Search Results (2,298)

Mining (1) operations in remote areas (2) face significant challenges related to energy supply, high fuel costs, and limited infrastructure. This study investigates the potential for achieving energy independence (3) and resilience (4) in such environments through the integration of renewable energy sources (5) and battery–electric mining equipment. Using the “Studena Vrila” underground bauxite mine as a case study, a comprehensive techno-economic and environmental analysis was conducted across three development models. These models explore incremental scenarios of solar and wind energy adoption combined with electrification of mobile machinery. The methodology includes calculating levelized cost of energy (LCOE), return on investment (ROI), and greenhouse gas (GHG) reductions under each scenario. Results demonstrate that a full transition to RES and electric machinery can reduce diesel consumption by 100%, achieve annual savings of EUR 149,814, and cut GHG emissions by over 1.7 million kg CO₂-eq. While initial capital costs are high, all models yield a positive Net Present Value (NPV), confirming long-term economic viability. This research provides a replicable framework for decarbonizing mining operations in off-grid and infrastructure-limited regions. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Under the global context of climate governance and sustainable development, low-carbon energy transition has become a strategic imperative. As a critical force in resource allocation, the financial system’s impact on energy transition has attracted extensive academic attention. This paper presents the first comprehensive literature review on energy transition research in the context of financial development. We develop a “Financial Functions-Energy Transition Dynamics” analytical framework to comprehensively examine the theoretical and empirical evidence regarding the relationship between financial development (covering both traditional finance and emerging finance) and energy transition. The understanding of financial development’s impact on energy transition has progressed from linear to nonlinear perspectives. Early research identified a simple linear promoting effect, whereas current studies reveal distinctly nonlinear and multidimensional effects, dynamically driven by three fundamental factors: economy, technology, and resources. Emerging finance has become a crucial driver of transition through technological innovation, risk diversification, and improved capital allocation efficiency. Notable disagreements persist in the existing literature on conceptual frameworks, measurement approaches, and empirical findings. By synthesizing cutting-edge empirical evidence, we identify three critical future research directions: (1) dynamic coupling mechanisms, (2) heterogeneity of financial instruments, and (3) stage-dependent evolutionary pathways. Our study provides a theoretical foundation for understanding the complex finance-energy transition relationship and informs policy-making and interdisciplinary research. Full article

Nowadays, the circular economy is driving the construction industry towards greater sustainability for both environmental and financial purposes. One prominent area of research with significant contributions to circular economy is the reuse of steel from decommissioned structures in new construction projects. This approach is deemed far more efficient than ordinary steel recycling, due to the fact that it contributes towards reducing both the cost of the new project and the associated carbon emissions. Along these lines, the feasibility of utilizing steel wind turbine towers (WTTs) as part of a new structure is investigated herein, considering that wind turbines are decommissioned after a nominal life of approximately 25 years due to fatigue limitations. General principles of structural steel reuse are first presented in a systematic manner, followed by two case studies. Realistic data about the geometry and cross-sections of previous generation models of WTTs were obtained from the Greek Center for Renewable Energy Sources and Savings (CRES), including drawings and photographic material from their demonstrative wind farm in the area of Keratea. A specific wind turbine was selected that is about to exceed its life expectancy and will soon be decommissioned. Two alternative applications for the reuse of the tower were proposed and analyzed, with emphasis on the structural aspects. One deals with the use of parts of the tower as a small-span pedestrian bridge, while the second addresses the transformation of a tower section into a water storage tank. Several decision factors have contributed to the selection of these two reuse scenarios, including, amongst others, the geometric compatibility of the decommissioned wind turbine tower with the proposed applications, engineering intuition about the tower having adequate strength for its new role, the potential to minimize fatigue loads in the reused state, the minimization of cutting and joining processes as much as possible to restrain further CO₂ emissions, reduction in waste material, the societal contribution of the potential reuse applications, etc. The two examples are briefly presented, aiming to demonstrate the concept and feasibility at the preliminary design level, highlighting the potential of decommissioned WTTs to find proper use for their future life. Full article

