Search Results (73)

The growing global demand for energy and rising greenhouse gas emissions require effective mitigation strategies, including carbon capture and storage (CCS) technologies. This study reviews 16 widely used simulation tools, including Aspen Plus, MATLAB, Fluent, and gPROMS, for steady-state and dynamic modeling of post-combustion, pre-combustion, and oxy-fuel combustion carbon capture processes. The tools are evaluated using five criteria: chemical process simulation capability, dynamic modeling functionality, thermodynamic property management, heat transfer accuracy, and tool integration features. The results reveal distinct strengths across platforms. Aspen Plus and Aspen Plus Dynamics perform strongly in chemical process simulation and thermodynamic property modeling, reflecting their robustness in reaction modeling and property estimation. gPROMS excels in dynamic modeling, demonstrating strong capability for time-dependent and transient process analysis. MATLAB achieves the highest score in tool integration, highlighting its flexibility in coupling with optimization solvers, control systems, and external programming environments. Fluent shows strong performance in heat transfer modeling, particularly for detailed thermal analysis in oxy-fuel combustion systems. Most existing studies focus on individual carbon capture technologies rather than simulation tool capabilities. Following the PRISMA 2020 guidelines, a systematic search of Scopus yielded 53 peer-reviewed papers on CCS simulation, which were analyzed to identify dominant tools and inform the AHP-based evaluation. This work addresses that gap by clarifying tool-specific advantages, supporting informed model selection to improve the efficiency and sustainability of CCS process design. Full article

This study proposes an integrated optimization and control-based approach for reservoir development analysis in the Kurovdagh oil field of the Lower Kura Depression. The methodology combines reservoir parameter evaluation with Shewhart statistical control charts to identify deviations in production performance, analyze water breakthrough processes, and support production optimization in mature reservoirs. Based on geological and production data, control charts were constructed to analyze oil production, water cut, injected water volumes, and well performance across multiple reservoir horizons, including Aghjagil, PS03, and PS06. This study further integrates production analysis with horizon-specific enhanced oil recovery (EOR) recommendations. Polymer flooding is proposed for horizon III to improve sweep efficiency, micellar waterflooding for the Aghjagil horizon, with an estimated recovery increase of 10–20%, and in situ combustion for horizon VI, with potential recovery improvements of up to 20%. Additional analysis of production fluctuations, water breakthrough processes, and reservoir heterogeneity was incorporated to improve the interpretation of abnormal production behavior. The results demonstrate that the proposed approach enhances hydrocarbon recovery efficiency, improves understanding of mature reservoir behavior, and supports data-driven optimization of production systems. The developed framework provides practical implications for long-term field management, reservoir monitoring, and production forecasting in mature oil fields. Full article

