Cleaner Engineering and Technology最新文献_第10页

AI-driven energy optimization enhancing efficiency in urban environments with hybrid machine learning models 人工智能驱动的能源优化，通过混合机器学习模型提高城市环境效率

IF 6.5 Q2 ENGINEERING, ENVIRONMENTAL

Cleaner Engineering and Technology

Pub Date : 2025-09-01 DOI: 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.

准确预测用电量对于可持续城市规划至关重要，尤其是在德黑兰这样快速发展的城市。传统模型往往不能充分反映环境因素与能源需求之间的复杂关系。为了克服这些限制，本研究应用了先进的人工智能技术，如神经网络、随机森林回归和梯度增强，使用综合数据集（2000-2022），该数据集集成了气象、环境和燃料消耗变量，以提高预测性能。随机森林回归的准确率最高，R2为0.9835，MSE为0.0165，解释了98.35%的用电量变化。特征工程极大地提高了模型的准确性，强调温度变量（T2M, T2M_MAX, T2M_MIN）和燃料消耗是最具影响力的预测因子。相关分析显示，环境因素与电力需求之间存在很强的相关性。使用顺序最小二乘规划（SLSQP）优化，该研究确定了将电力消耗减少到109万千瓦时的条件。这些发现突出了人工智能模型在提高预测准确性和支持高效能源规划方面的价值。集成学习和优化方法加强可持续能源管理。然而，对历史数据的依赖和对社会经济因素的忽视可能会限制模型的适应性和预测能力。此外，人工智能模型的复杂性带来了可解释性方面的挑战，需要进一步努力使产出与决策需求保持一致。利用人工智能和数据驱动的方法，本研究为政策制定者提供了可操作的见解，以优化德黑兰等城市环境中的能源使用和遏制排放。未来的研究应纳入社会经济变量和混合模型，以提高预测的可靠性和实际相关性。

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引用次数: 0

Data-driven modeling using machine learning to investigate the desulfurization performance by zeolitic adsorbents 利用机器学习进行数据驱动建模，研究沸石吸附剂的脱硫性能

IF 6.5 Q2 ENGINEERING, ENVIRONMENTAL

Cleaner Engineering and Technology

Pub Date : 2025-09-01 DOI: 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。

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引用次数: 0

Exploring the barriers to hydrogen fuel cell vehicles adoption in the Gulf-Europe corridor: a Fuzzy AHP and ISM analysis 探索氢燃料电池汽车在海湾-欧洲走廊采用的障碍：模糊AHP和ISM分析

IF 6.5 Q2 ENGINEERING, ENVIRONMENTAL

Cleaner Engineering and Technology

Pub Date : 2025-09-01 DOI: 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.

氢燃料电池汽车（HFCVs）的采用对于实现可持续的低碳交通至关重要，但许多障碍阻碍了这一转变。因此，本研究旨在识别、分类和优先考虑海湾-欧洲走廊背景下的这些障碍，也被称为伊拉克发展道路项目（DRP）。为了实现这一目标，我们采用了一个两阶段的方法框架，该框架集成了模糊分析层次过程（Fuzzy AHP）来量化30个次级障碍的相对重要性，以及解释结构建模（ISM）来探索前十大障碍之间的相互依赖性。模糊层次分析法的结果强调技术、经济和基础设施相关的障碍是最关键的主要障碍。ISM的分析进一步显示，缺乏氢气生产中心、有限的氢气运输选择以及氢气储存和运输这三个障碍是独立的。燃料电池效率和耐用性、氢气生产和配送成本、车辆续航里程和加氢时间、基础设施投资、加氢站兼容性问题、氢气纯度要求等6个障碍被归类为链接障碍。一个障碍，高初始车辆成本，被发现是依赖的。为加快氢燃料电池的采用，我们建议加强氢基础设施建设，促进技术创新，通过有针对性的激励措施降低成本，并加强利益攸关方和决策者之间的政策协调。本研究通过提供对采用障碍的全面理解和提供可操作的见解来支持更有效策略的开发，从而为文献做出贡献。值得注意的是，它独特地解决了社会、后勤和技术障碍，以及地理障碍，这些障碍在以前的研究中很大程度上被忽视了。

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引用次数: 0

Forecasting hydrogen production through electrolysis powered by concentrated solar power plant using artificial neural network 利用人工神经网络预测聚光太阳能电站电解产氢

IF 6.5 Q2 ENGINEERING, ENVIRONMENTAL

Cleaner Engineering and Technology

Pub Date : 2025-08-22 DOI: 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.