During the process of oil and gas drilling, due to the existence of pores or micro-cracks, drilling fluid is prone to invade the formation. Under the action of hydration expansion of clay in the formation and liquid pressure, wellbore instability occurs. In order to reduce the wellbore instability caused by drilling fluid intrusion into the formation, this study proposed a method of forming a dynamic hydrogen bond cross-linked network weak gel structure with modified nano-silica and P(AM-AAC). The plugging performance of the drilling fluid and the performance of inhibiting the hydration of shale were evaluated through various experimental methods. The results show that the gel composite system (GCS) effectively optimizes the plugging performance of drilling fluid. The 1% GCS can reduce the linear expansion rate of cuttings to 14.8% and increase the recovery rate of cuttings to 96.7%, and its hydration inhibition effect is better than that of KCl and polyamines. The dynamic cross-linked network structure can significantly increase the viscosity of drilling fluid. Meanwhile, by taking advantage of the liquid-phase viscosity effect and the physical blocking effect, the loss of drilling fluid can be significantly reduced. Mechanism studies conducted using zeta potential measurement, SEM analysis, contact angle measurement and capillary force assessment have shown that modified nano-silica stabilizes the wellbore by physically blocking the nano-pores of shale and changing the wettability of the shale surface from hydrophilic to hydrophobic when the contact angle exceeds 60°, thereby reducing capillary force and surface free energy. Meanwhile, the dynamic cross-linked network can reduce the seepage of free water into the formation, thereby significantly lowering the fluid loss of the drilling fluid. This research provides new insights into improving the stability of the wellbore in drilling fluids. Full article

Carbon fiber-reinforced plastics (CFRPs) offer excellent in-plane mechanical performance, but their relatively low interlaminar fracture toughness makes them vulnerable to delamination, particularly around intralaminar discontinuities such as resin-rich regions or fiber gaps. This study investigates the effectiveness of polyamide (PA) mesh inserts in improving interlaminar toughness and suppressing delamination in CFRP laminates with such features. Two PA mesh configurations were evaluated: a fully embedded continuous layer and a 20 mm cut mesh strip placed between continuous and discontinuous plies near critical regions. Fracture toughness tests showed that PA mesh insertion improved interlaminar toughness approximately 2.4-fold compared to neat CFRP, primarily due to a mechanical interlocking mechanism that disrupts crack propagation and enhances energy dissipation. Uniaxial tensile tests with digital image correlation revealed that while initial matrix cracking occurred at similar stress levels, the stress at which complete delamination occurred was approximately 60% higher in specimens with a 20 mm mesh and up to 92% higher in specimens with fully embedded mesh. The fully embedded mesh provided consistent delamination resistance across the laminate, while the 20 mm insert localized strain redistribution and preserved global mechanical performance. These findings demonstrate that PA mesh is an effective interleaving material for enhancing damage tolerance in CFRP laminates with internal discontinuities. Full article

Complex mechanical systems, such as agricultural combine harvesters, are subjected to dynamic excitations from multiple coupled sources, compromising structural integrity and operational reliability. Disentangling these vibrations to identify dominant sources and quantify their transmission paths remains a significant engineering challenge. This study proposes a robust diagnostic framework to address this issue. We employed a multi-condition vibration test with sequential source activation and an improved Operational Transfer Path Analysis (OTPA) method. Applied to a harvester chassis, the results revealed that vibration energy is predominantly concentrated in the 0–200 Hz frequency band. Path contribution analysis quantified that the “cutting header → conveyor trough → hydraulic cylinder → chassis frame” path is the most critical contributor to vertical vibration, with a vibration acceleration level of 117.6 dB. Further analysis identified the engine (29.3 Hz) as the primary source for vertical vibration, while lateral vibration was mainly attributed to a coupled resonance between the threshing cylinder (58 Hz) and the engine’s second-order harmonic. This study’s theoretical contribution lies in validating a powerful methodology for vibration source apportionment in complex systems. Practically, the findings provide direct, actionable insights for targeted structural optimization and vibration suppression. Full article

Deep drilling and horizontal wells, as important means of unconventional oil and gas development, face problems with the high energy consumption but low removal efficiency of traditional well washing equipment, the uneven cleaning of horizontal well intervals, and an insufficient degree of automation. This paper proposes a novel hydraulic drive well washing device which consists of two main units. The wellbore cleaning unit comprises a hydraulic drive cutting–flushing module, a well cleaning mode-switching module, and a filter storage module. The unit uses hydraulic and mechanical forces to perform combined cleaning to prevent mud and sand from settling. By controlling the flow direction of the well washing fluid, it can directly switch between normal and reverse washing modes in the downhole area, and at the same time, it can control the working state of corresponding modules. The assembly control unit includes the chain lifting module and the arm assembly module, which can lift and move the device through the chain structure, allow for the rapid assembly of equipment through the use of a mechanical arm, and protect the reliability of equipment through the use of a centering structure. The device converts some of the hydraulic power into mechanical force, effectively improving cleaning and plugging removal efficiency, prolonging the downhole continuous working time of equipment, reducing manual operation requirements, and comprehensively improving cleaning efficiency and energy utilization efficiency. Full article

