Pub Date : 2025-09-13DOI: 10.1016/j.clet.2025.101074
Harn Wei Kua, Anqi Shi , Vignesh Kajandran, Charlene, Tiam Weng Mark Lam, Abhimanyu Goel, De Hui Alwin Wong, Peak Kee Lim, Layla Harris Kasee, Xi Wen Ong, Ghasan Fahem Huseien, Alexander Lin
This study investigates the effects of partial substitution of Ordinary Portland Cement with different proportions of non-calcined marine clay and different grades (by particle size) of Ground Limestone to formulate 3D-printable concrete. Non-calcined clay was used because of its lower energy requirement than calcined clay. The rheology of the mixes was evaluated by extrudability, tack, Large Amplitude Oscillatory shear, and Logarithmic Stress Ramp tests. Their mechanical performance was evaluated with respect to compressive, splitting, shear, and three-point bending (flexural) strengths. Results show that adding only about 1 % of oven-dried (low temperature) marine clay and 19 % of limestone improve cohesion and build-up of static yield strength while ensuring extrudability. This study hopes to pave the way for more studies on using non-calcined marine clay as a more sustainable option for construction 3D printing.
{"title":"Toward sustainable construction 3D printing: limestone and non-calcined recycled marine clay as partial cement replacement","authors":"Harn Wei Kua, Anqi Shi , Vignesh Kajandran, Charlene, Tiam Weng Mark Lam, Abhimanyu Goel, De Hui Alwin Wong, Peak Kee Lim, Layla Harris Kasee, Xi Wen Ong, Ghasan Fahem Huseien, Alexander Lin","doi":"10.1016/j.clet.2025.101074","DOIUrl":"10.1016/j.clet.2025.101074","url":null,"abstract":"<div><div>This study investigates the effects of partial substitution of Ordinary Portland Cement with different proportions of non-calcined marine clay and different grades (by particle size) of Ground Limestone to formulate 3D-printable concrete. Non-calcined clay was used because of its lower energy requirement than calcined clay. The rheology of the mixes was evaluated by extrudability, tack, Large Amplitude Oscillatory shear, and Logarithmic Stress Ramp tests. Their mechanical performance was evaluated with respect to compressive, splitting, shear, and three-point bending (flexural) strengths. Results show that adding only about 1 % of oven-dried (low temperature) marine clay and 19 % of limestone improve cohesion and build-up of static yield strength while ensuring extrudability. This study hopes to pave the way for more studies on using non-calcined marine clay as a more sustainable option for construction 3D printing.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"29 ","pages":"Article 101074"},"PeriodicalIF":6.5,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269617","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 increasing discharge of nutrient-rich industrial effluents poses a significant environmental challenge, necessitating efficient and sustainable wastewater treatment strategies. This study developed a living hydrogel-based biofilter incorporating co-immobilized Chlorella sp. and Bacillus subtilis TISTR 1415 to enhance nutrient recovery from secondary industrial effluent from vegetable oil factories. Hydrogels were formulated using guar gum and carrageenan, crosslinked with potassium chloride (KCl), and evaluated for their stability and microbial immobilization efficiency. Among the tested formulations, the hydrogel with 0.3 M KCl exhibited optimal properties, including moderate swelling capacity (∼1,005 % or ∼10 gwater/gdry hydrogel), reduced solubility (∼40 %), and enhanced mechanical stability and crosslinking density, leading to improved porosity and microbial retention. These physicochemical properties facilitated efficient nutrient diffusion and sustained cell viability within the hydrogel matrix. The synthetic co-culture biofilter with a 3:1 ratio of Chlorella sp. to B. subtilis significantly enhanced nutrient removal efficiencies compared to monocultures, achieving 98.68 % ammonium (NH4+), 53.45 % phosphate (PO43−), and 68.60 % COD removal over 7-day trials. The synergistic interaction between microalgae and bacteria facilitated improved nutrient uptake, organic matter degradation, and enhanced effluent treatment performance. Furthermore, pH and dissolved oxygen levels were significantly influenced by microbial activity, with microalgae contributing to oxygen production and pH elevation, while bacteria aided organic matter breakdown. The living hydrogel-based biofilter presents a promising alternative to conventional wastewater treatment methods by harnessing the synergistic interactions between biological processes and hydrogel immobilization technology. This approach enhances effluent quality and contributes to innovative solutions for environmental protection and nutrient recovery.
