Search Results (1,438)

The insufficient effectiveness of the European packaging waste policy has prompted the European Union to adopt more decisive measures in 2025. The Packaging and Packaging Waste Regulation of 2024 obliges Member States to use deposit–return systems to achieve high collection rates for beverage packaging and, as a result, to enhance packaging circularity. As evidence supporting this approach, i.e., that deposit systems indeed are an efficient solution for packaging waste collection, is still scattered, this article provides a systematic review of the evidence on various aspects of the use of deposit systems. A key finding of our review is that both scientific and empirical evidence support the European Union’s decision to make deposit–return systems mandatory: in European countries that have fully operational systems, the collection rates of packaging covered by these systems exceeded 85%. In addition to this positive contribution to packaging circularity, a significant (40–60%) reduction in littering is reported after implementation of the deposit systems. A significant novelty of this review is the presentation of the latest empirical data suggesting that deposit systems may be comparable to alternative collection methods in terms of costs to producers. Comprehensive assessments conducted using the cost–benefit analysis methods confirm that deposit systems generate net social benefits. It is suggested that innovations in logistics contribute to reduced environmental impacts of transport and transport-related costs. For this reason, updated life cycle assessments and cost–benefit analyses of deposit systems are needed to assess the role of deposit systems within the European circular economy framework. Full article

This research presents Geographic Information System (GIS) mapping and development of biomass for combined heat and power (CHP) generation in Nigeria. It includes crop and forest classification, thermodynamic, and exergo-economic analyses using ArcGIS, Engineering Equation Solver, and Microsoft Excel. Syngas generated from biomass residues powered an integrated CHP system combining a gas turbine (GT), dual steam turbine (DST), and a cascade organic Rankine cycle (CORC) plant. The net power output of the integrated system stood at 2911 MW, with a major contribution from the gas turbine cycle (GTC) unit. The system had a total exergy destruction of 6480 MW, mainly in the combustion chamber (2143 MW) and HP-HRSG (1660 MW), and produced 3370.41 MW of heat, with a flue gas exit temperature of 74 °C. The plant’s energy and exergy efficiencies were 87.16% and 50.30%, respectively. The BCHP system showed good economic and environmental performance, with an annualized life cycle cost of USD 93.4 million, unit cost of energy of 0.0076 USD/kWh, and a 7.5-year break-even. The emissions and impact factors align with those of similar existing plants. It demonstrates that biomass residue can significantly support Nigeria’s energy needs and contribute to clean energy goals under the Paris Agreement and UN-SDGs. This work suggests a pathway to tackle energy insecurity, inform policymakers on biomass-to-energy, and serve as a foundation for future techno-economic–environmental assessment of biomass residues across suitable locations in Nigeria. Full article

The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem. Full article

Geothermal energy has been recognized as a promising renewable resource for sustainable power generation; however, the efficiency of conventional geothermal power plants has remained relatively low, and high investment costs have limited their competitiveness with other renewable technologies. In this context, the present study introduced an innovative geothermal electricity generation system aimed at enhancing energy efficiency, cost-effectiveness, and sustainability. Unlike traditional configurations, the system raised the geothermal source temperature passively by employing advanced heat transfer mechanisms, eliminating the need for additional energy input. Comprehensive energy, exergy, and exergoeconomic analyses were carried out, revealing a net power output of 43,210 kW and an energy efficiency of 30.03%, notably surpassing the conventional Kalina cycle’s typical 10.30–19.48% range. The system’s annual electricity generation was 11,138.53 MWh, with an initial investment of USD 3.04 million and a short payback period of 3.20 years. A comparative assessment confirmed its superior thermoeconomic performance. In addition to its technoeconomic advantages, the environmental performance of the proposed configuration was quantified. A streamlined life cycle assessment (LCA) was performed with a functional unit of 1 MWh of net electricity. The proposed system exhibited a carbon footprint of 20–60 kg CO₂ eq MWh⁻¹ (baseline: 45 kg CO₂ eq MWh⁻¹), corresponding to annual emissions of 0.22–0.67 kt CO₂ eq for the simulated output of 11,138.53 MWh. Compared with coal- and gas-fired plants of the same capacity, avoided emissions of approximately 8.6 kt and 5.0 kt CO₂ eq per year were achieved. The water footprint was determined as ≈0.10 m³ MWh⁻¹ (≈1114 m³ yr⁻¹), which was substantially lower than the values reported for fossil technologies. These findings confirmed that the proposed system offered a sustainable alternative to conventional geothermal and fossil-based electricity generation. Multi-objective optimization using NSGA-II was carried out to maximize energy and exergy efficiencies while minimizing total cost. Key parameters such as turbine inlet temperature (459–460 K) and ammonia concentration were tuned for performance stability. A sensitivity analysis identified the heat exchanger, the first condenser (Condenser 1), and two separators (Separator 1, Separator 2) as influential on both performance and cost. The exergoeconomic results indicated Separator 1, Separator 2, and the turbine as primary locations of exergy destruction. With an LCOE of 0.026 USD/kWh, the system emerged as a cost-effective and scalable solution for sustainable geothermal power production without auxiliary energy demand. Full article

