Search Results (453)

The Kuramoto–Sivashinsky (KS) equation and its fractional generalizations (FKSs) arise as canonical models for a wide class of nonlinear dissipative–dispersive systems, including thin-film flows, combustion fronts, drift–wave turbulence in plasmas, and chemically reacting media, where shock-like and strongly localized structures play a central role in the dynamics. Despite their apparent simplicity, KS-type models become analytically intractable once higher-order dissipation, geometric effects, and memory (fractional) operators are incorporated, and standard perturbative or transform-based schemes often lead to cumbersome recursive structures, slow convergence, or severe restrictions on the initial data. In this work, a novel direct approximation procedure, referred to as the Tantawy Technique (TT), is developed and implemented to solve and analyze planar fractional KS-type equations and their Burgers-type reductions in a systematic manner. The central difficulty is to construct, for a given physically motivated initial profile, a rapidly convergent series in fractional time that remains stable for a broad range of the fractional order and transport coefficients, while still retaining a clear link to the underlying shock-wave physics. To overcome this, the TT combines (i) a Tanh-based exact shock solution of the planar integer-order KS equation, obtained first as a reference via the standard Tanh method, with (ii) a carefully designed fractional-time ansatz in powers of $t^{\rho }$, where the spatial coefficients are determined recursively from the governing equation in the Caputo sense. This construction yields closed-form expressions for the first few terms in the approximation hierarchy and allows one to monitor convergence through residual and absolute error measures. Full article

Refuse-derived fuel (RDF) presents a viable strategy to concurrently address the challenges of municipal solid waste management and the need for alternative energy. In this context, the present review systematically synthesizes recent advances in RDF preparation, combustion behavior, and efficient utilization technologies. The study examines the full chain of RDF production—including waste selection, mechanical/optical/magnetic sorting, granulation, briquetting, and chemical modification—highlighting how pretreatment technologies influence fuel homogeneity, calorific value, and emissions. The thermochemical conversion characteristics of RDF are systematically analyzed, covering the mechanism differences among slow pyrolysis, fast pyrolysis, flash pyrolysis, pyrolysis mechanisms, catalytic pyrolysis, fragmentation behavior, volatile release patterns, and kinetic modeling using Arrhenius and model-free isoconversional methods (e.g., FWO). Special attention is given to co-firing and high-efficiency combustion technologies, including ultra-supercritical boilers, circulating fluidized beds, and rotary kilns, where fuel quality, ash fusion behavior, slagging, bed agglomeration, and particulate emissions determine operational compatibility. Integrating recent findings, this review identifies the key technical bottlenecks—feedstock variability, chlorine/sulfur release, heavy-metal contaminants, ash-related issues, and the need for standardized RDF quality control. Emerging solutions such as AI-assisted sorting, catalytic upgrading, optimized co-firing strategies, and advanced thermal conversion systems (oxy-fuel, chemical looping, supercritical steam cycles) are discussed within the broader context of carbon reduction and circular economy transitions. Overall, RDF represents a scalable, flexible, and high-value waste-to-energy pathway, and the review provides insights into future research directions, system optimization, and policy frameworks required to support its industrial deployment. Full article

The article presents an extensive review of modern technological solutions for pulverized coal combustion, with emphasis on combustion modifiers and biomass co-firing. It highlights the role of coal in the national energy system and the need for its sustainable use in the context of energy transition. The pulverized coal combustion process is described, along with factors influencing its efficiency, and a classification of modifiers that improve combustion parameters. Both natural and synthetic modifiers are analyzed, including their mechanisms of action, application examples, and catalytic effects. Special attention is given to the synergy between transition metal compounds (Fe, Cu, Mn, Ce) and alkaline earth oxides (Ca, Mg), which enhances energy efficiency, flame stability, and reduces emissions of CO, SO₂, and NO_x. The article also examines biomass-coal co-firing as a technology supporting energy sector decarbonization. Co-firing reduces greenhouse gas emissions and increases the reactivity of fuel blends. The influence of biomass type, its share in the mixture, and processing methods on combustion parameters is discussed. Finally, the paper identifies directions for further technological development, including nanocomposite combustion modifiers and intelligent catalysts integrating sorption and redox functions. These innovations offer promising potential for improving energy efficiency and reducing the environmental impact of coal-fired power generation. Full article

