Search Results (1,626)

The optimization of grinding is a multi-objective problem characterized by high dimensionality, non-linearity, and complexity. Solving this multi-objective optimization (MOO) problem is one of the most challenging tasks in the field of mechanical engineering. In-depth research on multi-objective parameter optimization technology for grinding is of great significance for improving processing efficiency, optimizing product quality, and reducing energy consumption. This paper takes the multi-objective optimization problem of grinding as its starting point. First, it introduces the basic theory of multi-objective optimization and two primary methods for solving such problems: optimization target dimension reduction and multi-objective optimization. Second, the key technologies of the two methods are reviewed, including the modeling method of the optimization problem, the multi-objective optimization algorithm for solving the optimization model, and the prior and posterior trade-off methods used to obtain the compromised optimal solutions. Finally, the existing problems of the multi-objective optimization methods in grinding processing are summarized and the future development trends are predicted. This paper aims to provide researchers with a comprehensive understanding of the multi-objective optimization technology in grinding processing, enabling them to make more reasonable decisions when dealing with actual multi-objective optimization problems. Full article

The Urmia Lake Basin (ULB) in northwest Iran faces critical water management challenges significantly impacting agricultural sustainability and regional water–food security. This study presents a novel framework employing multi-objective linear programming to optimize crop selection and resource allocation strategies, addressing critical trade-offs inherent within the water–energy–food (WEF) nexus. Central to this framework is the Water–Energy–Food Nexus Index (WEFNI), which integrates seven pivotal productivity indicators: water consumption indicator (WCI), energy consumption (EC), water mass productivity (WMP), energy mass productivity (EMP), economic water productivity (EWP), and economic energy productivity (EPE). The analysis leverages 22 years of agricultural data (1995–2016) for the primary crops (wheat, barley, sugar beet, alfalfa, corn, and fruits) cultivated within the basin. Three distinct optimization scenarios are assessed: maximizing combined WEF productivity and economic returns (Sc₁); maximizing WEF productivity with minimized water consumption (Sc₂); maximizing economic returns under stringent water use limitations (Sc₃). Results consistently identify corn as the superior crop in terms of water–energy efficiency, whereas sugar beet demonstrated the lowest overall performance. This robust optimization approach elucidates critical trade-offs, providing actionable insights for policymakers managing similar water-stressed regions, although specific regional calibrations are necessary. Full article

With the advancement of 6G networks, terrestrial centralized network architectures are evolving toward integrated space–air–ground network frameworks, imposing higher requirements on the efficiency of computation offloading and multi-objective collaborative optimization. However, existing single-decision strategies in integrated space–air–ground networks find it difficult to achieve coordinated optimization of delay and load balancing under energy tolerance constraints during task offloading. To address this challenge, this paper integrates communication transmission and computation models to design a two-stage computation offloading model and formulates a multi-objective optimization problem under energy tolerance constraints, with the primary objectives of minimizing overall system delay and improving network load balance. To efficiently solve this constrained optimization problem, a two-stage computation offloading solution based on a Hierarchical Cooperative African Vulture Optimization Algorithm (HC-AVOA) is proposed. In the first stage, the task offloading ratio from ground devices to unmanned aerial vehicles (UAVs) is optimized; in the second stage, the task offloading ratio from UAVs to satellites is optimized. Through a hierarchical cooperative decision-making mechanism, dynamic and efficient task allocation is achieved. Simulation results show that the proposed method consistently maintains energy consumption within tolerance and outperforms PSO, WaOA, ABC, and ESOA, reduces the average delay and improves load imbalance, demonstrating its superiority in multi-objective optimization. Full article

