Pub Date : 2026-03-01Epub Date: 2026-02-27DOI: 10.1016/j.rineng.2026.109788
Abdellah Belhaouzi , Mohammed Ouassaid , Hamza Sabir
Synchronous machines (SMs) are critical components of power systems, playing a vital role in maintaining supply continuity and system stability. Despite their high reliability, the occurrence of defects remains inevitable and can compromise their performance. It is therefore essential to continuously monitor their condition to detect faults at an early stage to prevent major damage. Such damage not only leads to significant maintenance costs, but also causes unexpected downtime, resulting in high financial losses. This paper aims to identify the most common failure mechanisms affecting the main components of SMs by discussing their root causes and their impact on machine performance. It evaluates conventional diagnostic methods, highlighting their applicability and limitations, and critically examines relevant condition monitoring and fault diagnosis techniques, including signal-processing and artificial intelligence–based approaches. These methods are compared in terms of detection capability, fault localization, severity assessment and industrial deployment. The review also identifies key challenges, including sensitivity to operating conditions, data scarcity, and practical deployment constraints, while highlighting promising directions for improving predictive maintenance.
{"title":"Condition monitoring and fault diagnosis of synchronous machines–A review","authors":"Abdellah Belhaouzi , Mohammed Ouassaid , Hamza Sabir","doi":"10.1016/j.rineng.2026.109788","DOIUrl":"10.1016/j.rineng.2026.109788","url":null,"abstract":"<div><div>Synchronous machines (SMs) are critical components of power systems, playing a vital role in maintaining supply continuity and system stability. Despite their high reliability, the occurrence of defects remains inevitable and can compromise their performance. It is therefore essential to continuously monitor their condition to detect faults at an early stage to prevent major damage. Such damage not only leads to significant maintenance costs, but also causes unexpected downtime, resulting in high financial losses. This paper aims to identify the most common failure mechanisms affecting the main components of SMs by discussing their root causes and their impact on machine performance. It evaluates conventional diagnostic methods, highlighting their applicability and limitations, and critically examines relevant condition monitoring and fault diagnosis techniques, including signal-processing and artificial intelligence–based approaches. These methods are compared in terms of detection capability, fault localization, severity assessment and industrial deployment. The review also identifies key challenges, including sensitivity to operating conditions, data scarcity, and practical deployment constraints, while highlighting promising directions for improving predictive maintenance.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"29 ","pages":"Article 109788"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-29DOI: 10.1016/j.rineng.2025.108919
Pedro Teixeira , Rui Borges Lopes , Leonor Teixeira
Artificial Intelligence (AI) is transforming modern seaports. One key innovation is the implementation of AI-driven smart pre-gate systems, which streamline truck flow and enhance the overall efficiency of container terminals. There has been an increase in studies on the impact of AI integration in seaports, but few research studies focus on initiatives applied specifically to smart gates. To address this gap, this study aims to explore the integration of AI in smart gate processes and understand its impact. To reach this goal, a systematic literature review was applied following the PRISMA reporting guidelines. The review involved searching for primary studies in four major databases: SCOPUS, Web of Science, IEEE, and ACM. A snowball search was also used to find more studies. After a screening and selection procedure, 26 papers were deemed relevant and were analyzed through a bibliometric and content analysis. The results reveal that machine/deep learning and temporal models are predominantly used to predict truck arrivals and optimize gate scheduling, while reinforcement learning and computer vision automate vehicle and cargo recognition. Reinforcement learning and digital twin systems are emerging as tools for adaptive control and simulation of complex gate environments. Evidence suggests a dominance of experimental studies, indicating that while the feasibility of AI in smart gates is well-established, integration remains challenging. This review contributes to a clearer understanding of how AI is shaping the next generation of port access infrastructure and identifies gaps and future research directions for the development of smart and interoperable gates in maritime logistics.