富含营养物质的工业废水排放的增加对环境构成了重大挑战，因此需要有效和可持续的废水处理战略。以小球藻和枯草芽孢杆菌TISTR 1415为载体，开发了一种活性水凝胶生物过滤器，以提高植物油厂二级工业废水的养分回收率。以瓜尔胶和卡拉胶为原料，与氯化钾交联制备水凝胶，并对其稳定性和微生物固定化效率进行了评价。在测试的配方中，含有0.3 M KCl的水凝胶表现出最佳的性能，包括适度的膨胀能力（~ 1005%或~ 10 gwater/gdry水凝胶），降低溶解度（~ 40%），增强机械稳定性和交联密度，从而改善孔隙度和微生物保留率。这些物理化学性质促进了营养物质在水凝胶基质内的有效扩散和维持细胞活力。与单一培养相比，小球藻与枯草芽孢杆菌比例为3:1的合成共培养生物过滤器显著提高了营养物去除效率，在7天的试验中，铵（NH4+）去除率达到98.68%，磷酸盐（PO43−）去除率达到53.45%，COD去除率达到68.60%。微藻和细菌之间的协同作用促进了养分吸收、有机物降解和污水处理性能的提高。此外，pH和溶解氧水平受微生物活动的显著影响，微藻有助于产氧和pH升高，而细菌有助于有机物分解。水凝胶生物过滤器利用生物过程和水凝胶固定化技术之间的协同作用，为传统的废水处理方法提供了一种有前途的替代方案。这种方法提高了污水的质量，并为环境保护和养分回收提供了创新的解决方案。
{"title":"Bio-hydrogel formulation for co-immobilization of microalgae and bacteria in living biofilters for nutrient recovery from secondary industrial effluents","authors":"Chalampol Janpum , Jagroop Pandhal , Nuttapon Pombubpa , Tanakit Komkhum , Chonnikarn Sirichan , Piyakorn Srichuen , Pichaya In-na","doi":"10.1016/j.clet.2025.101075","DOIUrl":"10.1016/j.clet.2025.101075","url":null,"abstract":"<div><div>The increasing discharge of nutrient-rich industrial effluents poses a significant environmental challenge, necessitating efficient and sustainable wastewater treatment strategies. This study developed a living hydrogel-based biofilter incorporating co-immobilized <em>Chlorella</em> sp. and <em>Bacillus subtilis</em> TISTR 1415 to enhance nutrient recovery from secondary industrial effluent from vegetable oil factories. Hydrogels were formulated using guar gum and carrageenan, crosslinked with potassium chloride (KCl), and evaluated for their stability and microbial immobilization efficiency. Among the tested formulations, the hydrogel with 0.3 M KCl exhibited optimal properties, including moderate swelling capacity (∼1,005 % or ∼10 g<sub>water</sub>/g<sub>dry hydrogel</sub>), reduced solubility (∼40 %), and enhanced mechanical stability and crosslinking density, leading to improved porosity and microbial retention. These physicochemical properties facilitated efficient nutrient diffusion and sustained cell viability within the hydrogel matrix. The synthetic co-culture biofilter with a 3:1 ratio of <em>Chlorella</em> sp. to <em>B. subtilis</em> significantly enhanced nutrient removal efficiencies compared to monocultures, achieving 98.68 % ammonium (NH<sub>4</sub><sup>+</sup>), 53.45 % phosphate (PO<sub>4</sub><sup>3−</sup>), and 68.60 % COD removal over 7-day trials. The synergistic interaction between microalgae and bacteria facilitated improved nutrient uptake, organic matter degradation, and enhanced effluent treatment performance. Furthermore, pH and dissolved oxygen levels were significantly influenced by microbial activity, with microalgae contributing to oxygen production and pH elevation, while bacteria aided organic matter breakdown. The living hydrogel-based biofilter presents a promising alternative to conventional wastewater treatment methods by harnessing the synergistic interactions between biological processes and hydrogel immobilization technology. This approach enhances effluent quality and contributes to innovative solutions for environmental protection and nutrient recovery.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"29 ","pages":"Article 101075"},"PeriodicalIF":6.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060739","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 : 2025-09-08DOI: 10.1016/j.clet.2025.101076
Daniel O.F. Silva , Valdir M. Pereira , Antônio C.V. Coelho , Sérgio C. Angulo
Concrete slurry waste (CSW) is a by-product generated from returned concrete and the mixer truck washing process, accounting for approximately 3–5% of total concrete production. Although various strategies for recycling CSW have been developed, large-scale recovery of its anhydrous cement fraction, such implemented in the present study, has not been previously reported. Moreover, prior studies have focused almost exclusively on the use of low-reactivity CSW as a supplementary cementitious material (SCM). In this study, a filtering and rapid drying procedure was applied to CSW upon its return to the ready-mixed concrete plant (RMCP). The influence of the recovery time and type of concrete waste on the preservation of the anhydrous cement fraction was evaluated. The recovered material was subsequently used to partially replace Portland cement in cementitious paste formulations. The samples were characterized using analytical methods, such X-ray fluorescence, HCl leaching assay, thermogravimetric analysis, isothermal calorimetry, and quantitative X-ray diffraction (QXRD). The results demonstrated that the anhydrous cement content in CSW was significant, approximately 30% by mass, due to the efficiency of the filtering and rapid drying process. Furthermore, the time exposure to water had no notable effect on the preserved anhydrous cement fraction. Three CSW samples were selected and incorporated into cement pastes, replacing 35 %–75% (by mass) of Portland cement. The resulting pastes exhibited mechanical strength values comparable to, or statistically equivalent to, those of the reference paste made with 100 % Portland cement. The recovery methodology has potential for the development of zero-waste ready-mix concrete plants, and the low emission concrete formulation proposed in this study enabled a reduction of up to 55% in specific CO2 emissions. This approach could reduce Portland cement consumption by approximately 15% (by mass) in ready-mix operations, contributing significantly to sustainability in the concrete industry.