The growth of the Internet of Things (IoT) has increased the demand for low-cost nanostructured materials. Zinc tin oxide (ZTO) has been widely used as an alternative to current semiconductor technologies, but its production methods remain expensive. Combustion synthesis is a green, low-cost alternative that may allow us to reduce the complexity of ZTO production. In this work, zinc and tin-based nanostructures were produced through combustion synthesis using water and ethanol as solvents and different precursor solutions ratios (1:2, 1:1, and 2:1). The influence of ethylenediamine (EDA) on the crystallographic phase of 2:1 samples of both solvents and Ag doping on 2:1 ethanol samples was also studied. Samples produced with a 2:1 ratio presented a predominance of ZnO, while the 1:1 and 2:1 samples presented a mixture of ZnO, SnO₂, and ZnSnO₃. The use of EDA in the 2:1 ethanol and water samples led to the growth of ZnO after annealing at 600 °C. For the ZTO-Ag samples, X-ray diffraction (XRD) and Raman analysis also revealed the presence of ZnO after annealing at 600 °C. This work showed it is possible to produce ZTO nanostructures through solution combustion synthesis. Full article

This study produces high-purity nano-silica from corn straw ash (biomass power plants) using an alkaline fusion-derived sodium silicate solution. CO₂ replaces traditional acids in the carbonation reaction, enabling high extraction yield (93.11%). The process addresses the gap in directly utilizing combustion ash for such high-purity silica. Key optimal conditions identified were 5 M aq. HCl concentration, NaOH fusion reagent, 1:1.2 mixing ratio, 3 M aq. NaOH solvent, and 12 h ripening. The resulting nano-silica achieved 92.73% purity, 10–50 nm particle size, 270 × 10⁻⁵ m³/kg dibutyl phthalate (DBP) absorption, 55.9916 m²/g specific surface area, 6.38% loss on drying (LOD), and 6.69% loss on ignition (LOI). These properties meet national standards for premium, loosely structured nano-silica. This method provides an economical and effective silicon source, reducing costs and offering economic-environmental benefits. Full article

Accurate identification of the combustion state in municipal solid waste incineration (MSWI) processes is crucial for achieving efficient, low-emission, and safe operation. However, existing methods often struggle with stable and reliable recognition due to insufficient feature extraction capabilities when confronted with challenges such as complex flame morphology, blurred boundaries, and significant noise in flame images. To address this, this paper proposes a novel hybrid architecture model named PRTNet, which aims to enhance the accuracy and robustness of combustion state recognition through multi-scale feature enhancement and adaptive fusion mechanisms. First, a local-semantic enhanced residual network is constructed to establish spatial correlations between fine-grained textures and macroscopic combustion patterns. Subsequently, a feature-adaptive fusion Transformer is designed, which models long-range dependencies and high-frequency details in parallel via deformable attention and local convolutions, and achieves adaptive fusion of global and local features through a gating mechanism. Finally, a cross-scale feature guided aggregation module is proposed to fuse shallow detailed information with deep semantic features under dual-attention guidance. Experiments conducted on a flame image dataset from an MSWI plant in Beijing show that PRTNet achieves an accuracy of 96.29% in the combustion state classification task, with precision, recall, and F1-score all exceeding 96%, significantly outperforming numerous mainstream baseline models. Ablation studies further validate the effectiveness and synergistic effects of each module. The proposed method provides a reliable solution for intelligent flame state recognition in complex industrial scenarios, contributing to the advancement of intelligent and sustainable development in municipal solid waste incineration processes. Full article

Currently, the general focus of engine-produced pollution reduction lies in exhaust gas aftertreatment methods. This paper attempts a paradigm shift in the field by applying the pre-combustion treatment technologies by fumigation method, which consists of introducing an aqueous solution into the engine intake, which could lead to a significant reduction in polluting emissions. Common and inexpensive substances used (sodium borate, citric acid, podium carbonate, hydrogen peroxide, potassium permanganate, and ammonium nitrate) in tests are not ordinarily known to be combustible. The key to the research is understanding the thermochemical phenomena during combustion. The method used was to formulate hypotheses regarding thermochemical reactions and validate them by measuring parameters and pollutant emissions (CO, CO₂, NO, NO₂, NO_x, and smoke) of a single-cylinder engine mounted on the test stand. The results indicate that chemical fumigation leads to a significant reduction, specifically a decrease in CO by 145 ppm and NO_x (NO₂ and NO) by 55 ppm at an engine speed of 1500 rpm. All substances fumigated into the engine intake increased the exhaust gas temperature. The highest increase is nearly 150 °C at 1500 rpm, while the least pronounced rise is 50 °C at 3500 rpm. Additionally, a decarbonization process of a passenger car engine is presented, carried out by applying the fumigation method simultaneously with potassium permanganate and ammonium nitrate. In this case, the results showed that the opacity index decreased to 0.01 m⁻¹. Full article