在当今世界，人工智能已经成为一项重要的实用技术，有可能使各个行业和研究工作显著受益。其中一个应用是利用太阳能热能来实现绿色氢的目的。因此，本研究旨在利用摩洛哥20多个地点的时间序列数据，在Dish/Stirling动力电解制氢系统的背景下检查前馈反向传播网络（FFBPN）。开发FFBPN模型是为了评估不同输入参数（包括不同地理位置的直接正常照射（DNI））对Dish/Stirling供电电解系统产氢的影响。该模型使用不同的训练算法，即Levenberg-Marquardt (LM)， Fletcher-Powell共轭梯度（CGF），一步割线（OSS）和缩放共轭梯度反向传播（SCG）进行训练，以确定预测绿色氢气产量的最有效方法。通过使用各种训练算法和使用指定指标评估模型，研究旨在确定给定数据最合适和最有效的训练方法。不同的结果表明，在摩洛哥东部检查的所有地点中，Figuig和Bouarfa城市被确定为实施拟议系统的最合适地点，年净发电量最高，分别为83.52 GWh/年和81.92 GWh/年。此外，该系统允许生产超过1462吨/年的绿色氢，总装机容量为50兆瓦。此外，统计分析表明，使用33个神经元的Levenberg-Marquardt （LM）算法在训练和测试过程中都表现出最低的误差和最高的R2值，优于其他算法。具体而言，在训练期间，RMSE、MRE、COV和R2指标分别记录值为0.582、0.395、0.510和0.99999，而在测试期间，RMSE、MRE、COV和R2指标分别记录值为0.633、0.474、0.560和0.99999。FFBPN应用是预测Dish/Stirling系统绿色制氢的一个开创性和有效的模型。

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引用次数: 0

Silicon-functionalized nanotherapeutics modulate physio-biochemical functions and soil enzyme profile for curtailing cadmium toxicity in rice (Oryza sativa L.) at vegetative phase 硅功能化纳米治疗剂调节水稻营养期生理生化功能和土壤酶谱以降低镉毒性

IF 6.5 Q2 ENGINEERING, ENVIRONMENTAL

Cleaner Engineering and Technology

Pub Date : 2025-08-14 DOI: 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.

镉污染严重威胁着农业生产力和食品安全。本研究考察了生物源硅纳米颗粒（SiNPs）作为纳米治疗剂通过增强水稻的生理生化反应来缓解镉胁迫的能力。对照温室试验表明，SiNPs （250 mg kg−1）显著促进了Cd胁迫下植物的生长。施用SiNPs后，株高、鲜重和干重分别比对照提高22.98%、25.18%和30.01%。叶绿素a和b含量分别提高了17.02%和56.86%，提高了光合效率。SiNPs通过提高抗氧化酶如超氧化物歧化酶（23.18%）、过氧化物酶（41.98%）和抗坏血酸过氧化物酶（11.29%）的活性来增强植物的防御系统，同时减少活性氧的积累。与未处理SiNPs的Cd胁迫植株相比，SiNPs还增强了水稻叶片对各种必需营养素的吸收，减少了Cd积累（减少了61.04%）。基因表达分析显示，SiNPs上调了与硅转运、抗氧化活性和植物螯合相关的基因，进一步证实了水稻对镉的解毒作用。此外，土壤酶活性和养分循环在SiNPs暴露后得到改善。叶片超微结构分析显示，SiNPs保留了正常的细胞形态，并将cd诱导的损伤降至最低。这些发现强调了生物源性SiNPs（作为纳米治疗药物）是降低水稻镉毒性的有效和环保的解决方案。

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引用次数: 0

Optimizing economic and environmental objectives in sustainable machining processes 在可持续加工过程中优化经济和环境目标

IF 6.5 Q2 ENGINEERING, ENVIRONMENTAL

Cleaner Engineering and Technology

Pub Date : 2025-08-13 DOI: 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。

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引用次数: 0

Trade-off analysis and identification of optimization potentials between lightweight design and design for circularity 轻量化设计与圆度设计之间优化潜力的权衡分析与识别

IF 6.5 Q2 ENGINEERING, ENVIRONMENTAL

Cleaner Engineering and Technology

Pub Date : 2025-08-13 DOI: 10.1016/j.clet.2025.101068

Kristian König, Simon Mörsdorf, Michael Vielhaber

The pressing need for resource conservation and environmental impact reduction in sustainable development emphasizes the importance of lightweight design and design for circularity in product development. However, the complexity and multidisciplinary nature of decision-making in these areas pose a major challenge.