This paper presents a comprehensive sensitivity analysis of the pioneering real-world deployment of computer vision-enabled construction waste sorting in Finland, implemented by a leading provider of robotic recycling solutions. Building upon and extending the findings of prior field research, the study analyzes an industry flagship case to examine the financial feasibility of computer vision-enabled robotic sorting compared to conventional sorting. The sensitivity analysis covers cost parameters related to labor, wages, personnel training, machinery (including AI software, hardware, and associated components), and maintenance operations, as well as capital expenses. We further expand the existing cost model by integrating the net present value (NPV) of investments. The results indicate that the computer vision-enabled automated system (CVAS) achieves cost competitiveness over conventional sorting (CS) under conditions of higher labor-related costs, such as increased headcount, wages, and training expenses. For instance, when annual wages exceed EUR 20,980, CVAS becomes more cost-effective. Conversely, CS retains cost advantages in scenarios dominated by higher machinery and maintenance costs or extremely elevated discount rates. For example, when the average machinery cost surpasses EUR 512,000 per unit, CS demonstrates greater economic viability. The novelty of this work arises from the use of a pioneering real-world case study and the improvements offered to a comprehensive comparative cost model for CVAS and CS, and furthermore from clarification of the impact of key cost variables on solution (CVAS or CS) selection. Full article

In arid countries like Oman, fan–pad cooling systems are commonly used in greenhouse cultivation. However, in such harsh environmental conditions, a fan–pad cooling system can be inefficient, result in high water and energy consumption, and may cause plant and soil pathogens issues. To address these challenges, this study evaluated the technical performance of a greenhouse designed with the new concept of an on-grid, solar-powered, and fan-chiller tube bank (FCTB) cooling system, focusing on water use efficiency (WUE) and energy use efficiency (EUE) following pot-grown okra. In addition, greenhouse gas (GHG) emissions and financial aspects were evaluated through cost–benefit and cash flow analyses. This research was conducted with a Quonset side-walled single-span greenhouse equipped with a solar-powered FCTB cooling system and automatic scheduled irrigation system. Water and electricity consumption was recorded, and surplus energy supplied to the electricity grid was estimated. The greenhouse efficiencies were evaluated by computing the EUE, total WUE, cooling water use efficiency (CWUE), and irrigation water use efficiency (IWUE). The solar-powered FCTB greenhouse enhanced EUE, achieving a value of 1.16 and a positive net energy of 163.87 MJ·m⁻². The WUE, CWUE, and IWUE were 0.91 kg·m⁻³, 1.63 kg·m⁻³, and 2.07 kg·m⁻³, respectively. The economic assessment showed that okra cultivation with a solar-powered FCTB cooling system was economically unfeasible, as indicated by a benefit–cost ratio of 0.88. However, cucumber (IRR 46%, NPV 2.13 × 10⁴ USD) and cherry tomatoes (IRR 38%, NPV 1.98 × 10⁴ USD) demonstrated economic feasibility as supported by positive net present value (NPV) and the internal rate of return (IRR) values. Furthermore, incorporating solar energy with the FCTB cooling system enhanced the greenhouse’s sustainability, efficiencies, and profitability. This study recommends further research with this system for Oman’s seasonal effect with high-value crops and optimizing the size of the solar panel system to see how the energy and other efficiency components will vary. Full article