In recent years, long-term vehicle rental has gained importance as a flexible and cost-effective mobility solution. This model reduces the high initial costs associated with vehicle purchases, ensures predictable expenses through fixed monthly payments, reduces the risk of depreciation, and enables systematic fleet renewal, supporting its adaptation to changing environmental regulations and technological advancements. This paper proposes a tool to support the process of selecting propulsion technologies in long-term rental fleets, taking into account their economic, technical, environmental, and social implications for sustainable fleet management. The developed procedure combines secondary fleet data analysis, expert research conducted among service providers, and multi-criteria analysis conducted using the Analytic Hierarchy Process method. The results indicate that under current conditions in Poland, combustion vehicles remain the optimal solution for fleet operators, while electric vehicles—despite their environmental benefits and additional benefits—remain the least competitive. The proposed approach is comprehensive, adaptable, and easy to implement, providing a practical tool for fleet operators and end users. The results also provide guidance for public decision-makers on strengthening the market position of low- and zero-emission vehicles. Full article

The increasing popularity of electric vehicles (EVs) as green alternatives to traditional internal combustion engine cars has highlighted the need for sustainable and environmentally friendly supply chain models. In particular, the handling of EV batteries, which are environmentally unfriendly and logistically critical due to their hazardous nature and short life cycle, requires a well-designed closed-loop supply chain (CLSC). This study proposes a new multi-objective optimization model of the CLSC, explicitly tailored to EV batteries under demand and return rate uncertainty. The proposed model incorporates three primary objectives that are typically in conflict with one another: minimizing the total cost, reducing carbon emissions throughout the entire supply chain network, and maximizing the recycling and reuse of batteries. The model employs a neutrosophic goal programming (NGP) methodology to address the uncertainties associated with demand and battery return quantities. The NGP model translates multiple objectives into non-monolithic goals with crisp aspiration levels (i.e., prescribed ideal levels for achieving the best of each goal) and thresholds that capture tolerances, thereby accounting for uncertainty. The efficiency of the proposed method is illustrated by a numerical example, solved using a IBM ILOG CPLEX Optimization Studio 22.1.2 solver. The findings demonstrate that the NGP can offer cost-effective, low-impact, and environmentally friendly solutions, thereby enhancing system robustness and flexibility to adapt to uncertainties. This study contributes to the emerging literature on sustainable operations research by developing a decision-making tool for EV-HV battery supply chain management. It also offers relevant suggestions for policymakers and industrialists who seek to co-optimize economic benefits, ecological sustainability, and logical feasibility in the emerging green society. Full article

Non-linear systems of equations play a fundamental role in various engineering and data science models, where accurate solutions are essential for both theoretical research and practical applications. However, solving such systems is highly challenging due to their inherent non-linearity and computational complexity. This study proposes a novel hybrid iterative method with fourth-order convergence. The foundation of the proposed scheme combines the Walrus Optimization Algorithm and a fourth-order iterative technique. The objective of this hybrid approach is to enhance global search capability, reduce the likelihood of convergence to local optima, accelerate convergence, and improve solution accuracy in solving non-linear problems. The effectiveness of the proposed method is checked on standard benchmark problems and two real-world case studies, hydrocarbon combustion and electronic circuit design, and one non-linear boundary value problem. In addition, a comparative analysis is conducted with several well-established optimization algorithms, based on the optimal solution, average fitness value, and convergence rate. Furthermore, the proposed scheme effectively addresses key limitations of traditional iterative techniques, such as sensitivity to initial point selection, divergence issues, and premature convergence. These findings demonstrate that the proposed hybrid method is a robust and efficient approach for solving non-linear problems. Full article

Driven by the shipping industry’s pressing need to reduce its environmental impact, methanol has emerged as a promising marine fuel. Methanol-diesel dual-fuel (DF) engines present a viable solution, yet their optimization is challenging due to complex, nonlinear interactions among operational parameters. This study develops an integrated simulation and data-driven framework for multi-objective optimization of a large-bore two-stroke marine DF engine. We first establish a high-fidelity 1D model in GT-POWER, rigorously validated against experimental data with prediction errors within 10% for emissions (NOx, CO, CO₂) and 3% for performance indicators. To address computational constraints, we implement a Polynomial Regression (PR) surrogate model that accurately captures engine response characteristics. The innovative Triple-Adaptive Chaotic Sparrow Search Algorithm (TAC-SSA) serves as the core optimization tool, efficiently exploring the parameter space to generate Pareto-optimal solutions that simultaneously minimize fuel consumption and emissions. The Entropy-Weighted TOPSIS (E-TOPSIS) method then identifies the optimal compromise solution from the Pareto set. At 75% load, the framework determines an optimal configuration: methanol substitution ratio (MSR) = 93.4%; crank angle at the beginning of combustion (CAB) = 2.15 °CA; scavenge air pressure = 1.70 bar; scavenge air temperature = 26.9 °C, achieving concurrent reductions of 7.1% in NOx, 13.3% in CO, 6.1% in CO₂, and 4.1% in specific fuel oil consumption (SFOC) relative to baseline operation. Full article