As the goal of carbon peak and carbon neutrality becomes a global consensus, the circular economy is gradually evolving from an environmental concept to a core lever for national strategy and industrial transformation. To achieve green and low-carbon development, China is accelerating the construction of a circular economy system, particularly in the fields of resource recycling and utilization. Industrial waste heat, a strategically critical supplementary energy resource, performs a pivotal role in advancing the circular economy. Based on an energy technology coupling model, this study assesses the waste heat utilization potential in China and quantitatively measures its impact on energy conservation and carbon reduction. The results show that: (1) The potential of industrial waste heat in China is characterized by an inverted U-shaped trajectory. Over the near-to-medium term, the steel and power industries remain the primary contributors to waste heat utilization potential. (2) Low-grade waste heat represents the majority of utilization potential in China’s industrial sector, mainly from power generation, fuel processing, and steel manufacturing. The model results indicate that the proportion of low temperature waste heat will increase from approximately 66% in 2025 to 83% in 2060. (3) Waste heat utilization significantly influences the energy transition pathway. The findings of this study demonstrate that energy-intensive industries have the potential to reduce primary energy consumption by more than 13%. Moreover, making full use of waste heat could accelerate China’s carbon peaking target to 2028, and reduce peak carbon emissions by an estimated 5.1%. Full article

Active citizen participation in both consumption and production is essential for a successful renewable energy transition. The paper explores the early development of prosumerism in Jerusalem, a city characterized by socio-political fragmentation and unequal access to infrastructure. Based on a 320-sample survey conducted in East and West Jerusalem, the paper analyzes awareness, motivation, and barriers to solar energy adoption in the city. The results show that only 12% of respondents currently produce and consume their own energy, while 66% had never heard of the term “prosumerism.” Financial savings were shown to be the primary driver of implementing solar systems, both in East and West Jerusalem. Key barriers included high installation costs, limited regulatory knowledge, and administrative complexity. Despite these obstacles, 70% of respondents expressed interest in community energy initiatives, highlighting significant potential for citizen-led models in fragmented urban contexts. Full article

Global warming has become a top priority on the international environmental policy agenda. The recent rise in CO₂ emissions observed in Türkiye has further emphasized the country’s critical role in addressing climate change. This study aims to estimate Türkiye’s CO₂ emissions through 2030 and identify the key socioeconomic and environmental factors driving these emissions, using multiple linear regression (MLR) and time series analysis methods. Six primary variables are examined: population, gross domestic product (GDP), CO₂ intensity, per capita energy consumption, total greenhouse gas (GHG) emissions, and forest area. This study introduces a new multivariate forecasting framework that integrates time series projections with multiple linear regression and elasticity-based sensitivity analysis, providing novel insight into the relative influence of key emission drivers compared to prior research. The results suggest that, if current policy trends persist, Türkiye’s CO₂ emissions will increase substantially by 2030. Variables such as GHG emissions, energy consumption, and population growth are found to have an increasing effect on emissions, while the limited expansion of forest areas is insufficient to offset this trend. In contrast, the negative correlation between GDP and CO₂ emissions suggests that economic growth can occur in alignment with environmental sustainability. The model’s validity is supported by a high R² (0.99) value and low error rates. The findings indicate that Türkiye must reassess its current strategies and strengthen policies targeting renewable energy, energy efficiency, and carbon sinks to achieve its climate goals. The proposed framework provides a transparent basis for climate planning and policy prioritization in Türkiye. Full article

The growing demand for energy-efficient buildings necessitates innovative approaches to reduce energy consumption during early design stages. While traditional physics-based simulations remain time- and expertise-intensive, automated machine learning (AutoML) offers a promising alternative by enabling data-driven building performance prediction with minimal human intervention. This study conducts a benchmark evaluation of AutoML’s potential in building energy applications through three objectives: (1) a literature review revealing AutoML’s nascent adoption (10 identified studies) and primary use cases (heating/cooling prediction, energy demand forecasting); (2) a benchmark comparing three AutoML frameworks (AutoGluon, H2O, Auto-sklearn) against baseline and ensemble ML models using R², RMSE, MSE, and MAE metrics; and (3) SHAP (SHapley Additive exPlanations)-based interpretability analysis. Results demonstrate AutoGluon’s superior accuracy (R² = 0.993, RMSE = 2.280 kWh/m²) in predicting energy performance, outperforming traditional methods. Key influential features, including solar heat gain coefficient (SHGC) and U-values, were identified through SHAP, offering actionable design insights. The primary novelty of this work lies in its two-step methodology: a focused review to identify pertinent AutoML frameworks, followed by a comparative benchmarking of these frameworks against traditional ML for early-stage prediction. This process substantiates AutoML’s potential to democratize energy modeling and deliver practical, interpretable workflows for architectural design. Full article

