Sustainable Energy Technologies and Assessments最新文献

{"title":"A novel two-stage xylose-upfront SSCF process for second-generation ethanol production from acid pretreated rice straw at very low enzyme loading and process time","authors":"Manas R. Swain ,&nbsp;Ajit Singh ,&nbsp;Anshu S. Mathur ,&nbsp;Ravi P. Gupta ,&nbsp;SSV Ramakumar ,&nbsp;Ajay K. Sharma","doi":"10.1016/j.seta.2025.104570","DOIUrl":"10.1016/j.seta.2025.104570","url":null,"abstract":"<div><div>To enhance the cost-effectiveness of bioethanol production from acid-pretreated rice straw, a novel two-stage modified Simultaneous Saccharification and Co-Fermentation (SSCF) process was developed. This process incorporates an initial xylose-upfront (XU) fermentation stage, conducted under low glucose concentrations during enzymatic hydrolysis at 30 °C, followed by a second stage involving continued hydrolysis and glucose fermentation. Under optimized conditions, the modified XU-SSCF process achieved an ethanol concentration of 50  g/L at 20 % (w/v) solid loading within 46 h, using a total enzyme loading of 3.3 FPU/g total solids (TS). In contrast, the conventional SSCF process yielded 46  g/L ethanol over 77 h at the same solid loading, requiring a significantly higher enzyme dose of 7 FPU/gTS. The modified process also demonstrated enhanced ethanol productivity, reaching 1.08  g/L/h, compared to 0.63  g/L/h observed in the conventional SSCF. Furthermore, the implementation of a fractional cellulase dosing strategy—comprising in-house produced enzymes from <em>Penicillium funiculosum</em> MRJ-16 (2.0 FPU/gTS) and commercial CTec3 (0.3 FPU/gTS) enabled a 52.8 % reduction in total enzyme usage relative to the conventional approach, without compromising process efficiency.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"82 ","pages":"Article 104570"},"PeriodicalIF":7.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A sybil-resilient and privacy-aware blockchain architecture for dynamic demand response in decentralized microgrids","authors":"Abdullah Umar ,&nbsp;Prashant Kumar Jamwal ,&nbsp;Deepak Kumar ,&nbsp;Nitin Gupta ,&nbsp;Vijayakumar Gali ,&nbsp;Ajay Kumar","doi":"10.1016/j.seta.2025.104540","DOIUrl":"10.1016/j.seta.2025.104540","url":null,"abstract":"<div><div>This study proposes a decentralized, blockchain-enabled demand response (DR) framework to address the limitations of traditional centralized DR systems, which often suffer from privacy vulnerabilities, single points of failure, and susceptibility to Sybil attacks. By combining a hybrid Proof-of-Stake (PoS) mechanism with dynamic reputation scoring, the framework ensures secure and Sybil-resilient validator selection for consensus. To preserve privacy, Zero-Knowledge Proofs (ZKPs) ,SNARKs are embedded into smart contracts, enabling verifiable energy transactions without revealing sensitive bid or identity information. A dynamic game-theoretic model is used to capture the strategic interactions of prosumers during DR events, with formal analysis proving convergence to the Nash equilibrium under practical load conditions. The system is implemented using Solidity on the Polygon Mainnet and evaluated with real residential data from the Pecan Street dataset. Experimental results demonstrate significant performance gains, including a 12% reduction in peak demand, a 10% increase in prosumer generation, 83% load-shifting efficiency, and a 5.26% improvement in cost savings compared to static demand-side management (DSM) schemes. Additionally, the framework effectively mitigates 99% of Sybil attacks and achieves consensus within 8 s for up to 1000 nodes, highlighting its scalability, security, and suitability for integration with national DR platforms, carbon credit markets, and autonomous multi-agent energy systems.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"82 ","pages":"Article 104540"},"PeriodicalIF":7.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building-integrated photovoltaics in Mediterranean net-zero energy neighborhoods: Life cycle cost, energy, and environmental impact","authors":"Julius Jandl ,&nbsp;Elie Medioni ,&nbsp;Abraham Yezioro ,&nbsp;Sabrina Spatari","doi":"10.1016/j.seta.2025.104550","DOIUrl":"10.1016/j.seta.2025.104550","url":null,"abstract":"<div><div>The economic and environmental impact of building-integrated photovoltaic (BIPV) systems as a local power supply in Mediterranean neighborhoods was investigated using urban building energy simulations, material flow analysis, life cycle assessment, and life cycle cost analysis. Our findings reveal favorable economic performance with benefit–cost ratios of up to 3.35, low discounted payback times for most scenarios and reduced greenhouse gas intensity (48 to 119 g CO₂ eq./kWh) compared to consuming fossil energy. The estimated levelized cost of electricity ranges from 3.2 to 13.9 USD cents/kWh and is lower than that of electricity generated