As the range of applications for amphibious robots expands, higher demands are being placed on their working time and working range. This paper proposed a power supply system for an amphibious robot based on wave energy generation, which can convert wave energy into electric energy to enhance endurance. First, the no-load induced electromotive force, magnetic line distribution vector diagrams, and magnetic density cloud diagrams of the cylindrical and flat generators were compared by finite element simulation, which determined that the cylindrical structure has better power generation performance. Then, the electromagnetic parameters of the cylindrical generator were analyzed using Ansys Maxwell, and the final dimensions were determined. Finally, the wave motion was simulated using a swing motor, and the effects of different cutting speeds for the actuator before and after rectification, as well as series-parallel capacitance on the power generation performance of the designed generator, were experimentally analyzed. This provides a potential solution to enhance the working time and working range of amphibious robots. Full article

Background: Rock–blast design is a canonical inverse problem that joins elastodynamic partial differential equations (PDEs), fracture mechanics, and stochastic heterogeneity. Objective: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a systematic review of mathematical methods for geomechanically informed blast modelling and optimisation is provided. Methods: A Scopus–Web of Science search (2000–2025) retrieved 2415 records; semantic filtering and expert screening reduced the corpus to 97 studies. Topic modelling with Bidirectional Encoder Representations from Transformers Topic (BERTOPIC) and bibliometrics organised them into (i) finite-element and finite–discrete element simulations, including arbitrary Lagrangian–Eulerian (ALE) formulations; (ii) geomechanics-enhanced empirical laws; and (iii) machine-learning surrogates and multi-objective optimisers. Results: High-fidelity simulations delimit blast-induced damage with ≤0.2 m mean absolute error; extensions of the Kuznetsov–Ram equation cut median-size mean absolute percentage error (MAPE) from 27% to 15%; Gaussian-process and ensemble learners reach a coefficient of determination ($R^2>0.95$) while providing closed-form uncertainty; Pareto optimisers lower peak particle velocity (PPV) by up to 48% without productivity loss. Synthesis: Four themes emerge—surrogate-assisted PDE-constrained optimisation, probabilistic domain adaptation, Bayesian model fusion for digital-twin updating, and entropy-based energy metrics. Conclusions: Persisting challenges in scalable uncertainty quantification, coupled discrete–continuous fracture solvers, and rigorous fusion of physics-informed and data-driven models position blast design as a fertile test bed for advances in applied mathematics, numerical analysis, and machine-learning theory. Full article

Evaporative cooling (EC) offers an energy-efficient alternative to direct expansion (DX) cooling but suffers from high water consumption. This limitation can be mitigated by pre-cooling incoming fresh air using cooler exhaust air via energy recovery. This study presents and optimizes a solar-driven EC system integrated with underfloor air distribution (UFAD) to enhance thermal comfort and minimize water use in a temporary office in Riyadh’s arid climate. A 3D CFD model was developed and validated against published data to simulate indoor airflow, providing data for thermal comfort evaluation using the predicted mean vote model in cases with and without energy recovery. A year-round hourly energy analysis revealed that the solar-driven EC-UFAD system reduces grid power consumption by 93.5% compared to DX-based UFAD under identical conditions. Energy recovery further cuts annual EC water usage by up to 31.3%. Operational costs decreased by 84% without recovery and 87% with recovery versus DX-UFAD. Full article

