Search Results (125)

To alleviate the impact of economic and environmental detriments caused by the increased demands of electric vehicle battery production and disposal, the use of spent batteries in second-life stationary applications such as energy storage for renewable sources or backup power systems, offers many benefits. This paper focuses on reducing the energy consumption cost and greenhouse gas emissions of Internet-of-Things-enabled campus microgrids by installing solar photovoltaic panels on rooftops alongside energy storage systems that leverage second-life batteries, a gas-fired campus power plant, and a wind turbine while considering the potential loads of a prosumer microgrid. A linear optimization problem is derived from the system by scheduling energy exchanges with the Ontario grid through net metering and solved by using Python 3.11. The aim of this work is to support Sustainable Development Goals, namely 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), and 13 (Climate Action). A comparison between a base case scenario and the results achieved with the proposed scenarios shows a significant reduction in electricity cost and greenhouse gas emissions and an increase in self-consumption rate and renewable fraction. This research work provides valuable insights and guidelines to policymakers. Full article

The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper proposes a methodology for designing a holistic energy management system, based on advanced digital twins and optimization techniques, to minimize the cost of supplying industry loads and electric vehicles using local renewable energy sources, second-life battery energy storage systems, and grid power. The digital twins represent and forecast the principal energy assets, providing variables necessary for optimizers, such as photovoltaic generation, the state of charge and state of health of electric vehicles and stationary batteries, and industry power demand. Furthermore, a two-layer optimization framework based on mixed-integer linear programming is proposed. The optimization aims to minimize the cost of purchased energy from the grid, local second-life battery operation, and electric vehicle fleet charging. The paper details the mathematical fundamentals behind digital twins and optimizers. Finally, a real-world case study is used to demonstrate the operation of the proposed approach within the context of the waste collection and management industry. The study confirms the effectiveness of digital twins for forecasting and performance analysis in complex energy systems. Furthermore, the optimization strategies reduce the operational costs by 1.3%, compared to the actual industry procedure, resulting in daily savings of EUR 24.2 through the efficient scheduling of electric vehicle fleet charging. Full article

This study introduces a digital twin paradigm that uses both stationary and mobile sensors and cutting-edge machine learning for urban air quality monitoring. By boosting $R^2$ values from 0.29 to 0.87–0.95, our two-step calibration method increased the accuracy of low-cost PM sensors, showing the possibility of growing monitoring networks without sacrificing measurement accuracy. Significant temporal and spatial variability in PM concentrations was found by mobile sensor deployments, with variations of up to 300% over short distances, predominantly during heavy traffic. During rush hours, peak concentrations were found on multi-lane boulevards and intersections, indicating important exposure concerns usually overlooked by stationary monitoring networks. According to our Graph Neural Network model, which successfully described pollutant dispersion patterns, road dust resuspension predominates in residential areas, while vehicle emissions account for 65% of PM_2.5 along high-traffic corridors. Urban green areas lower PM levels by 30%, yet when the current low-emission zones were first implemented, they had no discernible effect on air quality. Municipal authorities can use this digital twin strategy to acquire practical insights for focused air quality improvements. The method helps make evidence-based traffic management and urban planning judgments by identifying unidentified pollution hotspots and source contributions. The technique offers a scalable option for establishing healthier urban development and marks a substantial leap in environmental monitoring. Full article

To achieve the precise quantification and real-time monitoring of CO₂ emissions from stationary sources, this study developed a Gaussian plume model-based dispersion framework incorporating emission characteristics. Critical factors affecting CO₂ dispersion were systematically analyzed, with model optimization conducted through plume rise height adjustments and reflection coefficient calibrations. MATLAB-based simulations on an industrial park case study demonstrated that wind speed, atmospheric stability, and effective release height constituted pivotal determinants for enhancing CO₂ dispersion modeling accuracy. Furthermore, the inverse estimation of source strength at emission terminals was implemented via particle swarm optimization, establishing both theoretical foundations and methodological frameworks for the precision monitoring and predictive dispersion analysis of stationary-source CO₂ emissions. Full article