人工智能（AI）正在改变现代海港。一项关键创新是实施人工智能驱动的智能预闸系统，该系统简化了卡车流程，提高了集装箱码头的整体效率。关于人工智能整合对海港影响的研究有所增加，但很少有研究关注专门应用于智能门的举措。为了解决这一差距，本研究旨在探索人工智能在智能门流程中的整合，并了解其影响。为了达到这一目标，按照PRISMA报告指南进行了系统的文献综述。该综述包括在四个主要数据库中检索主要研究：SCOPUS、Web of Science、IEEE和ACM。雪球搜索也被用来寻找更多的研究。经过筛选和选择程序，26篇论文被认为是相关的，并通过文献计量学和内容分析进行分析。结果表明，机器/深度学习和时间模型主要用于预测卡车到达和优化出入口调度，而强化学习和计算机视觉则自动识别车辆和货物。强化学习和数字孪生系统正在成为复杂闸门环境自适应控制和仿真的工具。有证据表明，实验研究占主导地位，表明尽管人工智能在智能门中的可行性已经确立，但整合仍然具有挑战性。这一综述有助于更清楚地了解人工智能如何塑造下一代港口接入基础设施，并确定海上物流中智能和可互操作门的发展差距和未来研究方向。
{"title":"The integration of Artificial Intelligence in seaports' smart gate processes: Evidence based on a systematic literature review","authors":"Pedro Teixeira , Rui Borges Lopes , Leonor Teixeira","doi":"10.1016/j.rineng.2025.108919","DOIUrl":"10.1016/j.rineng.2025.108919","url":null,"abstract":"<div><div>Artificial Intelligence (AI) is transforming modern seaports. One key innovation is the implementation of AI-driven smart pre-gate systems, which streamline truck flow and enhance the overall efficiency of container terminals. There has been an increase in studies on the impact of AI integration in seaports, but few research studies focus on initiatives applied specifically to smart gates. To address this gap, this study aims to explore the integration of AI in smart gate processes and understand its impact. To reach this goal, a systematic literature review was applied following the PRISMA reporting guidelines. The review involved searching for primary studies in four major databases: SCOPUS, Web of Science, IEEE, and ACM. A snowball search was also used to find more studies. After a screening and selection procedure, 26 papers were deemed relevant and were analyzed through a bibliometric and content analysis. The results reveal that machine/deep learning and temporal models are predominantly used to predict truck arrivals and optimize gate scheduling, while reinforcement learning and computer vision automate vehicle and cargo recognition. Reinforcement learning and digital twin systems are emerging as tools for adaptive control and simulation of complex gate environments. Evidence suggests a dominance of experimental studies, indicating that while the feasibility of AI in smart gates is well-established, integration remains challenging. This review contributes to a clearer understanding of how AI is shaping the next generation of port access infrastructure and identifies gaps and future research directions for the development of smart and interoperable gates in maritime logistics.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"29 ","pages":"Article 108919"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-02DOI: 10.1016/j.rineng.2025.108549
Minh Duc Le , Cong Tin Le , Quang Phat Vu , Van Phuc Nguyen
Analysis of truck chassis strength is an important aspect of the truck design process. For an experimental study, the measurements might be considered complex and expensive, with multiple prototypes required. To overcome this existing situation, a computational method is proposed for the analysis of the truck chassis strength deviation. In this study, multiple calculations were performed: (1) a multibody dynamics simulation used to replace experiments in determining the truck reaction force; (2) a truck chassis strength analysis model performed with the finite element method (FEM) combined with an inertia compensation technique; and (3) a design of experiment technique with an FEM model to predict the response deflection of the structure due to deviation of structural parameters. The analysis using the HyperStudy solver was verified against a mathematical model to increase confidence in the simulation results. The result shows the reliability of the OptiStruct solver and the HyperStudy models were about 98.66 % and 98.39 % compared to that of the verified mathematical model, respectively. The validation results exhibit that the maximum deviation between the HyperStudy computational model and the mathematical model is from 5.85 to 6.16 mm for the thickness variable, corresponding to 1.61 %. For the elastic modulus E, varying from 193,820 MPa to 213,447 MPa gives a maximum deviation of 1.54 %. The proposed computational model proves to be highly reliable and suitable for analyzing the truck chassis strength deviation. The variation in thickness and Young's modulus within the tolerance range does not show any adverse effect on the chassis strength. The maximum deflection stress value is equivalent to 3.75 %, while the displacement is equivalent to 3.15 %.