{"title":"High recovery of anhydrous cement in dried concrete slurry waste for use as supplementary cementitious material in low-CO2 concretes","authors":"Daniel O.F. Silva , Valdir M. Pereira , Antônio C.V. Coelho , Sérgio C. Angulo","doi":"10.1016/j.clet.2025.101076","DOIUrl":"10.1016/j.clet.2025.101076","url":null,"abstract":"<div><div>Concrete slurry waste (CSW) is a by-product generated from returned concrete and the mixer truck washing process, accounting for approximately 3–5% of total concrete production. Although various strategies for recycling CSW have been developed, large-scale recovery of its anhydrous cement fraction, such implemented in the present study, has not been previously reported. Moreover, prior studies have focused almost exclusively on the use of low-reactivity CSW as a supplementary cementitious material (SCM). In this study, a filtering and rapid drying procedure was applied to CSW upon its return to the ready-mixed concrete plant (RMCP). The influence of the recovery time and type of concrete waste on the preservation of the anhydrous cement fraction was evaluated. The recovered material was subsequently used to partially replace Portland cement in cementitious paste formulations. The samples were characterized using analytical methods, such X-ray fluorescence, HCl leaching assay, thermogravimetric analysis, isothermal calorimetry, and quantitative X-ray diffraction (QXRD). The results demonstrated that the anhydrous cement content in CSW was significant, approximately 30% by mass, due to the efficiency of the filtering and rapid drying process. Furthermore, the time exposure to water had no notable effect on the preserved anhydrous cement fraction. Three CSW samples were selected and incorporated into cement pastes, replacing 35 %–75% (by mass) of Portland cement. The resulting pastes exhibited mechanical strength values comparable to, or statistically equivalent to, those of the reference paste made with 100 % Portland cement. The recovery methodology has potential for the development of zero-waste ready-mix concrete plants, and the low emission concrete formulation proposed in this study enabled a reduction of up to 55% in specific CO<sub>2</sub> emissions. This approach could reduce Portland cement consumption by approximately 15% (by mass) in ready-mix operations, contributing significantly to sustainability in the concrete industry.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"29 ","pages":"Article 101076"},"PeriodicalIF":6.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159100","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 : 2025-09-01DOI: 10.1016/j.clet.2025.101063
Nishtha Talwar , Oscar Huerta , Daniela Millán , Paulina Pavez , Mauricio Isaacs , Nicholas M. Holden
Green chemistry promotes the design and application of chemical products and processes that reduce or preferably eliminate the use and generation of hazardous substances. The objective of this research was to evaluate the environmental performance of two methods of producing nanocellulose at the laboratory scale: (i) a conventional sulphuric acid hydrolysis that has been upscaled for industrial use (TRL 8–9); and (ii) the novel approach using the ternary eutectic mixture ChCl: pTSA: PA molar ratio (1:1:1.35) that is currently at TRL 2. The purpose of developing the new approach was to find a better alternative to the conventional process from an environmental perspective. To validate this, life cycle assessment was used to compare conventional vs novel methods with the functional unit of 1 g nanocellulose produced. The system boundary was from cradle to laboratory gate. The results were interpreted to select the best method for laboratory use and to identify design issues to address during upscaling of the novel method. For both methods, conventional and novel, the impact categories selected were climate change (kgCO2 eq), Acidification (kg SO2 eq), Ecotox Air (CTUe) and Eutrophication (kg N eq). To produce 1 g of nanocellulose with sulphuric acid caused a climate impact of between 68 kg CO2 eq (90 % yield) to 105 kg CO2 eq (57 % yield). Produced using DES the climate impact ranged from 85 kg CO2 eq.(90 % yield) to 132 kg CO2 (57 % yield). The results indicated that the novel method created greater impacts over the whole life cycle. Unless significant changes are made during upscaling, the novel method will not make a positive contribution to sustainable, circular bioeconomy. The method does have potential to be improved to reduce impact, including using decarbonised energy, a renewable, bio-based feedstock for the cellulose and choline chloride to improve the overall efficiency of using deep eutectic solvent (DES) at pilot scale. The low TRL life cycle assessment offered insights not possible if only the laboratory stage of the analysis had been considered.