This study aims to develop a process to calculate the carbon footprint of a company in the textile sector for the automotive industry, thus addressing a research gap identified in this sector. Based on a structured calculation model, the project aspires to innovate by quantifying not only the greenhouse gas emissions at different stages of the company’s operations, including those generated by the consumed electricity and gas, but also the emissions related to external and in-house transportation and solid waste management. The approach includes the design of a specific calculator, capable of integrating variables such as energy consumption, transport and types of waste, analysing them in the light of recognised conversion factors. This tool not only allows for a detailed assessment of emissions but also supports strategic decision-making, guiding the implementation of more sustainable business practices. The results indicate that, considering the use of renewable energy sources, the company’s total emissions amount to approximately 18 thousand tonnes of carbon dioxide equivalent. On the other hand, considering non-renewable energy, purchased electricity accounts for 31 thousand megawatt-hours per year, corresponding to 5 thousand tonnes of carbon dioxide equivalent, with the twisting area being the largest consumer at 89% of total usage, followed by the dipping area. In terms of mobile combustion, raw materials contribute 1373 million tonnes of carbon dioxide equivalent, while finished products generate 1869 million tonnes of carbon dioxide equivalent. Among the most impactful variables, solid waste, and stationary combustion stand out as the main contributors. These findings highlight the need for concrete measures to mitigate climate change, such as transitioning from stationary natural gas combustion to green electric power; identifying companies with more suitable waste treatment solutions, process changes that reduce disposable, and easily substitutable materials; making use of green electricity; exploring alternative transport methods or combining different modes, such as using electric vehicles for short distances; and optimizing transport routes. These initiatives reinforce the company’s commitment to sustainable development goals and the promotion of responsible environmental practices. Full article

Hybrid power generation systems utilizing pressurized Solid Oxide Fuel Cells (SOFCs) have gained considerable attention recently as an effective solution to the increasing demand for cleaner electricity sources. Among the various hybridization options, gas turbines (GT) and internal combustion engines (ICE) running on SOFC tail gas have been prominent. Although spark ignition (SI) tail gas engines have received less focus, they show significant potential for stationary power generation, particularly due to their ability to control combustion. This research experimentally characterized an SI engine fueled by simulated SOFC anode gas for five blends, which correspond to overall system power level and loads. The study aimed to optimize the engine operating conditions for each fuel blend and establish operational conditions that would sustain maximum performance. The results showed efficiencies as high as 31.4% at 1600 RPM, with a 17:1 compression ratio, equivalence ratio (φ) of 0.75, and a boost pressure of 165 kPa with low NOx emissions. The study also emphasizes the benefits of optimizing boost supply to minimize parasitic loads and improve brake thermal efficiency. Additionally, installing a catalytic oxidizer would enable the system to comply with new engine emission regulations. A proposed control scheme for automation includes regulating engine power by controlling the boost of the supercharger at a fixed throttle position. The results of this study help to promote the development of this SOFC-based clean energy technology. Full article

Railways are crucial in economic development and improving people’s livelihoods. Therefore, defect detection and maintenance of rails are particularly important. In order to accurately separate and identify the rail defect from the mixed signals by acoustic emission (AE) techniques, this paper proposes a novel time–frequency separation neural network (TFSNN) architecture to solve the problems existing in the blind source separation (BSS), such as in non-stationary signals and low stability in the convergence. Combined with the smoothed pseudo Wigner–Ville distribution (SPWVD), this method can increase the spectrogram resolution, suppress the noise interference, and effectively improve the extraction performance of crack signals. In addition, 1D-CNN and GRU structures were introduced in the TFSNN structure to exploit the dominant features from AE signals. A dense regressor was also subsequently used to estimate the separation weights. Simulation and experiments showed that compared with traditional algorithms like independent component analysis, shallow neural networks, and time–frequency blind source separation, the proposed algorithm can provide better separation performance and higher stability in rail crack detection. Full article

The recent success of emission reduction policies in China has significantly lowered sulfur dioxide (SO₂) levels. However, accurately forecasting these concentrations remains challenging due to their inherent non-stationary tendency. This study introduces an innovative hybrid deep learning model, RF-VMD-Seq2Seq, combining the Random Forest (RF) algorithm, Variational Mode Decomposition (VMD), and the Sequence-to-Sequence (Seq2Seq) framework to improve SO₂ concentration forecasting in five coastal cities of northern China. Our results show that the predicted SO₂ concentrations closely align with observed values, effectively capturing fluctuations, outliers, and extreme events—such as sharp declines the Novel Coronavirus Pneumonia (COVID-19) pandemic in 2020—along with the upper 5% of SO₂ levels. The model achieved high coefficients of determination (>0.91) and Pearson’s correlation (>0.96), with low prediction errors (RMSE < 1.35 μg/m³, MAE < 0.94 μg/m³, MAPE < 15%). The low-frequency band decomposing from VMD showed a notable long-term decrease in SO₂ concentrations from 2013 to 2020, with a sharp decline since 2018 during heating seasons, probably due to the ‘Coal-to-Natural Gas’ policy in northern China. The input sequence length of seven steps was recommended for the prediction model, based on high-frequency periodicities extracted through VMD, which significantly improved our model performance. This highlights the critical role of weekly-cycle variations in SO₂ levels, driven by anthropogenic activities, in enhancing the accuracy of one-day-ahead SO₂ predictions across northern China’s coastal regions. The results of the RF model further reveal that CO and NO₂, sharing common anthropogenic sources with SO₂, contribute over 50% to predicting SO₂ concentrations, while meteorological factors—relative humidity (RH) and air temperature—contribute less than 20%. Additionally, the integration of VMD outperformed both the standard Seq2Seq and Ensemble Empirical Mode Decomposition (EEMD)-enhanced Seq2Seq models, showcasing the advantages of VMD in predicting SO₂ decline. This research highlights the potential of the RF-VMD-Seq2Seq model for non-stationary SO₂ prediction and its relevance for environmental protection and public health management. Full article