{"title":"A computational model-based prediction approach for truck chassis strength deviation range","authors":"Minh Duc Le , Cong Tin Le , Quang Phat Vu , Van Phuc Nguyen","doi":"10.1016/j.rineng.2025.108549","DOIUrl":"10.1016/j.rineng.2025.108549","url":null,"abstract":"<div><div>Analysis of truck chassis strength is an important aspect of the truck design process. For an experimental study, the measurements might be considered complex and expensive, with multiple prototypes required. To overcome this existing situation, a computational method is proposed for the analysis of the truck chassis strength deviation. In this study, multiple calculations were performed: (1) a multibody dynamics simulation used to replace experiments in determining the truck reaction force; (2) a truck chassis strength analysis model performed with the finite element method (FEM) combined with an inertia compensation technique; and (3) a design of experiment technique with an FEM model to predict the response deflection of the structure due to deviation of structural parameters. The analysis using the HyperStudy solver was verified against a mathematical model to increase confidence in the simulation results. The result shows the reliability of the OptiStruct solver and the HyperStudy models were about 98.66 % and 98.39 % compared to that of the verified mathematical model, respectively. The validation results exhibit that the maximum deviation between the HyperStudy computational model and the mathematical model is from 5.85 to 6.16 mm for the thickness variable, corresponding to 1.61 %. For the elastic modulus <em>E</em>, varying from 193,820 MPa to 213,447 MPa gives a maximum deviation of 1.54 %. The proposed computational model proves to be highly reliable and suitable for analyzing the truck chassis strength deviation. The variation in thickness and Young's modulus within the tolerance range does not show any adverse effect on the chassis strength. The maximum deflection stress value is equivalent to 3.75 %, while the displacement is equivalent to 3.15 %.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"29 ","pages":"Article 108549"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-11-27DOI: 10.1016/j.rineng.2025.108451
Ahmed Omera , Guillermo Zaragoza , Mohammed Antar
Advancing membrane distillation (MD) requires innovative module configurations that enhance permeate flux and energy efficiency. Multi-envelope MD operated in vacuum-assisted air-gap mode (V-AGMD) has emerged as an effective alternative to the established conventional thermal desalination, offering higher energy efficiency and modular scalability. In this study, a physics-based model of a pilot-scale spiral-wound V-AGMD module is used to design and evaluate single- and two-stage energy-recovery configurations (parallel/series), quantifying gains in gained output ratio (GOR), reductions in specific thermal energy consumption (STEC), and productivity trade-offs. The model's predictions demonstrate close agreement with experimental findings, based on approximately 72 measurements originating from independent sources cited in the literature. A comprehensive physical analysis examines variations in air gap width, membrane length, both low and high operating conditions, such as temperature as well as the feed rate. GOR values initially increase with membrane channel length; they reach a peak before declining due to the balance between vapor transport irreversibility and the diminishing temperature gradient through the membrane. The operating and design conditions envelope exerts a strong, quantifiable influence on performance. Fresh water productivity shows a remarkable increase of 788.1 % with a rising feed flow rate from 100 to1000 l/h, while at 40 °C. Narrowing the air gap from 8.0 to 0.75 mm decreases the STEC from 1295 to 365.9 kWh/m³. The considerable result achieved in this work is observed in the detailed analysis of energy recovery methods that leads to a substantially high GOR. In a two-stage series configuration, the AS24 modules achieve the lowest STEC of 34.8 kWh/m³, corresponding to GOR of 18.96.