African catfish (Clarias gariepinus, AC) is one of the most widely farmed freshwater fish species in Central Europe. Processing operations generate up to 55% by-products (BPs), predominantly carcasses rich in proteins, lipids, and minerals. This study develops a comprehensive valorization process for ACBPs to recover gelatin, protein hydrolysate, fish oil, and pigments. The processing protocol consisted of sequential washing, oil extraction, demineralization, and biotechnological treatment to disrupt the collagen quaternary structure. A two-factor experimental design was employed to optimize the processing conditions. The factors included the extraction temperatures of the first (35–45 °C) and second fraction (50–60 °C). We hypothesized that enzymatic conditioning, combined with sequential hot-water extraction, would yield gelatin with properties comparable to those of mammalian- and fish-derived gelatins, while enabling a near-zero-waste process. The integrated process yielded 18.2 ± 1.2% fish oil, 9.8 ± 2.1% protein hydrolysate, 1.7 ± 0.7% pigment extract, and 25.3–37.8% gelatin. Optimal conditions (35 °C/60 °C) produced gelatin with gel strength of 168.8 ± 3.6 Bloom, dynamic viscosity of 2.48 ± 0.02 mPa·s, and yield of 34.76 ± 1.95%. Life cycle assessment (LCA) identified two primary environmental hotspots: water consumption and energy demand. This near-zero-waste biorefinery demonstrates the potential for comprehensive valorization of aquaculture BPs into multiple value-added bioproducts. Full article

Driven by the dual imperatives of global energy transition and sustainable development goals, lithium-ion batteries, as critical energy storage carriers, have seen the assessment of their lifecycle reliability and durability become a core issue underpinning the sustainable operation of clean energy systems. Grounded in a multidimensional perspective of sustainable development, this study aims to establish a quantifiable and monitorable battery reliability evaluation framework to address the challenges to lifespan and performance sustainability faced by batteries under complex multi-stress coupled operating conditions. Lithium-ion batteries are widely used across various fields, making an accurate assessment of their reliability crucial. In this study, to evaluate the lifespan and reliability of lithium-ion batteries when operating in various coupling stress environments, a multi-stress collaborative accelerated model(MCAM) considering interaction is established. The model takes into account the principal stress effects and the interaction effects. The former is developed based on traditional acceleration models (such as the Arrhenius model), while the latter is constructed through the combination of exponential, power, and logarithmic functions. This study firstly considers the scale parameter of the Weibull distribution as an acceleration effect, and the relationship between characteristic life and stresses is explored through the synergistic action of primary and interaction effects. Subsequently, a multi-stress maximum likelihood estimation method that considers interaction effects is formulated, and the model parameters are estimated using the gradient descent algorithm. Finally, the validity of the proposed model is demonstrated through simulation, and numerical examples on lithium-ion batteries demonstrate that accurate lifetime prediction is enabled by the MCAM, with test duration, cost, and resource consumption significantly reduced. This study not only provides a scientific quantitative tool for advancing the sustainability assessment of battery systems, but also offers methodological support for relevant policy formulation, industry standard optimization, and full lifecycle management, thereby contributing to the synergistic development of energy storage technology across the economic, environmental, and social dimensions of sustainability. Full article