In the present work, a novel approach for trade-off analyses is proposed to meticulously analyze the relationships between both design disciplines, facilitating decision-making and the identification of optimization potentials throughout design. Illustrated by material selection and a case study on the development of a semi-mobile handling system, two fundamental design approaches for decision-making in view of different and shared development objectives between lightweight design and design for circularity are identified. This enables the prioritization of development objectives in early phases and enhances eco-effectivity with regard to resource conservation and environmental impact reduction in the context of complex design considerations.

可持续发展中节约资源和减少环境影响的迫切需要强调了产品开发中轻量化设计和循环设计的重要性。然而，这些领域决策的复杂性和多学科性质构成了重大挑战。在本工作中，提出了一种新的权衡分析方法，以仔细分析两个设计学科之间的关系，促进整个设计过程中的决策和优化潜力的识别。通过材料选择和半移动搬运系统开发的案例研究，确定了轻量化设计和圆弧设计两种不同且共享的开发目标下决策的基本设计方法。这样就可以在早期阶段确定发展目标的优先次序，并在考虑复杂设计的情况下提高节约资源和减少环境影响方面的生态效益。

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引用次数: 0

Characterization of the physicochemical and functional properties of clay-based construction materials with a neutral carbon footprint using recycled pistachio shell waste 利用回收的开心果壳废弃物制备中性碳足迹粘土基建筑材料的理化特性和功能特性

IF 6.5 Q2 ENGINEERING, ENVIRONMENTAL

Cleaner Engineering and Technology

Pub Date : 2025-08-12 DOI: 10.1016/j.clet.2025.101062

Aziz El-yahyaoui, Imad Manssouri

This study explores the recycling of pistachio shell waste in producing eco-friendly, non-fired clay bricks, aiming for both economic and ecological benefits. The pistachio shells were sourced from the Taounate region in northern Morocco, and the clay used was extracted from Oulad Azam, in the same region. X-ray analysis of the soil revealed a high Illite clay content with around 40 % SiO₂, making it suitable for construction. Brick samples were made with varying pistachio shell content (0 %–20 %, including intermediate levels such as 1 %, 3 %, 5 %, 7 %, and 15 %) and sizes: small (δ < 1 mm), medium (1 mm < δ ≤ 3 mm), and large (δ > 6 mm). Physical (porosity, density), mechanical (compressive strength), and thermal (conductivity) properties were evaluated. Results showed lightweight bricks, with densities below 1.75 g/cm³, linked to higher porosity for smaller additive sizes. Microscopic observations confirmed reduced porosity for finer additive sizes (δ ≤ 1 mm), contributing to enhanced performance. Compressive strength improved by 8 % and thermal conductivity by 23 % with smaller grains. The strength of the bricks generally decreased with increasing additive content, while insulation improved significantly. However, there was a trade-off between insulation and strength. The optimal balance was found at additive dosages of 7 %, 8 %, and 9 % for large, medium, and small grains, respectively, yielding bricks with better performance than conventional industrial ones. These results suggest that the valorization of local agricultural waste contributes positively to both sustainable construction and the local economy.

本研究探索利用开心果壳废弃物生产环保、免烧制粘土砖，以达到经济效益和生态效益并重的目的。开心果壳来自摩洛哥北部的陶纳特地区，所使用的粘土来自同一地区的奥拉德阿扎姆。对土壤的x射线分析显示，伊利石粘土含量高，SiO2含量约为40%，适合建筑。砖样由不同的开心果壳含量（0% - 20%，包括中间水平，如1%，3%,5%，7%和15%）和尺寸制成：小(δ <；1毫米)，中(1毫米<；δ≤3mm)，且较大(δ >；6毫米)。评估了物理（孔隙度、密度）、机械（抗压强度）和热（导电性）性能。结果表明，轻质砖的密度低于1.75 g/cm3，添加剂尺寸越小，孔隙率越高。微观观察证实，更细的添加剂尺寸（δ≤1 mm）降低了孔隙率，有助于提高性能。晶粒越小，抗压强度提高8%，导热系数提高23%。随着添加剂含量的增加，砖的强度普遍降低，而保温性能明显提高。然而，在绝缘和强度之间有一个权衡。大、中、小颗粒砖在添加量分别为7%、8%和9%时达到最佳平衡，生产出的砖性能优于常规工业砖。这些结果表明，当地农业废弃物的增值对可持续建筑和当地经济都有积极的贡献。