{"title":"Effect of energy recovery on the performance of a spiral wound vacuum assisted air gap membrane distillation system","authors":"Ahmed Omera , Guillermo Zaragoza , Mohammed Antar","doi":"10.1016/j.rineng.2025.108451","DOIUrl":"10.1016/j.rineng.2025.108451","url":null,"abstract":"<div><div>Advancing membrane distillation (MD) requires innovative module configurations that enhance permeate flux and energy efficiency. Multi-envelope MD operated in vacuum-assisted air-gap mode (V-AGMD) has emerged as an effective alternative to the established conventional thermal desalination, offering higher energy efficiency and modular scalability. In this study, a physics-based model of a pilot-scale spiral-wound V-AGMD module is used to design and evaluate single- and two-stage energy-recovery configurations (parallel/series), quantifying gains in gained output ratio (GOR), reductions in specific thermal energy consumption (STEC), and productivity trade-offs. The model's predictions demonstrate close agreement with experimental findings, based on approximately 72 measurements originating from independent sources cited in the literature. A comprehensive physical analysis examines variations in air gap width, membrane length, both low and high operating conditions, such as temperature as well as the feed rate. GOR values initially increase with membrane channel length; they reach a peak before declining due to the balance between vapor transport irreversibility and the diminishing temperature gradient through the membrane. The operating and design conditions envelope exerts a strong, quantifiable influence on performance. Fresh water productivity shows a remarkable increase of 788.1 % with a rising feed flow rate from 100 to1000 l/h, while at 40 °C. Narrowing the air gap from 8.0 to 0.75 mm decreases the STEC from 1295 to 365.9 kWh/m³. The considerable result achieved in this work is observed in the detailed analysis of energy recovery methods that leads to a substantially high GOR. In a two-stage series configuration, the AS24 modules achieve the lowest STEC of 34.8 kWh/m³, corresponding to GOR of 18.96.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"29 ","pages":"Article 108451"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-11-25DOI: 10.1016/j.rineng.2025.108412
Tajudeen A. Oyehan, Christian Pfrang, Eugenia Valsami-Jones
The removal of synthetic dyes from wastewater remains a major environmental challenge, requiring efficient and sustainable adsorbents. In this study, mesoporous silica nanoparticles (MSNs) were synthesised via a low-temperature route and functionalised with carboxyl (COOH) and thiol (SH) groups through co-condensation to enhance adsorption of cationic and anionic dyes. Structural, thermal, and surface analyses confirmed successful synthesis and retention of functional groups. Batch adsorption experiments under varying pH, adsorbent dosage, dye concentration and contact time showed that MSNCOOH achieved the highest adsorption capacity (149.3 mg g-1 for crystal violet; 10.7 mg g-1 for methyl orange) and the fastest kinetics (two-fold faster than bare MSNs). Kinetic, isotherm, and zeta potential analyses indicated that electrostatic interactions and surface chemistry governed removal performance. A machine learning approach using the Random Forest (RF) model was applied to predict adsorption efficiency and evaluate the relative influence of experimental parameters. The RF model achieved high accuracy (r ≥ 0.97), effectively capturing complex adsorption trends and enabling rapid performance estimation without extensive laboratory trials. Permutation importance analysis identified functionalisation and pH as the dominant factors. Overall, this study optimised functional group loading for efficient dye removal, advancing both the scientific understanding and practical engineering of MSN-based adsorbents. It also highlights the combined benefits of low-temperature synthesis and ML-driven insights, positioning MSNCOOH as a promising and sustainable adsorbent for wastewater treatment. Future scale-up studies and integration with continuous treatment systems may accelerate the translation of this approach to real-world applications.