Growing demand for computing resources is increasing electricity use and cooling needs in data centres (DCs). Simultaneously, it creates opportunities for decarbonisation through the integration of waste heat (WH) into district heating (DH) systems. Such integration reduces primary energy (PE) consumption and emissions, particularly in low-temperature DH networks. In this study, the possibility for utilisation of WH from DC hybrid cooling system into third generation (3G), fourth generation (4G), and fifth generation (5G) DH systems is investigated. The work is based on the dynamic simulations in TRNSYS. The model of the hybrid cooling system consists of a direct liquid cooling (DLC) loop (25–30 °C) and a chilled water rack coolers (CRCC) loop (10–15 °C). For 3G DH, a high-temperature water-to-water heat pump (HP) is applied to ensure the required water temperature in the system. Measured meteorological and equipment data are used to reproduce real DC operating conditions. Relative to the reference system, integrating WH into 5G DH reduces PE consumption and CO₂ emissions by 88%. Results indicate that integrating WH into 5G DH and 4G DH minimises global cost and achieves a payback period of less than one year, whereas 3G DH, requiring high-temperature HPs, achieves 14 years. This approach to integrating waste heat from a hybrid DLC+CRCC DC cooling system is technically feasible, economically and environmentally viable for planning future urban integrations of waste heat into DH systems. Full article

This article presents a comprehensive analysis of the primary electrical power distribution system in hybrid-electric aircraft, with particular emphasis on its environmental performance assessed through Life Cycle Assessment (LCA). High-resolution Life Cycle Inventory (LCI) data were developed in collaboration with industry partners and refined to reflect current production standards. The results indicate that printed circuit boards (PCBs), magnets, precious metals (gold and silver), and copper are the primary contributors to environmental impact, with PCBs alone accounting for over 50% of material-related emissions. Although precious metals constitute only 0.014% of the product’s mass, they account for nearly 9% of total emissions due to the energy-intensive nature of their mining and refining processes. Additionally, manufacturing stages involving thermal treatments—such as surface coating of iron cores at 850 °C for 14 h—significantly increase energy consumption and associated emissions. The study concludes with recommendations for reducing the carbon footprint of future aircraft power systems through improved material efficiency, process optimization, and supply chain sustainability. Full article

This study presents a comprehensive economic and environmental evaluation of immobilized lipases produced on eggshell membrane-based carriers from eggshell waste, based on laboratory-scale experiments. By integrating economic analysis (EA) and life cycle analysis (LCA), the key factors affecting the economic viability and environmental impact of the process were identified, supporting sustainable and circular biorefinery concepts. The EA estimated the total process cost at EUR 25.63 for 15 g of product, while the effective net cost was negative (EUR −14.81) due to the valorization of anhydrous calcium chloride as a valuable by-product. The effective net cost reduction from by-product valorization of the immobilized lipase was estimated at 0.99 EUR/g as the minimum selling price (MSP). When expressed per unit of enzymatic activity, the immobilized lipase on the eggshell waste membrane-based carrier shows a substantially lower cost (EUR/U) compared with representative commercial immobilized lipases, demonstrating clear catalytic cost-efficiency advantages. The cradle-to-gate life cycle assessment, conducted using ReCiPe 2016 quantification methods, highlighted electricity consumption during drying as the primary environmental hotspot, accounting for up to 57% of the global warming potential. Sensitivity and uncertainty analyses showed that energy consumption strongly influences the impact in terms of climate change and fossil resource depletion, while the impact of chemical use was minimal. These results show that energy-efficient process optimization, especially in the drying phase, is crucial for further improving environmental and economic performance. These results indicate that optimizing energy efficiency, especially during the drying phase, is crucial for further improving the production process of immobilized lipases on eggshell membrane-based carriers, both environmentally and economically. Full article