This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN’s exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt temperature and holding pressure) and product quality is amplified by PSO’s intelligent search capability, which efficiently navigates the high-dimensional parameter space. Together, this hybrid approach achieves what neither method could accomplish alone: the BPNN accurately models the intricate process-quality relationships, while PSO rapidly converges on optimal parameter sets that simultaneously meet strict quality targets (66–70 g weight, 3–5 mm thickness) and minimize energy consumption. The significance of this integration is demonstrated through three key outcomes: First, the BPNN-PSO combination reduced optimization time by 40% compared to traditional trial-and-error methods. Second, it achieved remarkable prediction accuracy (RMSE 0.8229 for thickness, 1.5123 for weight) that surpassed standalone BPNN implementations. Third, the method’s efficiency enabled SMEs to achieve CAE-level precision without expensive software, reducing setup costs by approximately 25%. Experimental validation confirmed that the optimized parameters decreased energy use by 28% and material waste by 35% while consistently producing parts within specifications. This research provides manufacturers with a practical, scalable solution that transforms injection molding from an experience-dependent craft to a data-driven science. The BPNN-PSO framework not only delivers superior technical results but does so in a way that is accessible to resource-constrained manufacturers, marking a significant step toward sustainable, intelligent production systems. For SMEs, this framework offers a practical pathway to achieve both economic and environmental sustainability, reducing reliance on resource-intensive CAE tools while cutting production costs by an estimated 22% through waste and energy savings. The study provides a replicable blueprint for implementing data-driven sustainability in injection molding operations without compromising product quality or operational efficiency. Full article

In the process of CNC machining, reducing energy consumption, production costs, and improving machining quality are critical strategies for enhancing product competitiveness. Based on an analysis of machine tool processing mechanisms, calculation models for energy consumption, manufacturing cost, and quality (represented by surface roughness) in CNC lathes were established. These models were optimized using the Egret Swarm Optimization Algorithm (ESOA), which integrates three core strategies: waiting, random search, and bounding mechanisms. With the objectives of minimizing energy consumption, manufacturing cost, and maximizing quality, cutting parameters (e.g., cutting speed, feed rate, and depth of cut) were selected as optimization variables. A multi-objective ESOA (MOESOA) framework was applied to resolve trade-offs among conflicting objectives, and the effectiveness of the proposed method was validated through a case study. The simulation results show that the optimization of cutting parameters is beneficial to energy conservation during the machining process, although it may increase costs. Additionally, under the three-objective optimization, the improvement of surface roughness is relatively limited. The further two-objective (energy consumption and cost) optimization model demonstrates better convergence while ensuring that the surface roughness meets the basic requirements. This method provides an effective tool for optimizing cutting parameters. Full article

The current research trends of production engineering are based on optimizing the machining process concerning human and environmental factors. High-performance materials, such as hardened steels, nickel-based alloys, fiber-reinforced polymer (FRP) composites, and titanium alloys, are classified as hard-to-cut due to their ability to maintain strength at high operating temperatures. Due to these characteristics, such materials are employed in applications such as aerospace, marine, energy generation, and structural. The purpose of this article is to investigate the machinability of these alloys under various cutting conditions. The purpose of this article is to compare cryogenic cooling and cryogenic processing from the perspective of machinability and sustainability in the manufacturing process. Compared to conventional machining, hybrid techniques, which mix cryogenic and minimal quantity lubricant, led to significantly reduced cutting forces of 40–50%, cutting temperatures and surface finishes by approximately 20–30% and more than 40%, respectively. A carbon footprint is determined by several factors including power consumption, energy requirements, and carbon dioxide emissions. As a result of the cryogenic technology, the energy consumption, power consumption, and CO₂ emissions were reduced by 40%, 28%, and 35%. Full article

A novel approach to managing domestic heating systems through IoT technologies is introduced in this paper. The system optimizes energy consumption by dynamically adapting to electricity and fuel price fluctuations while maintaining user comfort. Integrating smart devices significantly reduce energy costs and offer a favorable payback period, positioning the solution as both sustainable and economically viable. Efficient heating management is increasingly critical amid growing energy and environmental concerns. This strategy uses IoT devices to collect real-time data on prices, consumption, and user preferences. Based on this data, the system adjusts heating settings intelligently to balance comfort and cost savings. IoT connectivity manages continuous monitoring and dynamic optimization in response to changing conditions. This study includes a real-case comparison between a conventional central heating system and an IoT-managed electric radiator setup. By applying automation rules linked to energy pricing and user habits, the system enhances energy efficiency, especially in cold climates. The economic evaluation shows that using low-cost IoT devices yields meaningful savings and achieves equipment payback within approximately three years. The results demonstrate the system’s effectiveness, demonstrating that smart, adaptive heating solutions can cut energy expenses without sacrificing comfort, while offering environmental and financial benefits. Full article