{"title":"Functionalised mesoporous silica nanoparticles for dye removal: Experimental insights and predictive modelling","authors":"Tajudeen A. Oyehan, Christian Pfrang, Eugenia Valsami-Jones","doi":"10.1016/j.rineng.2025.108412","DOIUrl":"10.1016/j.rineng.2025.108412","url":null,"abstract":"<div><div>The removal of synthetic dyes from wastewater remains a major environmental challenge, requiring efficient and sustainable adsorbents. In this study, mesoporous silica nanoparticles (MSNs) were synthesised via a low-temperature route and functionalised with carboxyl (COOH) and thiol (SH) groups through co-condensation to enhance adsorption of cationic and anionic dyes. Structural, thermal, and surface analyses confirmed successful synthesis and retention of functional groups. Batch adsorption experiments under varying pH, adsorbent dosage, dye concentration and contact time showed that MSN<img>COOH achieved the highest adsorption capacity (149.3 mg g<sup>-1</sup> for crystal violet; 10.7 mg g<sup>-1</sup> for methyl orange) and the fastest kinetics (two-fold faster than bare MSNs). Kinetic, isotherm, and zeta potential analyses indicated that electrostatic interactions and surface chemistry governed removal performance. A machine learning approach using the Random Forest (RF) model was applied to predict adsorption efficiency and evaluate the relative influence of experimental parameters. The RF model achieved high accuracy (<em>r</em> ≥ 0.97), effectively capturing complex adsorption trends and enabling rapid performance estimation without extensive laboratory trials. Permutation importance analysis identified functionalisation and pH as the dominant factors. Overall, this study optimised functional group loading for efficient dye removal, advancing both the scientific understanding and practical engineering of MSN-based adsorbents. It also highlights the combined benefits of low-temperature synthesis and ML-driven insights, positioning MSN<img>COOH as a promising and sustainable adsorbent for wastewater treatment. Future scale-up studies and integration with continuous treatment systems may accelerate the translation of this approach to real-world applications.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"29 ","pages":"Article 108412"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growing production of industrial byproducts such as pond ash and fly ash from thermal power plants presents a major waste management challenge. Integrating these byproducts into controlled low-strength materials (CLSM) offers a sustainable solution for backfilling behind retaining walls, tunnels, and utility trenches. For applications requiring higher strength, CLSM mixtures must achieve compressive strengths above 0.7 MPa after 28 days. This study investigates the effects of adding superplasticizers and lime to conventional CLSM materials through experimental work to develop high-strength CLSM mixtures. Results show a significant improvement in compressive strength with these additives, a finding not previously reported. To complement the experiments, machine learning models were developed to predict the unconfined compressive strength (UCS) of CLSM based on varying proportions of cement, lime, superplasticizers (SP), and pond ash as traditional experimental and empirical approaches are limited in capturing nonlinear interactions among mix parameters. A comprehensive dataset was created from systematic variations in mix proportions and corresponding strength measurements. Four predictive models XGBoost, XGBoost-GWO, XGBoost-PSO, and XGBoost-SSO were trained and tested. The XGBoost-SSO model achieved the best performance with R² values of 0.990 (training), 0.979 (validation), and 0.974 (testing), along with the lowest RMSE (0.026 MPa) and MAE (0.019 MPa) in the testing phase. Regression and REC analyses confirmed its superior predictive capability. Sensitivity analysis identified pond ash (55 %) and cement (17.6 %) as the most influential factors. A user-friendly GUI tool was also developed for real-time UCS prediction and data-driven mix optimization.
{"title":"Optimization of pond-ash-based controlled low-strength materials with lime and superplasticizer via experiments and supervised machine learning","authors":"Divesh Ranjan Kumar , Lini Dev Kannari , Teerapong Senjuntichai , Sakdirat Kaewunruen","doi":"10.1016/j.rineng.2025.108476","DOIUrl":"10.1016/j.rineng.2025.108476","url":null,"abstract":"<div><div>The growing production of industrial byproducts such as pond ash and fly ash from thermal power plants presents a major waste management challenge. Integrating these byproducts into controlled low-strength materials (CLSM) offers a sustainable solution for backfilling behind retaining walls, tunnels, and utility trenches. For applications requiring higher strength, CLSM mixtures must achieve compressive strengths above 0.7 MPa after 28 days. This study investigates the effects of adding superplasticizers and lime to conventional CLSM materials through experimental work to develop high-strength CLSM mixtures. Results show a significant improvement in compressive strength with these additives, a finding not previously reported. To complement the experiments, machine learning models were developed to predict the unconfined compressive strength (UCS) of CLSM based on varying proportions of cement, lime, superplasticizers (SP), and pond ash as traditional experimental and empirical approaches are limited in capturing nonlinear interactions among mix parameters. A comprehensive dataset was created from systematic variations in mix proportions and corresponding strength measurements. Four predictive models XGBoost, XGBoost-GWO, XGBoost-PSO, and XGBoost-SSO were trained and tested. The XGBoost-SSO model achieved the best performance with R² values of 0.990 (training), 0.979 (validation), and 0.974 (testing), along with the lowest RMSE (0.026 MPa) and MAE (0.019 MPa) in the testing phase. Regression and REC analyses confirmed its superior predictive capability. Sensitivity analysis identified pond ash (55 %) and cement (17.6 %) as the most influential factors. A user-friendly GUI tool was also developed for real-time UCS prediction and data-driven mix optimization.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"29 ","pages":"Article 108476"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-08DOI: 10.1016/j.rineng.2026.108993