{"title":"Evaluating the environmental impacts of nanocellulose production using conventional and novel approach at laboratory scale","authors":"Nishtha Talwar , Oscar Huerta , Daniela Millán , Paulina Pavez , Mauricio Isaacs , Nicholas M. Holden","doi":"10.1016/j.clet.2025.101063","DOIUrl":"10.1016/j.clet.2025.101063","url":null,"abstract":"<div><div>Green chemistry promotes the design and application of chemical products and processes that reduce or preferably eliminate the use and generation of hazardous substances. The objective of this research was to evaluate the environmental performance of two methods of producing nanocellulose at the laboratory scale: (i) a conventional sulphuric acid hydrolysis that has been upscaled for industrial use (TRL 8–9); and (ii) the novel approach using the ternary eutectic mixture ChCl: pTSA: PA molar ratio (1:1:1.35) that is currently at TRL 2. The purpose of developing the new approach was to find a better alternative to the conventional process from an environmental perspective. To validate this, life cycle assessment was used to compare conventional vs novel methods with the functional unit of 1 g nanocellulose produced. The system boundary was from cradle to laboratory gate. The results were interpreted to select the best method for laboratory use and to identify design issues to address during upscaling of the novel method. For both methods, conventional and novel, the impact categories selected were climate change (kgCO2 eq), Acidification (kg SO2 eq), Ecotox Air (CTUe) and Eutrophication (kg N eq). To produce 1 g of nanocellulose with sulphuric acid caused a climate impact of between 68 kg CO2 eq (90 % yield) to 105 kg CO2 eq (57 % yield). Produced using DES the climate impact ranged from 85 kg CO2 eq.(90 % yield) to 132 kg CO2 (57 % yield). The results indicated that the novel method created greater impacts over the whole life cycle. Unless significant changes are made during upscaling, the novel method will not make a positive contribution to sustainable, circular bioeconomy. The method does have potential to be improved to reduce impact, including using decarbonised energy, a renewable, bio-based feedstock for the cellulose and choline chloride to improve the overall efficiency of using deep eutectic solvent (DES) at pilot scale. The low TRL life cycle assessment offered insights not possible if only the laboratory stage of the analysis had been considered.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101063"},"PeriodicalIF":6.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019214","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 : 2025-09-01DOI: 10.1016/j.clet.2025.101072
Ali Majnoon , Amirali Saifoddin
Accurate forecasting of electricity consumption is essential for sustainable urban planning, particularly in fast-growing cities like Tehran. Conventional models often fail to adequately capture the intricate relationships between environmental factors and energy demand. To overcome these limitations, this study applies advanced AI techniques such as Neural Networks, Random Forest Regression, and Gradient Boosting, using a comprehensive dataset (2000–2022) that integrates meteorological, environmental, and fuel consumption variables to enhance predictive performance. Random Forest Regression achieved the highest accuracy, with an R2 0.9835 and MSE of 0.0165, explaining 98.35 % of the variation in electricity consumption. Feature engineering substantially improved model accuracy, highlighting temperature variables (T2M, T2M_MAX, T2M_MIN) and fuel consumption as the most influential predictors. Correlation analysis revealed strong associations between environmental factors and electricity demand. Using Sequential Least Squares Programming (SLSQP) optimization, the study determined conditions that reduced electricity consumption to 1.09 million kWh. These findings highlight the value of AI models in enhancing forecasting accuracy and supporting efficient energy planning. Ensemble learning and optimization methods strengthen sustainable energy management. However, reliance on historical data and neglect of socio-economic factors may constrain the models’ adaptability and predictive power. Moreover, the complexity of AI models presents interpretability challenges, requiring additional efforts to align outputs with policy-making needs. Leveraging AI and data-driven methods, this study offers actionable insights for policymakers to optimize energy use and curb emissions in urban settings like Tehran. Future research should incorporate socio-economic variables and hybrid models to enhance predictive reliability and practical relevance.
{"title":"AI-driven energy optimization enhancing efficiency in urban environments with hybrid machine learning models","authors":"Ali Majnoon , Amirali Saifoddin","doi":"10.1016/j.clet.2025.101072","DOIUrl":"10.1016/j.clet.2025.101072","url":null,"abstract":"<div><div>Accurate forecasting of electricity consumption is essential for sustainable urban planning, particularly in fast-growing cities like Tehran. Conventional models often fail to adequately capture the intricate relationships between environmental factors and energy demand. To overcome these limitations, this study applies advanced AI techniques such as Neural Networks, Random Forest Regression, and Gradient Boosting, using a comprehensive dataset (2000–2022) that integrates meteorological, environmental, and fuel consumption variables to enhance predictive performance. Random Forest Regression achieved the highest accuracy, with an R<sup>2</sup> 0.9835 and MSE of 0.0165, explaining 98.35 % of the variation in electricity consumption. Feature engineering substantially improved model accuracy, highlighting temperature variables (T2M, T2M_MAX, T2M_MIN) and fuel consumption as the most influential predictors. Correlation analysis revealed strong associations between environmental factors and electricity demand. Using Sequential Least Squares Programming (SLSQP) optimization, the study determined conditions that reduced electricity consumption to 1.09 million kWh. These findings highlight the value of AI models in enhancing forecasting accuracy and supporting efficient energy planning. Ensemble learning and optimization methods strengthen sustainable energy management. However, reliance on historical data and neglect of socio-economic factors may constrain the models’ adaptability and predictive power. Moreover, the complexity of AI models presents interpretability challenges, requiring additional efforts to align outputs with policy-making needs. Leveraging AI and data-driven methods, this study offers actionable insights for policymakers to optimize energy use and curb emissions in urban settings like Tehran. Future research should incorporate socio-economic variables and hybrid models to enhance predictive reliability and practical relevance.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101072"},"PeriodicalIF":6.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019212","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 : 2025-09-01DOI: 10.1016/j.clet.2025.101073
Mahyar Mansouri, Mohsen Shayanmehr, Ahad Ghaemi
This work introduces an experimentally validated, data-driven machine learning (ML) framework for predicting the adsorptive desulfurization (ADS) performance of zeolite-based materials. A curated dataset of 700 entries was compiled from diverse sources, incorporating key structural and operational parameters such as Brunauer–Emmett–Teller (BET) surface area, total pore volume (TPV), temperature, contact time, and molecular weight of sulfur compounds (MW-S). Seven ML models were developed and compared, with Extra Trees Regressor (ETR) achieving the best performance (R2 = 0.9979, MAE = 0.0308), followed by Random Forest (RF) (R2 = 0.9932, MAE = 0.0524). Feature importance analysis and shapley additive explanations (SHAP) identified molecular weight and BET surface area as the most influential descriptors. For better interpretability and generalizability, the zeolite type was excluded as an input feature and replaced by physicochemical properties. Furthermore, the top-performing model was integrated with a genetic algorithm (GA) to optimize operating conditions, resulting in a predicted maximum adsorption capacity of 131.63 mg S/g. Model robustness was also confirmed using an independent test set. Overall, this study provides a reliable and interpretable framework for accelerating ADS system design and can be extended to other adsorption-based separation processes.