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引用次数: 0

The concept of a technological system for the treatment and using rainwater in swimming pool installations 在游泳池设施中处理和利用雨水的技术系统的概念

IF 6.5 Q2 ENGINEERING, ENVIRONMENTAL

Cleaner Engineering and Technology

Pub Date : 2025-08-11 DOI: 10.1016/j.clet.2025.101066

Rafał Rapacewicz , Anna Lempart-Rapacewicz , Edyta Kudlek , Katarzyna Brukało

This study introduces and validates the “SwimmInRain” system, a technological solution for rainwater treatment in public swimming pool installations. Laboratory experiments and analytical evaluations focused on optimizing process components, with special attention to UV-based advanced oxidation. Raw rainwater often exhibited turbidity (1.67 ± 0.1 NTU) and pH (6.04 ± 0.1) values outside acceptable ranges, prompting the development of a multistage treatment system. The complete technology includes filtration, separation of petroleum substances, coagulation, mechanical filtration, UV disinfection, chlorination, and pH correction. Medium-pressure UV lamps, particularly the TQ-Z3 model, showed superior effectiveness in degrading organic micropollutants. Removal efficiency was influenced by UV dose and exposure time. Post-treatment water quality improved significantly, with total organic carbon reduced by up to 35.7 %. Combined chlorine concentrations were also minimized, indicating fewer disinfection by-products. The “SwimmInRain” system offers an environmentally sound method for reusing rainwater in swimming pools, aligning with sustainability goals and ensuring safe, cost-effective water management.

本研究介绍并验证了“SwimmInRain”系统，这是一种用于公共游泳池装置雨水处理的技术解决方案。实验室实验和分析评估侧重于优化工艺组件，特别关注基于紫外线的高级氧化。未经处理的雨水经常表现出超出可接受范围的浊度（1.67±0.1 NTU）和pH值（6.04±0.1），促使多级处理系统的发展。完整的工艺包括过滤、分离石油物质、混凝、机械过滤、紫外线消毒、氯化和pH校正。中压紫外灯，特别是TQ-Z3型，在降解有机微污染物方面表现出优异的效果。紫外剂量和照射时间对去除率有影响。处理后水质得到显著改善，总有机碳减少35.7%。氯的综合浓度也被降到最低，表明消毒副产物更少。“SwimmInRain”系统为游泳池的雨水再利用提供了一种环保的方法，与可持续发展目标保持一致，并确保安全，具有成本效益的水管理。

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引用次数: 0

Solvent-free biorefinery of leather fleshing waste into FAMEs and nitrogen-rich co-products 无溶剂生物炼制皮革，将废肉转化为富氮副产品

IF 6.5 Q2 ENGINEERING, ENVIRONMENTAL

Cleaner Engineering and Technology

Pub Date : 2025-08-11 DOI: 10.1016/j.clet.2025.101065

Víctor Deroncelé , Sílvia Sorolla , Anna Bacardit

This study presents a solvent-free method for converting bovine and ovine fleshing waste into biodiesel-compatible compounds and a nitrogen-rich solid by-product. The process combines mechanical lipid separation using Tricanter® centrifugation with an optimized alkaline conversion step. Lipid recovery exceeded 92 %, and under optimal reaction conditions, conversion yields surpassed 95 % without the need for pre-treatment. Bovine-derived esters complied with international biodiesel standards, while ovine esters showed promising properties for thermal energy storage applications. The solid fraction, containing up to 9.3 % nitrogen and no regulated heavy metals, met European requirements for use as fertilizer. The process consumed approximately 23 % less energy than conventional methods, and techno-economic analysis supports its industrial feasibility. Overall, this integrated approach offers a scalable, sustainable solution for tannery waste valorization aligned with circular bioeconomy goals.

本研究提出了一种无溶剂的方法，将牛和羊的肉废物转化为生物柴油相容的化合物和富氮的固体副产品。该工艺结合了机械脂质分离使用Tricanter®离心与优化的碱性转化步骤。脂质回收率超过92%，在最佳反应条件下，转化率超过95%，无需预处理。牛源酯符合国际生物柴油标准，而羊源酯在热能储存方面表现出良好的性能。固体部分，含有高达9.3%的氮和不受管制的重金属，符合欧洲要求作为肥料使用。该工艺消耗的能源比传统方法少约23%，技术经济分析支持其工业可行性。总的来说，这种综合方法为制革厂废物增值提供了可扩展的、可持续的解决方案，与循环生物经济目标保持一致。

引用次数: 0