Microgrids (MGs) have emerged as pivotal players in the energy transition by enabling the efficient integration of distributed energy resources and the provision of ancillary services to the power system. Despite their technical capabilities, MGs still face economic and regulatory barriers that hinder their widespread deployment in electricity markets. This paper presents a structured methodological framework to assess the economic viability of MGs delivering services such as peak shaving, loss compensation, and voltage support, among others. The proposed approach considers three distinct scenarios: (1) MGs supplying energy to local loads, (2) hybrid MGs combining local supply with ancillary services, and (3) MGs exclusively dedicated to ancillary services. The framework incorporates adjusted levelized cost of electricity (LCOE), levelized avoided cost of electricity (LACE), and net value metrics, while accounting for tax incentives and market price signals. A case study based in Colombia (Cali and Camarones) validates the framework through simulations conducted in HOMER Pro V3.18.4 and MATLAB Online. The results indicate that remuneration schemes based on availability and service utilization significantly enhance the viability of MGs. The proposed methodology is applicable to emerging regulatory environments and offers guidance for designing public policies that promote the active participation of MGs in supporting grid operations. Full article

Access to clean cooking technologies is crucial for achieving SDG7 for remote households in small Pacific Islands like Fiji and for developed countries alike. Many small households in Fiji still rely on traditional biomass for cooking. This study explores the environmental sustainability and clean cooking potential of the Home Biogas 2.0 flexible balloon digester installed at Kamil Muslim College in Ba, Fiji. Comparative bench experiments were also performed. The bench-scale experiments produced higher biogas yields than the digester trials, with optimal outputs recorded from fresh cow dung (541 mL of cumulative biogas) and vegetable waste excluding rice (125 mL). When scaled, annual energy production from fresh cow dung reached 4644.64 MJ, equivalent to replacing 7.82 standard LPG cylinders, while vegetable waste produced 3763.76 MJ, offsetting 6.34 cylinders. Notably, biogas from cow dung exceeded the estimated annual household cooking demand of 3840 MJ for a family of four persons. The biogas produced from fresh cow dung provided an average cooking duration of 1 h 29 min, while biogas from vegetable waste lasted for 1 h 21 min. The economic analysis indicated that combining liquid digestate, used as biofertiliser, and biogas from cow dung resulted in the highest financial return, with a 67% Internal Rate of Return, a Net Present Value of $12,364.30, a Benefit Cost Ratio of 5.12, and a Discounted Payback Period of 1.28 years. This indicates the potential of Home Biogas 2.0 as a climate-smart technology that integrates renewable energy production, waste reduction, and sustainable agriculture, making it particularly suitable for rural and remote communities. Full article

Progressive climate change and the increasing concentration of carbon dioxide in the atmosphere represent one of the most serious challenges facing modern energy systems. At the same time, the global overproduction of plastics, particularly polyethylene terephthalate (PET), places a significant burden on the natural environment and waste management infrastructure. Electrochemical reactors offer a promising solution by enabling the simultaneous conversion of CO₂ and EG into valuable products such as carbon monoxide and glycolic acid, using electricity derived from renewable energy sources. Carbon monoxide can be further processed into high-energy synthetic fuels, such as propanol, while glycolic acid holds substantial importance in the pharmaceutical and plastics industries. An economic analysis was conducted to estimate the capital expenditures required for an electrochemical reactor and to assess the investment’s profitability based on the net present value (NPV) indicator. In addition, a Life Cycle Assessment (LCA) was carried out to evaluate the environmental impact of the proposed technology, with particular attention to its carbon footprint. The results indicate that the profitability of the system strongly depends on the market price and purity of glycolic acid, as well as on access to low-cost renewable electricity. The LCA confirms a significantly lower carbon footprint compared to conventional CO production, though further technological advancements are required for industrial deployment. Full article