from fossil fuels. The study highlights the importance of comprehensive economic assessments that explore the profitability and potential revenue generation from energy production and material savings, when considering adoption of BIPV. The analysis of life-cycle stages underscores the significance of system pricing and BIPV recycling efficiency in determining the overall economic performance of BIPV systems. This research extends previous studies of single-building BIPV economic assessment to neighborhood-scale integration, while also comparing multiple PV materials and includes end-of-life costs and revenues. Most importantly, the replacement of failed panels is integrated into the analysis, offering new insights into the potential of BIPV systems to support planning net-zero energy neighborhoods.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"82 ","pages":"Article 104550"},"PeriodicalIF":7.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decreasing temporal convolutional method integrated with Fourier transform for precise solar PV and wind power generation prediction","authors":"Jian Yang,&nbsp;Guoxing Li,&nbsp;Mingbo Niu","doi":"10.1016/j.seta.2025.104553","DOIUrl":"10.1016/j.seta.2025.104553","url":null,"abstract":"<div><div>Accurately predicting power generation from solar photovoltaic (PV) and wind power systems is paramount for grid scheduling decisions, operational efficiency enhancement, and energy conservation. Many existing deep learning-based methods have more network layers, more complex models, and higher computational costs. They generally have limitations, such as difficulty in accurately extracting features of long sequences and predicting multiple types of energy separately. In response to the challenges above, this paper proposes a novel Decreasing Time-Domain Convolutional Network (DTCN), whose core innovation lies in its seamless integration with Fourier transform methods for the task of power generation prediction for solar photovoltaic and wind power facilities. The method’s key advantage lies in its deep integration of frequency-domain features: by incorporating frequency-domain features captured through the Fourier transform, it reveals the compositional information of the sequence across different frequency components, thereby endowing the model with a global perspective. This characteristic makes it inherently suited for multi-variable time series prediction tasks. Specifically, the method innovatively constructs a ”time-domain - frequency-domain” collaborative processing framework: first, the original time series data is mapped to the frequency domain using the fast Fourier transform (FFT) algorithm to model the frequency characteristics of the data explicitly; then, DTCN is innovatively applied to the frequency domain representation to extract complex dependencies between multiple variables. Notably, DTCN achieves efficient extraction of feature information in long-sequence frequency-domain representations through its core components, including diminishing expansion convolution, causal convolution, multi-step processing mechanisms, and residual connections. This design overcomes the limitations of traditional convolutional networks in modeling dependencies in long sequences. This paper presents experiments conducted on a full-year solar PV and wind power generation dataset in Xinjiang, China. The experimental results demonstrate that the proposed DTCN-FFT method outperforms other methods in accuracy. Taking the results of Xinjiang’s fourth quarter solar PV power generation prediction as an example, compared with Transformer, DLinear, TCN-GRU (Gated Recurrent Unit), TCN-LSTM (Long Short-Term Memory), TCN, and TCN-FFT, the Mean Absolute Error (MAE) of this method is reduced by 94.82%, 93.21%, 69.79%, 71.91%, 46.52%, and 31.85%, respectively.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"82 ","pages":"Article 104553"},"PeriodicalIF":7.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive rural distribution network optimization: Tailored demand-side management via multi-agent deep reinforcement learning coupled with distributionally robust stochastic models","authors":"Shuncheng Liu ,&nbsp;Jiajia Xiang ,&nbsp;Huizu Lin ,&nbsp;Yingxuan Li","doi":"10.1016/j.seta.2025.104516","DOIUrl":"10.1016/j.seta.2025.104516","url":null,"abstract":"<div><div>The increasing penetration of renewable energy in rural distribution networks presents a critical opportunity to transition toward sustainable energy systems. However, rural networks face unique challenges, including geographical dispersion, intermittent renewable generation, and socio-economic constraints, which complicate effective energy management. To address these issues, this paper proposes a novel Adaptive Demand-Side Management (DSM) framework tailored for rural distribution networks with high renewable energy integration. The framework integrates