Grassland fire is one of the major disasters threatening regional ecological security. Its occurrence, development, and spread are closely related to the spatial distribution and coverage of surface vegetation and snow cover across grassland areas. As the primary combustible fuel source, higher vegetation coverage increases fuel load and continuity, thereby directly determining grassland fire danger levels and accelerating fire spread velocity. In contrast, snow cover imposes an indirect regulatory effect on the spatiotemporal pattern of fire danger factors: it lowers surface temperature, raises near-surface humidity, and restricts the germination and growth of herbaceous vegetation in cold seasons, which effectively reduces available combustible materials and weakens regional fire hazard conditions. Therefore, accurately obtaining the coverage status of vegetation (direct combustible fuel factor) and snow cover (indirect fire-regulating factor) in complex grassland scenarios is the essential premise for reliable grassland fire danger monitoring, early warning, disaster prevention and control, and regional ecological management. Aiming at the practical problems in complex grassland scenarios (such as undulating terrain, uneven vegetation growth, large differences in snow depth, and complex lighting conditions), including difficulty in extracting vegetation and snow-covered areas, blurred and confusing boundaries, and low accuracy in coverage calculation, which seriously restrict the technical bottleneck of precise monitoring of grassland fire danger factors, this study takes near-ground images collected by grassland fire danger factor monitoring stations as the core data source, and proposes an improved UNet image segmentation model combined with image segmentation technology and deep learning methods to realize precise extraction of vegetation and snow-covered areas and efficient calculation of coverage in complex scenarios. To improve the model’s feature extraction ability, boundary localization accuracy, and reduce model parameters and computational overhead, the CBAM-ASPP (Convolutional Block Attention Module—Atrous Spatial Pyramid Pooling) module is integrated at the end of the encoding path. The attention mechanism is used to enhance the weight of key features, and the multi-scale receptive field of atrous spatial pyramid pooling is utilized to strengthen the model’s ability to fuse features of vegetation and snow areas of different scales. The residual attention mechanism is introduced in the upsampling stage to effectively alleviate the gradient disappearance problem, improve the model’s ability to accurately locate the boundaries of vegetation and snow areas, and reduce segmentation errors. In the training process, a dynamically weighted hybrid loss function is adopted to dynamically adjust the weights according to the segmentation difficulty of different types of samples during training, optimize the model training effect, and improve the segmentation accuracy and generalization ability. Experiments were conducted using near-ground images of typical complex grassland scenarios as the dataset, and the performance of the proposed model was verified through comparative experiments. The results show that in the vegetation segmentation task, the mean Intersection over Union (mIoU) of the model reaches 84.70%, and the accuracy rate is 91.28%, which are 1.48 and 1.58 percentage points higher than those of the standard UNet model, respectively. In the snow segmentation task, the mIoU of the model reaches 92.74%, and the accuracy rate is 94.19%, which are 2.39 and 2.36 percentage points higher than those of the standard UNet model, respectively. At the same time, the number of parameters of the model is reduced by 12.85% compared with the standard UNet. Also, its comprehensive performance is significantly better than that of mainstream image segmentation models such as FCN, SegNet, and DeepLabv3+. Based on the standardized time-series data retrieved by the optimized segmentation model, this study further constructs a Grassland Fire Risk Index (GFRI) using the Analytic Hierarchy Process (AHP). Pearson correlation verification confirms that the GFRI has an extremely significant positive correlation with historical fire frequency, accurately capturing the seasonal dynamic rhythm of regional grassland fire occurrence. This integrated framework of intelligent segmentation and fire risk quantification provides a reliable technical solution for grassland fire factor monitoring, dynamic fire risk assessment, early warning systems, and refined regional ecological management. Full article

Tunnel kilns are widely used in ceramic manufacturing due to their continuous operation, stable performance, and relatively high thermal efficiency. However, the firing stage remains highly energy-intensive and is a major source of environmental impact, necessitating advanced strategies for performance optimization and sustainability. This study presents a comprehensive and critical review of recent developments in tunnel kiln technology, focusing on heat transfer mechanisms, thermal modeling, process optimization, airflow management, energy recovery, computational fluid dynamics (CFD), and environmental sustainability. The literature shows that kiln performance is governed by strongly coupled interactions among fluid flow, heat transfer, combustion, and material transformations. Although significant progress has been achieved through analytical modeling, experimental studies, and numerical simulations, many approaches rely on simplified assumptions or isolated subsystem analyses, limiting their applicability to real industrial conditions. Key findings emphasize the importance of optimizing airflow distribution, kiln geometry, and product arrangement to enhance convective heat transfer and temperature uniformity. Energy optimization strategies—including waste heat recovery, combustion control, and reduction in kiln car thermal mass—demonstrate considerable potential, but their effectiveness depends on integrated, system-level implementation. Environmental analyses identify the firing stage as the primary source of greenhouse gas emissions, highlighting the need for coordinated energy and emission reduction strategies. In this context, Digital Twin and Industry 4.0 technologies offer promising capabilities for real-time monitoring, predictive control, and data-driven optimization. Generally, this review underscores the need to transition from isolated optimization approaches to integrated, multi-scale frameworks that combine advanced modeling, experimental validation, and intelligent digital systems to achieve sustainable and energy-efficient ceramic manufacturing. Full article