Water and energy are strongly intertwined, especially in wastewater treatment plants (WWTPs) whose electrical loads can strain local grids. This work evaluates the technical, economic, and environmental feasibility of powering the WWTP attached to the smart building of Ibn Tofail University (Morocco) with building-integrated photovoltaics (PV) and a complementary wind turbine. Using the HOMER Pro optimizer, two configurations were compared: (i) stand-alone PV and (ii) a hybrid PV/wind system. The hybrid design raises the renewable energy fraction from 8.5% to 17.9%, cutting annual grid purchases by 8% and avoiding 47.9 t CO₂ yr⁻¹. The levelized cost of electricity decreases from 1.08 to 0.97 MAD kWh⁻¹ (≈0.11 to 0.10 USD kWh⁻¹), while the net present cost drops by 6%. Sensitivity analyses confirm robustness under grid electricity tariff and load-growth uncertainties. These results demonstrate that modest wind additions can double the renewable share and improve economics, offering a replicable pathway for WWTPs and smart buildings across the MENA region. Full article

In order to ensure the earthquake safety of existing buildings, retrofitting applications come to the fore in terms of being fast and cost-effective. Among these applications, fiber-reinforced polymer (FRP) composites are widely preferred thanks to their advantages such as high strength, corrosion resistance, applicability without changing the cross-section and easy assembly. This study presents a data augmentation, modeling, and comparison-based approach to predict the fire resistance (FR) of FRP-strengthened reinforced concrete beams. The aim of this study was to explore the role of data augmentation in enhancing prediction accuracy and to find out which augmentation method provides the best prediction performance. The study utilizes an experimental dataset taken from the existing literature. The dataset contains inputs such as varying geometric dimensions and FRP-strengthening levels. Since the original dataset used in the study consisted of 49 rows, the data size was increased using augmentation methods to enhance accuracy in model training. In this study, Gaussian noise, Regression Mixup, SMOGN, Residual-based, Polynomial + Noise, PCA-based, Adversarial-like, Quantile-based, Feature Mixup, and Conditional Sampling data augmentation methods were applied to the original dataset. Using each of them, individual augmented datasets were generated. Each augmented dataset was firstly trained using eXtreme Gradient Boosting (XGBoost) with 10-fold cross-validation. After selecting the best-performing augmentation method (Adversarial-like) based on XGBoost results, the best-performing augmented dataset was later evaluated in HyperNetExplorer, a more advanced NAS tool that can find the best performing hyperparameter optimized ANN for the dataset. ANNs achieving R² = 0.99, MSE = 22.6 on the holdout set were discovered in this stage. This whole process is unique for the FR prediction of structural elements in terms of the data augmentation and training pipeline introduced in this study. Full article

The complexity of smart home technologies and the need for personalized energy efficiency solutions highlight the importance of user-oriented decision-support tools. This study presents a Smart Home Management System (SHMS) selection solution that combines a web-based dashboard, a mobile application, and a relational database. A 54-question structured questionnaire was designed to capture user requirements, and four alternatives—KNX, JUNG Home, LB Management, and eNet Smart Home—were compared using the Simple Additive Weighting (SAW) method. Evaluation criteria included installation complexity, communication technology, integration and control capabilities, and user experience. The system was implemented with Next.js, React Native, and Post-greSQL, ensuring flexibility, scalability, and secure data management. Preliminary evaluation with specialists (system integrators, architects, designers) and students confirmed the coherence of the questionnaire, the adequacy of criteria, and the clarity of recommendations. Results showed that the tool improves user engagement, reduces decision-making uncertainty, and supports the adoption of energy-efficient residential solutions. The study’s main limitation is the small test sample, which will be expanded in future large-scale validation. Planned improvements include interactive product comparisons, cost estimation, adaptive questionnaire logic, and 3D visualizations. Overall, the system bridges the gap between technical SHMS solutions and user-oriented decision-making, offering practical and academic value. Full article