Multi-Agent Deep Reinforcement Learning (MADRL) with Distributionally Robust Optimization (DRO) to enable decentralized, adaptive, and resilient decision-making under uncertain conditions. The MADRL component models distributed energy resources (DERs), such as renewable generators, storage systems, and flexible loads. The proposed framework is validated through extensive simulations on a rural distribution network case study. Results demonstrate significant improvements in renewable energy utilization, voltage stability, and overall system resilience. Key findings include a 20% increase in renewable energy utilization, a 15% reduction in voltage deviations, and enhanced adaptability to variable load and generation conditions. This research contributes to the growing body of knowledge on DSM in rural energy systems, offering a scalable and robust solution to support the global transition toward low-carbon, sustainable energy infrastructures.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"82 ","pages":"Article 104516"},"PeriodicalIF":7.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of artificial light spectral quality for enhancing growth, nutrient uptake and resource use efficiency of pak choi cv. Choko in indoor agriculture","authors":"Mahesh R ,&nbsp;Murtaza Hasan ,&nbsp;D.K. Singh ,&nbsp;R.N. Sahoo ,&nbsp;Soora Naresh Kumar ,&nbsp;Md. Yeasin ,&nbsp;Nand Lal Kushwaha ,&nbsp;Roaf Ahmad Parray","doi":"10.1016/j.seta.2025.104566","DOIUrl":"10.1016/j.seta.2025.104566","url":null,"abstract":"<div><div>Global challenges including population growth, urbanization, resource scarcity and climate change demand transformative innovations in indoor agriculture to enhance resource efficiency and ensure sustainable crop production. The present study evaluates the impact of artificial light spectral treatments on the growth, nutrient uptake and resource use efficiency (RUE) of pak choi (<em>Brassica rapa</em> var. <em>chinensis</em>) in a controlled multi-tier indoor cultivation system. Four light treatments were tested including white + red and blue (W+RB), red and blue (RB), white + blue (W + B) and control (white LED). W+RB significantly improved plant height (21.17 cm), leaf area (471.79 cm²), NDVI and biomass. Enhanced root architecture under W+RB correlated with increased nutrient uptake of nitrogen, phosphorus, potassium, calcium, zinc and iron, underscoring the synergy between root development and nutrient absorption. Additionally, W+RB demonstrated superior resource efficiency, achieving an electrical energy use efficiency of 0.15, water-use efficiency of 77.01 g FW L⁻¹ and light-use efficiency of 0.41 g DW mol⁻². These findings highlight W+RB’s potential to boost productivity and sustainability in indoor agriculture. Future research should scale its application across crops, integrate renewable energy solutions and further enhance resource efficiency to address global food security challenges sustainably.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"82 ","pages":"Article 104566"},"PeriodicalIF":7.0,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of processes and mechanisms driving structural and mechanical evolution in lithium-ion batteries","authors":"Huzaifa Rauf ,&nbsp;Muhammad Khalid ,&nbsp;Naveed Arshad","doi":"10.1016/j.seta.2025.104545","DOIUrl":"10.1016/j.seta.2025.104545","url":null,"abstract":"<div><div>Performance degradation in lithium-ion batteries (LIBs) is often accompanied by structural and mechanical changes that pose safety risks, including internal pressure buildup, deformation, fire hazards and enclosure rupture. Understanding and controlling these structural and mechanical changes is vital to maintaining battery safety and reliability, particularly as LIBs are central to advancing global electrification and achieving net-zero emissions targets. This review systematically examines the processes responsible for such structural evolution in LIBs, distinguishing between reversible mechanical expansion caused by electrode expansion and irreversible deformation due to gas generation and interfacial degradation. The influence of temperature extremes and cycling protocols on the accumulation of mechanical stress is studied. Key mitigation strategies, including material optimization, design improvements, and operational protocols, are discussed to address these challenges. This review contributes to the advancement of battery safety for applications in electric vehicles and renewable energy systems, while also highlighting areas where further research is needed.