这项工作介绍了一个实验验证的，数据驱动的机器学习（ML）框架，用于预测沸石基材料的吸附脱硫（ADS）性能。收集了来自不同来源的700个条目，包括关键的结构和操作参数，如brunauer - emmet - teller （BET）表面积、总孔隙体积（TPV）、温度、接触时间和硫化合物分子量（MW-S）。建立7个ML模型进行比较，其中Extra Trees regression （ETR）模型表现最佳（R2 = 0.9979, MAE = 0.0308），其次是Random Forest （RF）模型（R2 = 0.9932, MAE = 0.0524）。特征重要性分析和shapley加性解释（SHAP）确定分子量和BET表面积是最具影响力的描述符。为了更好地解释和推广，沸石类型被排除在输入特征之外，取而代之的是物理化学性质。此外，将最佳模型与遗传算法（GA）相结合，对操作条件进行优化，预测最大吸附容量为131.63 mg S/g。模型的稳健性也通过一个独立的测试集得到证实。总的来说，本研究为加速ADS系统的设计提供了一个可靠和可解释的框架，并可扩展到其他基于吸附的分离过程。
{"title":"Data-driven modeling using machine learning to investigate the desulfurization performance by zeolitic adsorbents","authors":"Mahyar Mansouri, Mohsen Shayanmehr, Ahad Ghaemi","doi":"10.1016/j.clet.2025.101073","DOIUrl":"10.1016/j.clet.2025.101073","url":null,"abstract":"<div><div>This work introduces an experimentally validated, data-driven machine learning (ML) framework for predicting the adsorptive desulfurization (ADS) performance of zeolite-based materials. A curated dataset of 700 entries was compiled from diverse sources, incorporating key structural and operational parameters such as Brunauer–Emmett–Teller (BET) surface area, total pore volume (TPV), temperature, contact time, and molecular weight of sulfur compounds (MW-S). Seven ML models were developed and compared, with Extra Trees Regressor (ETR) achieving the best performance (R<sup>2</sup> = 0.9979, MAE = 0.0308), followed by Random Forest (RF) (R<sup>2</sup> = 0.9932, MAE = 0.0524). Feature importance analysis and shapley additive explanations (SHAP) identified molecular weight and BET surface area as the most influential descriptors. For better interpretability and generalizability, the zeolite type was excluded as an input feature and replaced by physicochemical properties. Furthermore, the top-performing model was integrated with a genetic algorithm (GA) to optimize operating conditions, resulting in a predicted maximum adsorption capacity of 131.63 mg S/g. Model robustness was also confirmed using an independent test set. Overall, this study provides a reliable and interpretable framework for accelerating ADS system design and can be extended to other adsorption-based separation processes.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101073"},"PeriodicalIF":6.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932336","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 : 2025-09-01DOI: 10.1016/j.clet.2025.101069
Md. Habibur Rahman , Roberto Baldacci , Carlos Méndez , Md Al Amin
The adoption of hydrogen fuel cell vehicles (HFCVs) is essential for achieving sustainable, low-carbon transportation, but many barriers hinder this transition. Therefore, this study aims to identify, categorize, and prioritize these barriers in the context of the Gulf-Europe corridor, also known as the Iraq Development Road Project (DRP). To achieve this, we adopt a two-stage methodological framework that integrates the Fuzzy Analytical Hierarchy Process (Fuzzy AHP) to quantify the relative importance of thirty secondary barriers, and Interpretive Structural Modeling (ISM) to explore the interdependencies among the top ten. The Fuzzy AHP results highlight technological, economic, and infrastructure-related barriers as the most critical primary barriers. The ISM analysis further reveals that three barriers, lack of hydrogen production hubs, limited hydrogen transport options, and hydrogen storage and transportation, are independent. Six barriers, fuel cell efficiency and durability, hydrogen production and distribution costs, vehicle range and refueling time, infrastructure investment, refueling station compatibility issues, and hydrogen purity requirements, are classified as linkage barriers. One barrier, high initial vehicle cost, is found to be dependent. To accelerate HFCVs adoption, we recommend strengthening hydrogen infrastructure, fostering technological innovation, reducing costs through targeted incentives, and enhancing policy coordination among stakeholders and policymakers. This study contributes to literature by offering a comprehensive understanding of the adoption barriers and providing actionable insights to support the development of more effective strategies. Notably, it uniquely addresses social, logistical, and technological barriers, alongside geographic barriers, that have been largely overlooked in previous studies.