This study focuses on enhancing wind speed prediction for wildfire spread simulation by proposing an integrated forecasting approach. The original wind speed series is first processed via variational mode decomposition (VMD), with its parameters [K, $\alpha$] optimized via particle swarm optimization (PSO). Every intrinsic mode function (IMF) resulting from this decomposition is predicted using a bidirectional long short-term memory model incorporating an attention mechanism (AM-BiLSTM), and the final wind series is reconstructed from these predictions. Model training and validation were conducted using data from controlled burning experiments in the Mao’er Mountain area of Heilongjiang Province, China. Predictive performance is evaluated through multiple statistical metrics, error distribution analysis, and Taylor diagrams. To assess practical utility, the predicted wind field is further applied in FARSITE to drive wildfire spread simulations. Results demonstrate that the PSO-VMD-AM-BiLSTM model provides reliable wind forecasts and contributes to improved fire spread prediction accuracy, indicating its potential for decision support in wildfire management. To achieve accurate forest fire spread prediction, we construct the MCNN model, which is based on early perception of understory wind fields using predicted wind speed data and adopts a multi-branch convolutional neural network architecture to extract fire spread features. FARSITE is employed to simulate forest fire spread in the Mao’er Mountain region, generating a dataset for model training and testing. After 50 training epochs, the loss value of the MCNN model converges, achieving optimal prediction performance when the combustion threshold is set to 0.7. Compared to models such as CNN, DCIGN, and DNN, MCNN shows improvements in evaluation metrics including precision, recall, Sørensen coefficient, and Kappa coefficient. To validate the model’s predictive performance in real fire scenarios, four field ignition experiments were conducted at the Liutiao Village test site: homogeneous fuel combustion, long fire line combustion, alternating fuel combustion, and multiple ignition source merging combustion. Comprehensive evaluation across the four experiments indicates that the model achieves precision, recall, Sørensen coefficient, and Kappa coefficient values of 0.940, 0.965, 0.953, and 0.940, respectively, with stable prediction errors below 6%. These results represent improvements over the comparative models DCIGN and DNN. The proposed MCNN model can adapt to forest fire spread prediction under different scenarios, offering a novel approach for accurate forest fire prediction and prevention. Full article

Municipal solid waste incineration (MSWI) is a typical complex industrial process for achieving sustainable development of the global environment. It implements the “perception-prediction–control” mode based on domain experts by using multimodal information. To harness the complementary value of different modal data, prevent information conflicts or fusion failures caused by misalignment, and ensure the availability of multimodal datasets and the reliability of analytical conclusions, constructing a benchmark operational condition multimodal dataset is essential. The objective of this work was to create a multimodal reference database for the operational status of IMSW processes. Based on the description of the MSWI process and the analysis of the characteristics of the multimodal data, the process data is first preprocessed under different missing scenarios, missing value processing and outlier processing. Then, single-frame images of the flame video are captured on a minute scale, and the missing combustion lines are quantized by using machine vision technology. Finally, the alignment of combustion line quantization (CLQ) values with the minute time scale of process data is achieved through the multimodal time synchronization module. Taking an MSWI power plant in Beijing as the research object, the combustion flame video and process data under the benchmark operating conditions were collected. The hybrid missing value management strategy combining linear interpolation with the LRDT model improved data integrity, and a spatiotemporal aligned multimodal dataset was constructed. The standardized benchmark operating condition multimodal data was obtained to support combustion state analysis during the incineration process, pollutant generation prediction, and process optimization. Therefore, the objectives of ‘reduction, harmlessness, and resource utilization’ of municipal solid waste, addressing land resource shortages, protecting the ecological environment, and promoting the dual carbon goal can be supported. Additionally, data and technical support for environmental and urban sustainable development are provided. Full article