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"82 ","pages":"Article 104545"},"PeriodicalIF":7.0,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated technoeconomic and environmental assessment of biomethane production via anaerobic digestion of food waste in Ireland: Updated insights under market volatility","authors":"Andrés Martínez-Arce ,&nbsp;Wriju Kargupta ,&nbsp;Carmen Girón Domínguez ,&nbsp;Emmanuel Alepu Odey ,&nbsp;Helena McMahon ,&nbsp;Jorge Diaz Huerta ,&nbsp;Jesko Zimmermann ,&nbsp;George Bishop ,&nbsp;James Gaffey ,&nbsp;David Styles","doi":"10.1016/j.seta.2025.104563","DOIUrl":"10.1016/j.seta.2025.104563","url":null,"abstract":"<div><div>This study presents an integrated techno-economic and environmental assessment of a biomethane plant in Ireland, processing 50,000 tonnes of food waste annually via anaerobic digestion (AD), according to the National Biomethane Strategy. SuperPro Designer simulation was employed to quantify material and energy flows, supporting a techno-economic analysis (TEA) and life cycle assessment (LCA).</div><div>The levelized cost of biomethane is estimated at 249 Euro/MWh, 2.9 times the 2019 benchmark, due to high capital (30.3 million Euro) and operational (4.2 million Euro/year) costs driven by inflationary effects caused by external events, such as the war in Ukraine. A biomethane selling price of 111.7 Euro/MWh (household gas price) and gate fees above 69 Euro/tonne are required for breakeven. Unfavourable market conditions would inevitably drive the need for policy supports such as renewable energy incentives or carbon credits to achieve profitability.</div><div>The LCA shows a net climate benefit of 46 kgCO₂eq/tonne. Still, under-estimated methane leaks could offset these gains and cause revenue losses over 100,000 Euro/year. Digestate circularity following the Nitrates Directive presents trade-offs depending on the impact category.</div><div>This integrated analysis reinforces the economic challenges and environmental potential of biomethane production, offering key insights for advancing Ireland’s circular bioeconomy and renewable energy goals.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"82 ","pages":"Article 104563"},"PeriodicalIF":7.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent heat storage enables self-sustaining endurance in unmanned underwater vehicles","authors":"Jiadian Wang ,&nbsp;Deming Li ,&nbsp;Zhe Wu ,&nbsp;Haonan Sha ,&nbsp;Chengbin Zhang ,&nbsp;Tieyu Gao","doi":"10.1016/j.seta.2025.104533","DOIUrl":"10.1016/j.seta.2025.104533","url":null,"abstract":"<div><div>Unmanned underwater vehicles (UUVs) are critical assets for ocean exploration and marine-resource development. To address the issues of limited endurance, suboptimal manoeuvrability, and restricted payload capacity in existing UUVs, this paper proposes a method to harness ocean thermal energy through a solid–liquid phase change during the cyclical motion of a UUV from the seabed to the sea surface. A power generation system based on the organic Rankine cycle was designed for UUVs. The system transforms ocean thermal energy into electrical energy for driving UUVs. The feasibility and operational characteristics of the system were evaluated by developing a dynamic model for the power generation system. The results of this study indicate that the proposed system can effectively support the self-sustained operations of UUVs. A power generation system equipped with 210 kg of a phase change material required approximately 6000 and 1200 s for energy storage and power generation, respectively. The system achieved a peak power output of 1600 W and a total energy generation of 1920 kJ. During the power generation stage, the ratio of time required for latent heat release to that for sensible heat release was approximately 8:1, with the ratio of corresponding energy outputs being approximately 10:1.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"82 ","pages":"Article 104533"},"PeriodicalIF":7.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrogen refueling siting: A case study from China on the influence of commercial entities","authors":"Hui Fang ,&nbsp;Ping Ma ,&nbsp;XiaoLei Wang ,&nbsp;NaiRong Tan ,&nbsp;Tao Ma","doi":"10.1016/j.seta.2025.104556","DOIUrl":"10.1016/j.seta.2025.104556","url":null,"abstract":"<div><div>This study introduces machine-learning techniques to analyze the impact of surrounding commercial entities on hydrogen refueling station (HRS) site selection. A large-scale multi-entity dataset is established through field research and data fusion, and the random forest (RF) algorithm is used to quantify the importance of influencing factors, thereby overcoming the subjectivity bias in existing studies. The cross-validation results suggest that the RF model has high stability and generalizability for HRS site selection. In addition, the RF model excels in classification tasks and maintains consistent performance across different datasets. This study provides valuable insights into HRS site selection by incorporating commercial entity data and leveraging machine-learning techniques.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"82 ","pages":"Article 104556"},"PeriodicalIF":7.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}