{"title":"Exploring the barriers to hydrogen fuel cell vehicles adoption in the Gulf-Europe corridor: a Fuzzy AHP and ISM analysis","authors":"Md. Habibur Rahman , Roberto Baldacci , Carlos Méndez , Md Al Amin","doi":"10.1016/j.clet.2025.101069","DOIUrl":"10.1016/j.clet.2025.101069","url":null,"abstract":"<div><div>The adoption of hydrogen fuel cell vehicles (HFCVs) is essential for achieving sustainable, low-carbon transportation, but many barriers hinder this transition. Therefore, this study aims to identify, categorize, and prioritize these barriers in the context of the Gulf-Europe corridor, also known as the Iraq Development Road Project (DRP). To achieve this, we adopt a two-stage methodological framework that integrates the Fuzzy Analytical Hierarchy Process (Fuzzy AHP) to quantify the relative importance of thirty secondary barriers, and Interpretive Structural Modeling (ISM) to explore the interdependencies among the top ten. The Fuzzy AHP results highlight technological, economic, and infrastructure-related barriers as the most critical primary barriers. The ISM analysis further reveals that three barriers, lack of hydrogen production hubs, limited hydrogen transport options, and hydrogen storage and transportation, are independent. Six barriers, fuel cell efficiency and durability, hydrogen production and distribution costs, vehicle range and refueling time, infrastructure investment, refueling station compatibility issues, and hydrogen purity requirements, are classified as linkage barriers. One barrier, high initial vehicle cost, is found to be dependent. To accelerate HFCVs adoption, we recommend strengthening hydrogen infrastructure, fostering technological innovation, reducing costs through targeted incentives, and enhancing policy coordination among stakeholders and policymakers. This study contributes to literature by offering a comprehensive understanding of the adoption barriers and providing actionable insights to support the development of more effective strategies. Notably, it uniquely addresses social, logistical, and technological barriers, alongside geographic barriers, that have been largely overlooked in previous studies.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101069"},"PeriodicalIF":6.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019213","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 : 2025-08-22DOI: 10.1016/j.clet.2025.101071
Hanane Ait Lahoussine Ouali , Otman Abida , Mohamed Essalhi , Nisar Ali , Ibrahim Moukhtar
In today's world, artificial intelligence has become a vital utility technology with the potential to benefit various industries and research endeavors significantly. One such application lies in harnessing solar thermal energy for green hydrogen purposes. Hence, this study aims to examine the feed-forward back-propagation network (FFBPN) in the context of a Dish/Stirling powered electrolysis system for hydrogen production, utilizing time series data from over twenty locations in Morocco. The FFBPN model was developed to evaluate the impact of various input parameters, including direct normal irradiation (DNI) at different geographical locations, on hydrogen production from the Dish/Stirling powered electrolysis system. This model was trained using different training algorithms, namely Levenberg-Marquardt (LM), Fletcher-Powell Conjugate Gradient (CGF), One Step Secant (OSS), and Scaled Conjugate Gradient Back-propagation (SCG), to identify the most effective approach for predicting green hydrogen production. By using a variety of training algorithms and evaluating the models using specified metrics, the study aimed to determine the most suitable and effective training approach for the given data. The different results indicate that, among all the locations examined in eastern Morocco, Figuig and Bouarfa cities have been identified as the most appropriate locations for implementing the proposed system, yielding the highest annual net electric energy output of 83.52 GWh/yr and 81.92 GWh/yr, respectively. Furthermore, the system allowed the production of over 1462 tons/yr of green hydrogen, supported by a total installed capacity of 50 MWe. Furthermore, the statistical analysis reveals that the Levenberg-Marquardt (LM) algorithm, using 33 neurons, outperformed others, exhibiting the lowest errors and the highest R2 value during both training and testing. Specifically, during training, the metrics of RMSE, MRE, COV, and R2 recorded values of 0.582, 0.395, 0.510, and 0.99999, respectively, whereas during testing they were 0.633, 0.474, 0.560, and 0.99999, respectively. The FFBPN application stands as a pioneering and effective model to predict green hydrogen production from the Dish/Stirling system.