Mohammed Ghazal , Malaz Osman , Marah Alhalabi , Abdalla Gad , Maha Yaghi , Mohamad Ramadan , Mohammad Alkhedher
Consuming large quantities of harmful fossil fuels is leading to significant disturbances in the ecosystem’s health, increasing global Greenhouse Gas (GHG) emissions per capita of approximately 8.3% between 1990 and 2022. Increasing temperatures, frequent natural disasters, and rising sea levels are among the consequences of climate change that threaten Earth’s sustainability. In this context, green hydrogen has been proven to be a sustainable, clean, and environmentally friendly solution to such challenges. Hydrogen can play a vital role in the storage, transportation, or provision of alternative energy. However, it contributes only 2% of global hydrogen production, whereas fossil fuels account for over 96%. This paper highlights the potential and crucial need to integrate green hydrogen into the current and future energy infrastructure, ensuring a smooth transition towards climate-resilient and environmentally safe systems. It also analyzes the components of a hydrogen-based economy and infrastructure, including green hydrogen production, storage, transportation, and utilization. Solar-powered systems, biomass gasification, wind or hybrid systems, and geothermal methods are examined and shown to improve production efficiency by 64% and reduce GHG emissions by 94%. Green hydrogen production methods, including the work presented, aim to identify the key advantages, challenges, limitations, and opportunities that hydrogen can bring to the global economy, as well as the potential of green hydrogen to provide a clean earth for future generations. This review highlights recent advancements in green hydrogen production and utilization technologies and identifies gaps that require attention from research, industry, society, and government. The work presented in this review is based on an analysis of over 140 scholarly publications spanning 2010 to 2025, highlighting current developments in the adoption of the hydrogen economy.
{"title":"Advancements and challenges in green hydrogen production, storage, transportation, and utilization for climate-resilient energy systems","authors":"Mohammed Ghazal , Malaz Osman , Marah Alhalabi , Abdalla Gad , Maha Yaghi , Mohamad Ramadan , Mohammad Alkhedher","doi":"10.1016/j.rineng.2026.108993","DOIUrl":"10.1016/j.rineng.2026.108993","url":null,"abstract":"<div><div>Consuming large quantities of harmful fossil fuels is leading to significant disturbances in the ecosystem’s health, increasing global Greenhouse Gas (GHG) emissions per capita of approximately 8.3% between 1990 and 2022. Increasing temperatures, frequent natural disasters, and rising sea levels are among the consequences of climate change that threaten Earth’s sustainability. In this context, green hydrogen has been proven to be a sustainable, clean, and environmentally friendly solution to such challenges. Hydrogen can play a vital role in the storage, transportation, or provision of alternative energy. However, it contributes only 2% of global hydrogen production, whereas fossil fuels account for over 96%. This paper highlights the potential and crucial need to integrate green hydrogen into the current and future energy infrastructure, ensuring a smooth transition towards climate-resilient and environmentally safe systems. It also analyzes the components of a hydrogen-based economy and infrastructure, including green hydrogen production, storage, transportation, and utilization. Solar-powered systems, biomass gasification, wind or hybrid systems, and geothermal methods are examined and shown to improve production efficiency by 64% and reduce GHG emissions by 94%. Green hydrogen production methods, including the work presented, aim to identify the key advantages, challenges, limitations, and opportunities that hydrogen can bring to the global economy, as well as the potential of green hydrogen to provide a clean earth for future generations. This review highlights recent advancements in green hydrogen production and utilization technologies and identifies gaps that require attention from research, industry, society, and government. The work presented in this review is based on an analysis of over 140 scholarly publications spanning 2010 to 2025, highlighting current developments in the adoption of the hydrogen economy.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"29 ","pages":"Article 108993"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-23DOI: 10.1016/j.rineng.2025.108854
Hui Kong, Qilong Li
The increasing demand for renewable energy sources has led to significant developments in the field of photovoltaics. Hybrid systems, which combine multiple energy sources, offer enhanced efficiency, reliability, and sustainability. This study will explore various aspects of hybrid photovoltaic systems, including their components, working principles, benefits, challenges, and recent advancements, providing an extensive overview of the advancements in hybrid photovoltaic energy systems. Additionally, it will discuss the potential applications and prospects of these systems. It highlights the importance of interdisciplinary research efforts to address challenges related to materials, integration strategies, performance enhancement techniques, applications, and scalability. Additionally, this study is dedicated to exploring the latest advancements in materials, technologies, and integration strategies that aim to optimize the performance and broaden the applications of hybrid photovoltaics, while also highlighting the significant progress made in this field. The continuous development of hybrid photovoltaic systems holds great promise for meeting the increasing global demand for clean and sustainable energy sources.