In recent decades, energy efficiency policies have increasingly focused on reducing buildings’ energy use and improving their performance. However, by overlooking the entire life cycle of a building, a considerable portion of its environmental impact has indeed been kept out of the process. As a result, even leading buildings that have advanced toward Zero-Energy status may not that as innocent as promised by evaluating environmental impacts during their whole life. Consequently, a logical method for achieving nearly Zero Energy Buildings (nZEBs) involves implementing energy-efficient measures and proper materials throughout the entire life cycle of buildings. This paper is one of its first kinds that includes all building systems and materials embodied energy and cost to explore the possibility of creating nearly zero residential buildings through their life cycle. Life-cycle energy consumptions, life-cycle CO₂ emissions and life-cycle cost of nZEB retrofit packages for a five-storey, 20-apartment residential building in Ankara, Turkey were evaluated. The methodology couples dynamic simulation (DesignBuilder/EnergyPlus) with an EN 15978-aligned boundary (A1–A5, B, C). The study highlights the critical role of both operational and embodied energy and carbon emissions in the pursuit of nZEBs. The best nZEB package reduces primary energy by ~55% and life-cycle CO₂ by ~45% relative to the reference building over 50 years, while cost-optimal packages deliver 6–7% lower global cost. These findings demonstrate the effectiveness of life cycle assessment in measuring building environmental impact, the utilization of renewable energy, and the optimization of building materials in reducing energy consumption and emissions, providing a sustainable and cost-efficient approach to residential building design. Full article

Machining nickel-based superalloys, such as Inconel 718, poses a significant technological challenge due to their high-temperature strength and low thermal conductivity, leading to rapid tool wear. This paper presents a comprehensive comparative analysis of two roughing strategies: high-feed milling and plunge milling, utilizing a unique custom-designed milling head. The primary objective was to evaluate the impact of tool material reinforcement on the process by comparing SiC whisker-reinforced ceramic inserts (CW100) with non-reinforced inserts (CS300). The experiment involved measuring cutting force components, power consumption, and analyzing tool wear progression (VBB) and mechanisms. Results showed that the presence of the reinforcing phase is critical for reducing the axial force component (Fz), particularly in plunge milling, where CW100 inserts achieved a 30–35% force reduction and avoided the catastrophic failure observed in non-reinforced ceramics. Microscopic analysis confirmed that composite inserts undergo predictable abrasive wear, whereas CS300 inserts are prone to brittle fracture and spalling. Multi-criteria optimization using Grey Relational Analysis (GRA) identified high-feed milling with reinforced inserts as the most efficient strategy, while also positioning plunge milling with composites as a competitive, less energy-intensive alternative. Full article

To address the challenges of enhancing driving stability and energy efficiency in distributed-drive electric vehicles, this paper proposes an extenics coordinated torque distribution control method that integrates energy efficiency optimization and vehicle stability. The primary contribution was the development of a vehicle stability assessment method grounded in extenics control theory, which was used to obtain the vehicle’s phase plane and stability region. Subsequently, an objective function with constraints for in-wheel motor torque distribution was formulated, targeting both optimal energy efficiency and maximum tire stability margin. Furthermore, the extension distances from the actual vehicle state to the stability boundaries were computed to determine adaptive weighting coefficients for these dual objectives. Finally, a Matlab/Simulink 2018a and Carsim2019 co-simulation platform was built to implement and test the proposed method. Simulations under the NEDC urban driving cycle and double-lane-change driving conditions were conducted to evaluate the following three distribution strategies: energy-optimal, stability-oriented, and extenics coordinated control. The results demonstrated that, regarding vehicle stability performance, extenics coordinated control showed a slightly inferior performance to the stability-oriented approach but substantially outperformed the energy-optimal strategy. In terms of energy consumption, the energy-optimal strategy achieved the lowest loss and the stability-oriented strategy showed the highest, while the extenics coordinated control presented intermediate results of 5.4 × 10⁹ J and 9.7 × 10⁷ J, respectively, under two driving conditions, representing reductions of 2.17% and 11.2% compared to the stability-oriented method. The proposed torque distribution method establishes an effective balance between energy-optimal and stability-oriented objectives. This method not only ensures satisfactory driving stability, but also reduces energy loss in in-wheel motors. Full article