{"title":"Forecasting hydrogen production through electrolysis powered by concentrated solar power plant using artificial neural network","authors":"Hanane Ait Lahoussine Ouali , Otman Abida , Mohamed Essalhi , Nisar Ali , Ibrahim Moukhtar","doi":"10.1016/j.clet.2025.101071","DOIUrl":"10.1016/j.clet.2025.101071","url":null,"abstract":"<div><div>In today's world, artificial intelligence has become a vital utility technology with the potential to benefit various industries and research endeavors significantly. One such application lies in harnessing solar thermal energy for green hydrogen purposes. Hence, this study aims to examine the feed-forward back-propagation network (FFBPN) in the context of a Dish/Stirling powered electrolysis system for hydrogen production, utilizing time series data from over twenty locations in Morocco. The FFBPN model was developed to evaluate the impact of various input parameters, including direct normal irradiation (DNI) at different geographical locations, on hydrogen production from the Dish/Stirling powered electrolysis system. This model was trained using different training algorithms, namely Levenberg-Marquardt (LM), Fletcher-Powell Conjugate Gradient (CGF), One Step Secant (OSS), and Scaled Conjugate Gradient Back-propagation (SCG), to identify the most effective approach for predicting green hydrogen production. By using a variety of training algorithms and evaluating the models using specified metrics, the study aimed to determine the most suitable and effective training approach for the given data. The different results indicate that, among all the locations examined in eastern Morocco, Figuig and Bouarfa cities have been identified as the most appropriate locations for implementing the proposed system, yielding the highest annual net electric energy output of 83.52 GWh/yr and 81.92 GWh/yr, respectively. Furthermore, the system allowed the production of over 1462 tons/yr of green hydrogen, supported by a total installed capacity of 50 MWe. Furthermore, the statistical analysis reveals that the Levenberg-Marquardt (LM) algorithm, using 33 neurons, outperformed others, exhibiting the lowest errors and the highest R<sup>2</sup> value during both training and testing. Specifically, during training, the metrics of RMSE, MRE, COV, and R<sup>2</sup> recorded values of 0.582, 0.395, 0.510, and 0.99999, respectively, whereas during testing they were 0.633, 0.474, 0.560, and 0.99999, respectively. The FFBPN application stands as a pioneering and effective model to predict green hydrogen production from the Dish/Stirling system.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101071"},"PeriodicalIF":6.5,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916284","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 : 2025-08-14DOI: 10.1016/j.clet.2025.101070
Munazza Ijaz , Rafia Ijaz , Ji'an Bi , Temoor Ahmed , Muhammad Noman , Humera Rani , Muhammad Babar Malook , Muhammad Shafiq Shahid , Gabrijel Ondrasek , Baoyi Lin , Bin Li
Cadmium (Cd) contamination severely threatens agricultural productivity and food safety. This study examines the ability of biogenic silicon nanoparticles (SiNPs) as nanotherapeutics to mitigate Cd stress in rice (Oryza sativa L.) by enhancing physiological and biochemical responses. A controlled greenhouse experiment demonstrated that SiNPs (250 mg kg−1) significantly improved plant growth under Cd stress. The application of SiNPs increased plant height, fresh and dry weight by 22.98 %, 25.18 %, and 30.01 %, respectively, as compared to the control. Photosynthetic efficiency was also improved, as evidenced by increase in chlorophyll a and b content (17.02 % and 56.86 %, respectively). SiNPs strengthened the plant defense system by enhancing the activities of antioxidant enzymes, such as superoxide dismutase (23.18 %), peroxidase (41.98 %), and ascorbate peroxidase (11.29 %), while simultaneously reducing reactive oxygen species accumulation. SiNPs also enhanced the absorption of various essential nutrients and reduced Cd accumulation (by 61.04 %) in rice leaves compared to Cd-stressed plants without SiNPs treatment. Gene expression analysis showed that SiNPs upregulated genes associated with silicon transport, antioxidant activity, and phyto-chelation, further validating the Cd detoxification in rice plants. Moreover, soil enzyme activities and nutrient cycling improved upon SiNPs exposure. Leaf ultrastructure analysis revealed that SiNPs preserved normal cellular morphology and minimized Cd-induced damage. These findings highlight biogenic SiNPs (as nanotherapeutics) are effective and environmentally friendly solution for reducing Cd toxicity in rice.