{"title":"Next-generation hybrid photovoltaic energy systems: Research and developments","authors":"Hui Kong, Qilong Li","doi":"10.1016/j.rineng.2025.108854","DOIUrl":"10.1016/j.rineng.2025.108854","url":null,"abstract":"<div><div>The increasing demand for renewable energy sources has led to significant developments in the field of photovoltaics. Hybrid systems, which combine multiple energy sources, offer enhanced efficiency, reliability, and sustainability. This study will explore various aspects of hybrid photovoltaic systems, including their components, working principles, benefits, challenges, and recent advancements, providing an extensive overview of the advancements in hybrid photovoltaic energy systems. Additionally, it will discuss the potential applications and prospects of these systems. It highlights the importance of interdisciplinary research efforts to address challenges related to materials, integration strategies, performance enhancement techniques, applications, and scalability. Additionally, this study is dedicated to exploring the latest advancements in materials, technologies, and integration strategies that aim to optimize the performance and broaden the applications of hybrid photovoltaics, while also highlighting the significant progress made in this field. The continuous development of hybrid photovoltaic systems holds great promise for meeting the increasing global demand for clean and sustainable energy sources.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"29 ","pages":"Article 108854"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-06DOI: 10.1016/j.rineng.2026.109457
Sami Assad , Nor Ashidi Mat Isa , Sami Abdulla Mohsen Saleh
Hybrid CNN-Transformer models have emerged as a promising approach for industrial defect detection, aiming to leverage the complementary strengths of Convolutional Neural Networks (CNNs) and Transformers. This systematic review proposes a dual-path taxonomy to classify hybrid models based on their fusion strategies, namely, structural and modular fusion. Structural fusion strategies include parallel, sequential, and hierarchical fusion, focusing on the information flow between architectural components. Modular fusion strategies involve integrating transformer components into specific stages of object detection architectures, such as the backbone, neck, head, or multi-stage embedding. This review presents a systematic analysis of hybrid models across various industrial sectors, including Printed Circuit Boards (PCBs), steel surfaces, fabric textiles, transmission lines, and railways. A comparative assessment of deployment feasibility, considering inference latency, model size, and edge readiness, is also presented. This review identifies research gaps and provides guidance on future directions, including lightweight design, synthetic data expansion, and domain-transfer techniques. The findings highlight the potential of hybrid CNN-Transformer models to improve defect detection accuracy while addressing the challenges of small, occluded, and irregular defects in complex industrial environments. However, further research is required to optimize model efficiency, generalization, and real-world deployment feasibility.