The paper discusses the economic and infrastructural challenges preventing the adoption of Electric Vehicles (EVs) in Pakistan. It focuses on key factors such as affordability, consumer preferences, and the overall readiness of the market. Based on a segment-wise comparison, the analysis reveals that four-wheeler EVs carry an initial price premium of 20 to 64 percent over internal combustion engine (ICE) vehicles, with payback periods ranging from 11 to 25 years, placing them out of reach for most middle-income consumers. In contrast, electric two- and three-wheelers—comprising more than 90 percent of registered vehicles—offer a significantly more practical and affordable pathway for mass adoption. These vehicles exhibit minimal upfront cost differences, annual operational savings exceeding PKR 62,000, and short payback periods of just 4 to 6 months, making them highly feasible in the local context. The study adopts a mixed-methods approach using national price data, vehicle registration records, and international case studies from India, Kenya, and Norway. It evaluates financing innovations such as battery leasing, concessional green loans, and carbon-credit-linked microfinance, and outlines a consumer-focused policy framework that emphasizes financial inclusion, decentralized infrastructure development, and phased implementation strategies. By aligning global lessons with Pakistan’s socioeconomic and infrastructural realities, the paper offers a scalable and inclusive roadmap for accelerating EV adoption through targeted, consumer-driven solutions. Full article

The extensive area of goaf makes high-temperature points highly concealed, and prolonged heating can easily trigger spontaneous coal combustion. Traditional temperature monitoring methods are limited in spatial coverage and thus fail to detect high-temperature points in a timely manner. To address this issue, this study proposes an integrated analytical method combining numerical simulation and intelligent inversion, with Taihe Coal Mine as the research object. First, A coupled flow–temperature–gas field model of the goaf was established in COMSOL Multiphysics 6.3 to simulate working-face CO concentration distributions corresponding to high-temperature points at different locations, thereby constructing a comprehensive dataset. Then, a BP neural network prediction model improved by the dung beetle optimization algorithm (DBO-BP) was trained to infer the spatial location of high-temperature points based on CO concentration distributions. Finally, a geometric prediction method was introduced to guide precise drilling within the predicted high-risk areas for field verification. The results demonstrate that the proposed DBO-BP model can effectively trace the locations of high-temperature points from CO concentration data. When combined with the geometric prediction method, it provides an efficient and reliable technical solution for the early prevention of spontaneous coal combustion in goaf. Full article

Dry reforming of methane (DRM) is a method whereby two greenhouse gases (methane and carbon dioxide) are synthesized into a high-value gas. Suitable catalysts with optimal compositions are still in development, as problems concerning coking and metal sintering remain unresolved. Since the late 20th century, catalysts prepared via solution combustion synthesis (SCS) have been applied for catalytic reactions, as these materials (catalyst or supports) demonstrate high catalytic performance; for example, SCS catalysts have been tested in DRM. This review describes the history of solution combustion synthesis, compares it with traditional methods of preparing catalysts for DRM, and charts recent developments in SCS catalytic systems based on Ni and Co. SCS catalysts are prepared by burning nitrates (oxidizing agents) and fuels (reducing agents) at mild pre-ignition temperatures. In this review, the effects of fuel type and mixed-fuel systems on the catalyst composition, as well as its activity in DRM, are described. These catalysts have shown high metal dispersion, good coke resistance, and stable catalytic performance in long-term tests. This review demonstrates the main reasons for catalyst deactivation, such as coke deposition on the catalyst surface, and suggests ways to reduce them. Full article

Due to the environmental impacts of greenhouse gas emissions from traditional combustion vehicles, governments worldwide are encouraging the transition to electric vehicles (EVs). However, as EV use increases, user-related charging challenges have become evident. To identify possible solutions to improve EV charging management from a user-centered perspective, a state-of-the-art study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Optimization systems and artificial intelligence (AI) methods applied to decision-making were compared and a growing trend towards the implementation of artificial intelligence in current applications was identified. This study investigates in more depth the application of AI in multi-agent systems for energy management in EV charging. It provides a critical review of charging stations, focusing on aggregator-based models that operate within a multi-agent system in smart grids. This analysis adopts the vehicle owner’s perspective and considers the charging duration of the EV as a parameter. This article identifies significant gaps in how existing research addresses individual electric vehicle users, noting a lack of consideration for energy management and system connectivity to support EV recharging locations. This work presents solutions to these gaps by using aggregators and multi-agent systems to represent charging stations, facilitate user access, and improve energy management. Full article