{"title":"Silicon-functionalized nanotherapeutics modulate physio-biochemical functions and soil enzyme profile for curtailing cadmium toxicity in rice (Oryza sativa L.) at vegetative phase","authors":"Munazza Ijaz , Rafia Ijaz , Ji'an Bi , Temoor Ahmed , Muhammad Noman , Humera Rani , Muhammad Babar Malook , Muhammad Shafiq Shahid , Gabrijel Ondrasek , Baoyi Lin , Bin Li","doi":"10.1016/j.clet.2025.101070","DOIUrl":"10.1016/j.clet.2025.101070","url":null,"abstract":"<div><div>Cadmium (Cd) contamination severely threatens agricultural productivity and food safety. This study examines the ability of biogenic silicon nanoparticles (SiNPs) as nanotherapeutics to mitigate Cd stress in rice (<em>Oryza sativa</em> L.) by enhancing physiological and biochemical responses. A controlled greenhouse experiment demonstrated that SiNPs (250 mg kg<sup>−1</sup>) significantly improved plant growth under Cd stress. The application of SiNPs increased plant height, fresh and dry weight by 22.98 %, 25.18 %, and 30.01 %, respectively, as compared to the control. Photosynthetic efficiency was also improved, as evidenced by increase in chlorophyll <em>a</em> and <em>b</em> content (17.02 % and 56.86 %, respectively). SiNPs strengthened the plant defense system by enhancing the activities of antioxidant enzymes, such as superoxide dismutase (23.18 %), peroxidase (41.98 %), and ascorbate peroxidase (11.29 %), while simultaneously reducing reactive oxygen species accumulation. SiNPs also enhanced the absorption of various essential nutrients and reduced Cd accumulation (by 61.04 %) in rice leaves compared to Cd-stressed plants without SiNPs treatment. Gene expression analysis showed that SiNPs upregulated genes associated with silicon transport, antioxidant activity, and phyto-chelation, further validating the Cd detoxification in rice plants. Moreover, soil enzyme activities and nutrient cycling improved upon SiNPs exposure. Leaf ultrastructure analysis revealed that SiNPs preserved normal cellular morphology and minimized Cd-induced damage. These findings highlight biogenic SiNPs (as nanotherapeutics) are effective and environmentally friendly solution for reducing Cd toxicity in rice.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101070"},"PeriodicalIF":6.5,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863461","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 : 2025-08-13DOI: 10.1016/j.clet.2025.101067
Muhammad Atif Saeed , Faraz Junejo , Imran Amin , Irfan Khan Tanoli , Sadique Ahmad , Ala Saleh D. Alluhaidan , Abdelhamied A. Ateya
This work presents a two-stage optimization approach designed to improve the sustainability of machining processes by integrating multi-objective optimization, multi-criteria decision-making, and experimental design approaches. The proposed work uses the non-dominated sorting genetic algorithm-II (NSGA-II) to balance economic and environmental objectives. A case study on the machining of EN8 steel demonstrated significant improvements after applying the proposed framework, achieving a 65.9 % increase in the environmental sustainability assessment (EnSA) and a 28.8 % improvement in the economic sustainability assessment (ESA). Key performance indicators, including total energy consumption of machine (TECM), temperature (T), and surface roughness (Ra), improved by 24.5 %, 25 %, and 48.8 %, respectively, though trade-offs between energy efficiency (EE) and process flexibility (PF). Sensitivity analysis highlighted that axial depth ('X4') was the most influential factor, accounting for 50 % of ESA variation and 86 % of EnSA variation. This framework offers a practical approach to optimizing machining parameters, contributing to sustainable manufacturing practices. Future research could extend its application to other manufacturing processes and incorporate additional sustainability dimensions, such as social impacts, to further promote overall sustainability in the industry.
本工作提出了一种两阶段优化方法，旨在通过集成多目标优化、多准则决策和实验设计方法来提高加工过程的可持续性。提出的工作使用非支配排序遗传算法- ii （NSGA-II）来平衡经济和环境目标。对EN8钢加工的案例研究表明，应用该框架后，环境可持续性评估（EnSA）提高了65.9%，经济可持续性评估（ESA）提高了28.8%。关键性能指标，包括机器总能耗（TECM），温度(T)和表面粗糙度（Ra），分别提高了24.5%，25%和48.8%，尽管在能源效率（EE）和工艺灵活性（PF）之间进行了权衡。敏感性分析显示，轴向深度（X4）是影响最大的因素，分别占ESA变化的50%和EnSA变化的86%。该框架提供了一种实用的方法来优化加工参数，有助于可持续的制造实践。未来的研究可以将其应用扩展到其他制造过程，并纳入额外的可持续性维度，如社会影响，以进一步促进行业的整体可持续性。
{"title":"Optimizing economic and environmental objectives in sustainable machining processes","authors":"Muhammad Atif Saeed , Faraz Junejo , Imran Amin , Irfan Khan Tanoli , Sadique Ahmad , Ala Saleh D. Alluhaidan , Abdelhamied A. Ateya","doi":"10.1016/j.clet.2025.101067","DOIUrl":"10.1016/j.clet.2025.101067","url":null,"abstract":"<div><div>This work presents a two-stage optimization approach designed to improve the sustainability of machining processes by integrating multi-objective optimization, multi-criteria decision-making, and experimental design approaches. The proposed work uses the non-dominated sorting genetic algorithm-II (NSGA-II) to balance economic and environmental objectives. A case study on the machining of EN8 steel demonstrated significant improvements after applying the proposed framework, achieving a 65.9 % increase in the environmental sustainability assessment (EnSA) and a 28.8 % improvement in the economic sustainability assessment (ESA). Key performance indicators, including total energy consumption of machine (TECM), temperature (T), and surface roughness (Ra), improved by 24.5 %, 25 %, and 48.8 %, respectively, though trade-offs between energy efficiency (EE) and process flexibility (PF). Sensitivity analysis highlighted that axial depth ('X<sub>4</sub>') was the most influential factor, accounting for 50 % of ESA variation and 86 % of EnSA variation. This framework offers a practical approach to optimizing machining parameters, contributing to sustainable manufacturing practices. Future research could extend its application to other manufacturing processes and incorporate additional sustainability dimensions, such as social impacts, to further promote overall sustainability in the industry.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101067"},"PeriodicalIF":6.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908982","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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