching approximately 179 MWh/year, whereas performance ratio shows a slight increase with higher tilt angles. These findings suggest that, under offshore environmental conditions, operational strategies such as fouling mitigation and maintenance play a more critical role than geometric optimization. This study provides practical insights for improving the reliability and energy efficiency of offshore PV systems. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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6 pages, 911 KB

Open AccessProceeding Paper

An Imaging Approach for Identifying Damage in Plate-like Structures Using PZT Sensor Arrays Through the Integration of the RAPID Algorithm

by Mohamed Ajakane, Ismaine Zitouni, Hoda Ben Chraa, Bouchra Saadouki and Hassan Rhimini

Eng. Proc. 2026, 144(1), 12; https://doi.org/10.3390/engproc2026144012 - 29 Jul 2026

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Abstract

The goal of this work is to develop an optimized framework for ultrasonic guided wave imaging in order to increase the precision of damage localization in planar structures. This work is novel in that it refines the Reconstruction Algorithm for Probabilistic Inspection of [...] Read more.

The goal of this work is to develop an optimized framework for ultrasonic guided wave imaging in order to increase the precision of damage localization in planar structures. This work is novel in that it refines the Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) by assessing how the scaling factor β affects imaging resolution. To track structural integrity, the technique makes use of a surface-mounted array of twelve piezoelectric transducers (PZT). Numerical simulations were performed on an aluminum plate with through-hole defect in order to assess the performance. To replicate realistic Lamb wave interactions, a finite element model was created using CIVA SHM software (version 2023). MATLAB (version 2022) was used to process the obtained signals in order to produce probabilistic defect distribution maps. The RAPID algorithm, optimized with a scaling factor β = 1.05, achieves relative localization accuracy, according to numerical results. The study provides a solid numerical foundation for further experimental validation by confirming that the suggested configuration detects structural damage. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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11 pages, 3904 KB

Open AccessProceeding Paper

Quantifying the Impact of Drivetrain Flexibility on PID-Based MPPT Performance in Modern Wind Turbines: A Simulation-Based Comparative Study

by Mohamed Tahiri, Yassine Lakhal, Soulaiman Louah and Abdelaziz Mimet

Eng. Proc. 2026, 144(1), 13; https://doi.org/10.3390/engproc2026144013 - 31 Jul 2026

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Abstract

The following is a detailed account of how the flexibility of a drive train affects maximum power point tracking (MPPT) for wind turbines. The study uses the National Renewable Energy Laboratory 5 MW reference turbine as a base model with Kaimal spectrum having [...] Read more.

The following is a detailed account of how the flexibility of a drive train affects maximum power point tracking (MPPT) for wind turbines. The study uses the National Renewable Energy Laboratory 5 MW reference turbine as a base model with Kaimal spectrum having felt waves creating turbulent winds. This study compares both rigid and flexible drive trains that undergo identical turbulence conditions (average wind speed 9.0 m/s and flight weather turbulence, 16% T.I.). The results show that the flexible unit provides an 8.95 percent area increase in mean power coefficient, (p < 0.001, Cohen’s d = 2.108) while the rigid unit produces a 4173 percent (p < 10⁻⁴⁷, Cohen’s d = 31.8) sudden rise in power during catastrophic loss of control and produce a direct torque oscillation of over 153 MN·m RMS between the two configurations. The conclusion of this research establishes that PID Controllers that are manually tuned based on measurements taken with rigid drive trains cannot be used with flexible drivetrains. Therefore, it is necessary to either re-tune the PID Controller quickly after switching between form of drive train; and/or implement advanced control strategies to effectively manage these situations in the future. This study shows that rigid-model assumptions are fundamentally inadequate for contemporary turbine control design, which directly results in decreased energy yield. The findings necessitate a paradigm shift towards control strategies that specifically take structural dynamics into consideration in order to produce vibration-resilient MPPT for next-generation turbines. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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10 pages, 1877 KB

Open AccessProceeding Paper

AI-Driven Shortest-Path Routing Techniques in IoT-Enabled RES-Based EV and Vehicular Networks: A Comprehensive Review of Deep Learning Models

by Balaji Viswanathan, Thoudam Basanta Singh, Brindha Devi Varadharajalu, Maheswari Ellappan and Mutum Bidyarani Devi

Eng. Proc. 2026, 144(1), 14; https://doi.org/10.3390/engproc2026144014 - 31 Jul 2026

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Abstract

Renewable energy system (RES)-based electric vehicle (EV) charging infrastructure enhances energy security. The need for intelligent routing techniques that achieve low latency, high reliability and adaptive path selection under extremely dynamic traffic situations has increased owing to the quick growth of Internet of [...] Read more.

Renewable energy system (RES)-based electric vehicle (EV) charging infrastructure enhances energy security. The need for intelligent routing techniques that achieve low latency, high reliability and adaptive path selection under extremely dynamic traffic situations has increased owing to the quick growth of Internet of Things (IoT)-enabled vehicular networks. With an emphasis on recurrent neural networks (RNNs), deep belief networks (DBNs), radial basis function neural networks (RBFNNs), and long short-term memory (LSTM) networks, in addition to convolutional neural networks (CNNs), this analysis looks at cutting-edge AI-based models used for shortest-path routing in IoT-driven vehicular ad hoc networks (VANETs). The paper examines how various designs handle issues such as connection instability, heterogeneous sensor data, quick topological changes, and real-time decision making. A comparative analysis shows that DBN and CNN display strong feature learning for intricate mobility patterns and congestion recognition, while sequence-aware techniques like RNN and LSTM advance spatiotemporal traffic estimation. For low-latency route evaluation, RBFNN compromises rapid nonlinear representation. The examination shows that CNN models greatly improve the scalability, adaptability and optimality of routing, confirming AI-enabled structures as a promising path for next-generation IoT-based vehicular routing methods. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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13 pages, 9177 KB