{"title":"Hybrid CNN-Transformer models for industrial defect detection: A systematic review","authors":"Sami Assad , Nor Ashidi Mat Isa , Sami Abdulla Mohsen Saleh","doi":"10.1016/j.rineng.2026.109457","DOIUrl":"10.1016/j.rineng.2026.109457","url":null,"abstract":"<div><div>Hybrid CNN-Transformer models have emerged as a promising approach for industrial defect detection, aiming to leverage the complementary strengths of Convolutional Neural Networks (CNNs) and Transformers. This systematic review proposes a dual-path taxonomy to classify hybrid models based on their fusion strategies, namely, structural and modular fusion. Structural fusion strategies include parallel, sequential, and hierarchical fusion, focusing on the information flow between architectural components. Modular fusion strategies involve integrating transformer components into specific stages of object detection architectures, such as the backbone, neck, head, or multi-stage embedding. This review presents a systematic analysis of hybrid models across various industrial sectors, including Printed Circuit Boards (PCBs), steel surfaces, fabric textiles, transmission lines, and railways. A comparative assessment of deployment feasibility, considering inference latency, model size, and edge readiness, is also presented. This review identifies research gaps and provides guidance on future directions, including lightweight design, synthetic data expansion, and domain-transfer techniques. The findings highlight the potential of hybrid CNN-Transformer models to improve defect detection accuracy while addressing the challenges of small, occluded, and irregular defects in complex industrial environments. However, further research is required to optimize model efficiency, generalization, and real-world deployment feasibility.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"29 ","pages":"Article 109457"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-02DOI: 10.1016/j.rineng.2026.109416
Jia Li , Yelriza Yeszhan , Kalampyr Bexeitova , Alzhan Baimenov , Kenes Kudaibergenov , Mohd Ridhwan Adam , Jechan Lee , Seitkhan Azat
Developing green, efficient, low-cost, and sustainable multifunctional water treatment materials has become an important research direction. Bio-based SiO₂ has attracted extensive attention due to its abundant sources, low cost, and high specific surface area. However, its limited functionality makes it difficult to achieve efficient removal, degradation, and antibacterial performance in water treatment. By incorporating metal oxides, the composites exhibit synergistic adsorption, photocatalytic, and antibacterial performance, with removal performance 1.5 to 5 times higher than that of pure SiO₂ and maintain good removal performance across various pollutant systems compared with traditional adsorbents, while enhancing their reusability. The review explores the research and application of bio-based SiO₂-metal oxide composites in water treatment. To this end, the influence of different methods for extracting bio-based SiO₂ from agricultural waste on the structure and properties of the materials is systematically surveyed. The preparation and characterization of bio-based SiO₂-metal oxide composites for structural optimization direction and performance regulation mechanisms are elaborated on heavy metal removal, organic pollutant degradation, and antibacterial purification. Emphasis is placed on structure–performance relationships, synergistic mechanisms, and engineering feasibility. Overall, this review presents a systematic research approach for the combination of metal oxides and bio-based SiO₂ to guide the direction toward sustainable water purification technologies.
{"title":"Recent progress in bio-based SiO₂–metal oxide composites for water treatment applications: A review","authors":"Jia Li , Yelriza Yeszhan , Kalampyr Bexeitova , Alzhan Baimenov , Kenes Kudaibergenov , Mohd Ridhwan Adam , Jechan Lee , Seitkhan Azat","doi":"10.1016/j.rineng.2026.109416","DOIUrl":"10.1016/j.rineng.2026.109416","url":null,"abstract":"<div><div>Developing green, efficient, low-cost, and sustainable multifunctional water treatment materials has become an important research direction. Bio-based SiO₂ has attracted extensive attention due to its abundant sources, low cost, and high specific surface area. However, its limited functionality makes it difficult to achieve efficient removal, degradation, and antibacterial performance in water treatment. By incorporating metal oxides, the composites exhibit synergistic adsorption, photocatalytic, and antibacterial performance, with removal performance 1.5 to 5 times higher than that of pure SiO₂ and maintain good removal performance across various pollutant systems compared with traditional adsorbents, while enhancing their reusability. The review explores the research and application of bio-based SiO₂-metal oxide composites in water treatment. To this end, the influence of different methods for extracting bio-based SiO₂ from agricultural waste on the structure and properties of the materials is systematically surveyed. The preparation and characterization of bio-based SiO₂-metal oxide composites for structural optimization direction and performance regulation mechanisms are elaborated on heavy metal removal, organic pollutant degradation, and antibacterial purification. Emphasis is placed on structure–performance relationships, synergistic mechanisms, and engineering feasibility. Overall, this review presents a systematic research approach for the combination of metal oxides and bio-based SiO₂ to guide the direction toward sustainable water purification technologies.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"29 ","pages":"Article 109416"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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