This study addresses the management and optimization of decentralized bioresource energy generation under conditions of political instability, using Ukraine as a representative case. The research aims to enhance energy security and operational resilience where centralized energy infrastructure is vulnerable to disruption. A high-efficiency technology for decentralized heat generation is proposed, based on the direct combustion of non-standard agricultural biomass with a one-year renewal cycle. The methodology combines experimental and statistical analysis of biomass feeding processes with advanced three-dimensional modeling of mixture formation and combustion, as well as the development of an artificial intelligence-driven automated control system. The system enables the use of sunflower, rapeseed, wheat, corn, and other agricultural residues with variable particle size and moisture content of up to 40%, without the need for pre-drying or pelletization. An original jet–vortex bioheat generator and optimized dosing systems were designed to ensure continuous and stable combustion. An operational algorithm allowing stable performance within 25–100% of nominal capacity was formulated based on statistical evaluation of screw feeder behavior and optimization of adjustable electric drive parameters, ensuring thermal carrier temperature stability within ±1–2 °C. The main novelty lies in the integrated optimization framework combining unconventional biomass utilization, adaptive electric drive control, and AI-based automation to achieve high energy efficiency and environmental performance. The results indicate that such decentralized systems can substantially strengthen national energy security and support sustainable energy supply in unstable political environments. 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 adoption of electric vehicles (EVs) for last-mile delivery requires adapting existing routes designed for internal combustion engine (ICE) vehicles. This study introduces an enhanced Ant Colony System (ACS) that optimizes EV routing by dynamically incorporating state of charge (SOC), charging station proximity, and time constraints. Unlike traditional methods, our approach adjusts pheromone deposition in real time, prioritizing charging stops only when necessary, significantly improving adherence to delivery times. Using real-world delivery data from Paris, our results show that routes under 90 km tend to remain energetically feasible, although intermediate time-window violations may occur due to cumulative charging delays. For longer routes, the need for additional charging stops introduces a risk of delays, but the system effectively manages these constraints to minimize disruption. These results provide fleet operators with a practical decision-support tool to identify which pre-optimized routes can be efficiently adapted to EVs, thus offering a pathway for the integration of electric vehicles into existing logistics without significant operational disruption. Future work will focus on enhancing the system by incorporating real-time traffic updates and charging station availability to further optimize the routing process. Full article

The selective catalytic reduction (SCR) system is a key component for addressing NOx emissions from internal combustion engines. To resolve the issues of modeling distortion in SCR systems and the difficulty in characterizing the local reaction mechanism, a multi-dimensional SCR reaction model based on the coupling of Eley-Rideal (E-R) and Langmuir-Hinshelwood (L-H) dual mechanisms was established and conducted by experiment. The SCR catalytic characteristics and the dual-mechanism reaction process were systematically investigated. Additionally, based on the combined analysis of species concentration distribution coupled with temperature characteristics, a calculation method for the synergy of concentration-temperature fields was developed, and the synergistic characteristics of the concentration-temperature fields were explored. The results showed that high load accelerated the light-off speed, but this effect was counteracted by the negative impact of high flow rate. A strong negative correlation was maintained between temperature and NOx concentration across the full load range, and the axial consistency increased with load increasing. The results provide important theoretical support for the mechanism analysis of diesel engine SCR reactions and the optimization of thermal management. Full article

The accumulation of residual hydrolyzed polyacrylamide (HPAM) gel or molecular-based solutions in reservoirs after polymer flooding poses dual challenges: irreversible formation damage and long-term environmental risk issues. However, existing research mainly focuses on treating polymers in surface-produced water, neglecting both in situ decomposition of residual polymer gel or molecular-based solutions in reservoirs and the degradation of HPAM gels under high temperatures from in situ combustion (ISC). This work investigates the thermochemical behavior of HPAM gel during ISC and its dual-function role in enhanced oil recovery (EOR) and reservoir remediation. It was demonstrated that the residual gel and/or molecular-based solutions undergo efficient degradation, serving as an in situ fuel that significantly reduces the activation energy for crude oil oxidation by up to 58.4% in the low-temperature stage and 75.2% in the high-temperature stage. Factors influencing the gel’s degradation and the combustion process, including its molecular weight, ionic type, and crude oil viscosity, were systematically evaluated. Optimal conditions achieved over 90% gel degradation. Combustion tube experiments validated the dual benefits of this approach: an incremental oil recovery of 68.6% and an average HPAM gel removal efficiency of 64.8%. This work presents a novel strategy for utilizing retained gels in situ to simultaneously enhance oil recovery and mitigate gel-induced formation damage, offering significant insights for the management of mature gel-treated reservoirs. Full article