Open AccessProceeding Paper

A Systematic Literature Review of Thermoelectric Properties of Antimony Trisulfide (Sb₂S₃)

by Sabir Hajjaji and Khalid Nouneh

Eng. Proc. 2026, 144(1), 15; https://doi.org/10.3390/engproc2026144015 - 3 Aug 2026

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Abstract

Because antimony trisulfide (Sb₂S₃) is abundant on Earth, non-toxic, and naturally has a low lattice thermal conductivity, it has garnered increasing interest as a possible thermoelectric material. One factor contributing to its anisotropic transport behavior is the orthorhombic structure [...] Read more.

Because antimony trisulfide (Sb₂S₃) is abundant on Earth, non-toxic, and naturally has a low lattice thermal conductivity, it has garnered increasing interest as a possible thermoelectric material. One factor contributing to its anisotropic transport behavior is the orthorhombic structure in which Sb₂S₃ crystallizes, which is made up of one-dimensional (Sb₄S₄)_n ribbons. For thermoelectric energy conversion, its comparatively broad band gap (~1.5–1.7 eV) leads to a high Seebeck coefficient, usually in the 200–600 μV/K range. However, due to its inherently low carrier mobility, pristine Sb₂S₃ exhibits poor electrical conductivity, thereby restricting its power factor. Recent research indicates that composite engineering, nanostructuring, and doping (e.g., with elements such as Ln, As, Se, Ni, Zn, and Fe) can enhance the dimensionless figure of merit (ZT) by increasing carrier concentration while suppressing phonon transport. ZT values in bulk Sb₂S₃ range from 0.1 to 0.2 to approximately 0.5 in optimized nanostructured or doped systems. Higher ZT values (>1) are expected to be possible with advanced band engineering and defect management. According to these results, Sb₂S₃ is a promising mid-temperature thermoelectric material that can be used for waste-heat recovery and possibly integrated into hybrid photovoltaic–thermoelectric systems. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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11 pages, 4912 KB

Open AccessProceeding Paper

Design and Energy Cost Evaluation of a Portable Cold Storage Unit for Tuna Fish Using the LCOE Approach

by Muhammad Arif Budiyanto, Xaviera Fidela, Wardi and Renaldi

Eng. Proc. 2026, 144(1), 18; https://doi.org/10.3390/engproc2026144018 - 20 Aug 2026

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Abstract

Indonesia has significant fisheries potential; however, limited cold chain infrastructure at small-scale fishing ports contributes to post-harvest losses and quality degradation of fishery products. This study presents the design and techno-economic assessment of a modular portable cold storage unit for tuna fisheries integrated [...] Read more.

Indonesia has significant fisheries potential; however, limited cold chain infrastructure at small-scale fishing ports contributes to post-harvest losses and quality degradation of fishery products. This study presents the design and techno-economic assessment of a modular portable cold storage unit for tuna fisheries integrated with renewable energy systems. The system (7 × 3 × 5 m) uses polyurethane sandwich panels and requires a maximum cooling load of 6.14 kW with peak power consumption of 7.93 kW. The estimated capital cost is approximately USD 34,100, while the operational cost is about USD 198 per cycle. A comparative analysis using the Levelized Cost of Energy (LCOE) method indicates that diesel generators provide the lowest cost at approximately USD 0.56/kWh, whereas standalone photovoltaic (PV) systems exhibit the highest cost at around USD 0.89/kWh. However, hybrid PV systems offer the best balance between cost efficiency and environmental performance by reducing carbon emissions. The results demonstrate that integrating hybrid renewable energy into modular cold storage enhances cold chain reliability, reduces fish losses, and supports sustainable coastal development. This approach contributes to low-carbon fisheries infrastructure and aligns with global sustainability and renewable energy transition goals. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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8 pages, 1008 KB

Open AccessProceeding Paper

Technical Design and Investment Feasibility Analysis of a 30-GT Steel Fishing Vessel for Operations in the Java Sea

by Muhammad Arif Budiyanto and Achmad Fatchur Utama

Eng. Proc. 2026, 144(1), 19; https://doi.org/10.3390/engproc2026144019 - 20 Aug 2026

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Abstract

Indonesia, as one of the world’s largest maritime nations, has substantial fishery potential in the Java Sea, yet fleet modernization remains limited. This study proposes a novel integrated framework that combines computer-aided vessel design with techno-economic feasibility analysis for a 30-GT steel fishing [...] Read more.

Indonesia, as one of the world’s largest maritime nations, has substantial fishery potential in the Java Sea, yet fleet modernization remains limited. This study proposes a novel integrated framework that combines computer-aided vessel design with techno-economic feasibility analysis for a 30-GT steel fishing vessel as a replacement for traditional wooden fleets. The novelty lies in the simultaneous evaluation of technical design parameters and investment performance within a single framework tailored to small-scale fisheries. The vessel, with principal dimensions of 16 m (L), 4 m (B), 1.85 m (D), and 1.2 m (T), demonstrates adequate stability and operational capability. Financial results show positive NPV and an IRR above the benchmark, confirming technical and economic feasibility. The results confirm that 30-GT steel vessels provide a technically and economically feasible solution to support sustainable fishery development. Full article

(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)

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Eng. Proc., 2026, STR2E 2026

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