This paper presents a modular and distributed supervisory control integration framework for intelligent micro-manufacturing systems (MMSs) under event-level failures. Addressing the increasing demand for scalable and reliable supervisory control in both micro- and smart manufacturing, the proposed approach equips each subsystem with a detector automaton that classifies runtime states into Strictly robust, Recoverably robust, or Non-robust categories. Distributed supervisors then make real-time local decisions to ensure fault-tolerant evolution of system behaviors. Unlike conventional centralized or Petri net-based methods, the proposed automaton-based framework supports modular design and structural scalability. Quantitative comparisons show that the robustness-detection cost scales approximately linearly with the summed sizes of local graphs, indicating good structural scalability. Simulation studies validate the feasibility and scalability of the framework, demonstrating its effectiveness in maintaining production cycle reachability and its integration potential for micro-electro-mechanical systems (MEMS)-based production lines, micro-fabrication platforms, and smart factory environments. These results confirm that the proposed method can serve as a robust and deployable control layer for next-generation intelligent and micro-manufacturing integration architectures. Full article

In clinical medicine, rats are commonly used as experimental subjects. However, their estrous cycle significantly impacts their biological responses, leading to differences in experimental results. Therefore, accurately determining the estrous cycle is crucial for minimizing interference. Manually identifying the estrous cycle in rats presents several challenges, including high costs, long training periods, and subjectivity. To address these issues, this paper proposes a classification network, Spatial Long-distance EfficientNet (SLENet). This network is designed based on EfficientNet, specifically modifying the Mobile Inverted Bottleneck Convolution (MBConv) module by introducing a novel Spatial Efficient Channel Attention (SECA) mechanism to replace the original Squeeze Excitation (SE) module. Additionally, a non-local attention mechanism is incorporated after the last convolutional layer to enhance the network’s ability to capture long-range dependencies. On 2655 microscopy images of rat vaginal epithelial cells (with 531 test), SLENet achieves 96.31% accuracy, surpassing EfficientNet (94.20%). This finding provides practical value for optimizing experimental design in rat-based studies such as reproductive and pharmacological research, but this study is limited to microscopy image data, without considering other factors; thus, future work could incorporate temporal pattern and multi-modal inputs to further enhance robustness. Full article

The role of green and low-carbon energy (gLE) resources in realizing the envisaged future decarbonized energy generation and supply cannot be overemphasized. The world has witnessed growing attention to the application of green energy (gE) sources such as solar, wind, hydro, geothermal, and biomass (energy crops, biogas, biodiesel, etc.). There is also the existence of low-carbon energy (LE) resources such as power-to-X, power-to-fuel, power-to-gas, e-fuel, waste-to-energy, etc., which possess huge potential for delivering sustainable energy, thus facilitating a pathway for achieving the desired environmental sustainability. In addition, the evolution of the cyber-physical power systems and the need for strengthening capacity in advanced energy materials are among the key factors that drive the deployment of gLE technologies around the world. This paper, therefore, presents the recent global developments in gLE resources, including the trends in their deployments for different applications in commercial premises. The study introduces different conceptual technical models and configurations of energy systems; the potential of multi-energy generation in a microgrid (m-grd) based on the gLE resources is also explored using the System Advisor Model (SAM) software. The m-grd is being fueled by solar, wind, and fuel cell resources for supplying a commercial load. The quantity of carbon emissions avoided by the m-grd is evaluated compared to a purely conventional m-grd system. The paper presents the cost of energy and the net present cost of the proposed m-grid; it also discusses the relevance of carbon capture and storage and carbon sequestration technologies. The paper provides deeper insights into the understanding of clean and unconventional energy resources. Full article