New energy vehicles face a critical challenge in balancing the thermal safety management of high-specific-energy battery systems with the simultaneous improvement of energy density. With the large-scale application of high-energy-density systems such as silicon-based anodes and solid-state batteries, their inherent thermal runaway risks pose severe challenges to battery thermal management systems (BTMS). Currently, the thermal insulation performance, temperature resistance, and fire protection capabilities of flame-retardant materials (e.g., foam cotton, fiber felts) used in automotive batteries are inadequate to meet the demands of intense combustion and high temperatures generated during thermal failure in high-energy-density batteries. Against this backdrop, thermal insulation and fireproof aerogel materials are emerging as a revolutionary solution for the next generation of power battery thermal protection systems. Leveraging their nanoporous structure’s exceptional thermal insulation properties (thermal conductivity of 0.013–0.018 W/(m·K) at room temperature) and extreme fire resistance (temperature resistance > 1100 °C/UL94 V-0 flame retardancy), aerogels are gaining prominence. This article provides a systematic review of thermal runaway phenomena in automotive batteries and corresponding protective measures. It highlights recent breakthroughs in the selection of material systems, optimization of preparation processes, and fiber–matrix composite technologies for automotive fireproof aerogel composites. The core engineering values of these materials, such as blocking thermal runaway propagation, reducing system weight, and improving volumetric efficiency, are quantitatively validated. Furthermore, the paper explores future research directions, including the development of low-cost aerogel composites and the design of organic–inorganic hybrid composite structures, aiming to provide a foundation and industrial pathway for the research and development of next-generation high-performance battery thermal management systems. Full article

With increasing urbanization and population growth, the volume of municipal solid waste (MSW) continues to rise. Efficient and environmentally responsible waste processing has become a core issue in sustainable development. Incineration plays a key role in reducing landfill usage and recovering energy from waste, contributing to circular economy initiatives. However, fluctuations in furnace temperature significantly affect combustion efficiency and emissions, undermining the environmental benefits of incineration. To address these challenges under dynamic operational conditions, this paper proposes a hybrid model combining an Improved Snow Ablation Optimizer (ISAO), Dual-Branch Convolutional Neural Network (DBCNN), and Bidirectional Long Short-Term Memory (BiLSTM). The model extracts dynamic features from control and condition variables and incorporates time series characteristics for accurate temperature prediction, thereby enhancing the overall efficiency of the incineration process. ISAO integrates Lévy flight, differential mutation, and elitism strategies to optimize parameters, contributing to better energy recovery and reduced emissions. Experimental results on real MSWI data demonstrate that the proposed method achieves high prediction accuracy and adaptability under varying operating conditions, showcasing its robustness and application potential in promoting sustainable waste management practices. By improving combustion efficiency and minimizing environmental impact, this model aligns with global sustainability goals, supporting a more efficient, eco-friendly waste-to-energy process. Full article

Coal plays a significant role in the economies of many countries and serves as an energy source for numerous societies. However, its combustion causes various environmental problems and contributes to climate change. This article examines the efficiency of coal consumption in 26 European Union countries and its changes from 2014 to 2022. Data Envelopment Analysis (DEA) methodology was applied to measure the extent of overall technical, pure technical, and scale technical efficiency, based on data concerning three production factors (labour, fixed assets, and energy), with GDP as a desirable output and CO₂ emissions as an undesirable output. The empirical findings revealed that Cyprus, Denmark, Luxembourg, and Poland were efficiency leaders throughout the entire study period. France, Germany, Italy, and the Netherlands managed energy and non-energy resources efficiently but were found inefficient in terms of operational scale. Countries that do not use their resources at optimal levels in the production of goods and services should provide greater technical and financial support to their production processes and improve the organisation and structure of labour. Full article

This study presents a comprehensive analysis of the chemical composition of bottom ash samples generated during municipal waste incineration. A total of 52 samples were collected and subjected to statistical analysis for 17 elements and 2 element sums using techniques such as correlation analysis and one-way ANOVA. The results confirm a high degree of heterogeneity in the elemental content, reflecting the variability of waste streams and combustion processes. Strong correlations were identified between certain elements, including Cu-Zn, Co-Ni, and HREE-LREE, indicating common sources and similar geochemical properties. The analysis also revealed significant seasonal variability in the content of Ba and Sr, with lower average values observed during the spring season and greater variability noted during summer and winter. Although Al and HREE did not reach classical significance levels, their distributions suggest possible seasonal differentiation. These findings underscore the need for long-term monitoring and seasonal analysis of incineration bottom ash composition to optimize resource recovery processes and assess environmental risk. The integration of chemical data with operational data on waste composition and combustion parameters may contribute to a better understanding of the variability of individual elements, ultimately supporting the development of effective strategies for ash management and element recovery. Full article