With the growing interest in reducing CO₂ emissions to combat climate change, humanity is turning to green or renewable sources of electricity. There are numerous issues associated with the development of these sources. One of the key aspects of renewable energy sources is their problematic controllability, namely the control of energy production over time. Renewable sources are also associated with issues of recycling, utilization in different geographical zones, environmental impact within the required area, and so on. One of the most discussed issues today, however, is the question of efficient use of the energy produced from these sources. There are several different approaches to storing renewable energy, e.g., supercapacitors, flywheels, batteries, PCMs, pumped-storage hydroelectricity, and flow batteries. In the commercial sector, however, mainly due to acquisition costs, these options are narrowed down to only one concept: storing energy using an electrochemical storage device—batteries. Nowadays, lithium-ion batteries (LIBs) are the most widespread battery type. Despite many advantages of LIB technology, the availability of materials needed for the production of these batteries and the associated costs must also be considered. Thus, this battery type is not very ideal for large-scale stationary energy storage applications. Sodium-ion batteries (SIBs) are considered one of the most promising alternatives to LIBs in the field of stationary battery storage, as sodium (Na) is the most abundant alkali metal in the Earth’s crust, and the cell manufacturing process of SIBs is similar to that of LIBs. Unfortunately, considering the physical and electrochemical properties of Na, different electrode materials, electrolytes, and so on, are required. SIBs have come a long way since they were discovered. This review discusses the latest developments regarding the materials used in SIB technology. Full article

Increasing demand for clean energy power generation is a direct result of the rapid depletion of fossil fuel reserves, the volatility of fossil commodity prices, and the environmental damage caused by burning fossil fuels. Fuel cell vehicles, portable power supplies, stationary power stations, and submarines are just some of the applications where proton exchange membrane (PEM) fuel cells are a prominent technology for power generation. PEM fuel cells have several advantages over conventional power sources, including a higher power density, lower emissions, a lower operating temperature, higher efficiency, noiseless operation, ease of design, and operation. The catalyst layer of the membrane electrode assembly is discussed in this paper as a vital part of the proton exchange membrane fuel cell. Along with that, the platinum (Pt)-based catalyst, carbon support, and nafion ionomer found in the catalyst layer often degrade. Catalyst growth, agglomeration, Pt loss, migration, active site contamination, and other microscopic processes are all considered in the degradation process. Employing experimental and numerical research with a focus on enhancing the material properties was suggested as a possible solution to understanding the problem of catalyst layer degradation. Ultimately, this review aims to prevent catalyst layer degradation and lower the high costs associated with replacing catalysts in proton exchange membrane fuel cells through the recommendations provided in this study. Full article

The acoustic analysis of a moving object, such as in pass-by or fly-over tests, is a very important and demanding issue. These types of analyses make it possible to characterize the machine in quite realistic conditions, but the typical difficulties related to source localization and characterization are usually exacerbated by the need to take into consideration and to compensate for the object movement. In this paper, a technique based on acoustic beamforming is proposed, which is applicable to all those cases where the object under investigation is moving. In the proposed technique, the object’s movement is not regarded as a problem but as a resource, enabling a virtual increase in the number of microphone acquisitions. For a stationary acoustic emission from a moving object, each time segment of the acquired signal is treated as if it is coming from a microphone (virtual) positioned differently relative to the object’s reference system. This paper describes the technique and presents examples of results obtained from both simulated and real signals. Performance analysis is conducted and discussed in detail. Full article

This study focuses on understanding the health impacts of hazardous air pollutant (HAP) emissions from the Kaohsiung Coastal Industrial Park and port areas in southern Taiwan on neighboring communities. Six important HAPs (formaldehyde, benzene, arsenic, vinyl chloride, 1,3-butadiene, and diesel particulate matter (DPM)) were identified in this area. By considering the impact of emissions from stationary sources, mobile sources, and port activities, the relative importance of each emission source was assessed. In addition, the AERMOD (AMS (American Meteorological Society)/EPA (U.S. Environmental Protection Agency)) diffusion model was employed to simulate the increases in target pollutant concentrations and to analyze the influence and spatial distribution of various emission sources on atmospheric HAP concentrations in nearby communities. This study further evaluated the exposure risks of composite HAP sources, to understand their impacts and to determine their control priorities. The findings revealed that emissions and carcinogenic weighting from composite sources, particularly DPM emissions from port activities, including from ocean-going vessels and heavy-duty vehicles, had a significant impact. The maximum incremental concentration for DPM in the study area occurred around the port area, whereas the maxima for formaldehyde, benzene, arsenic, vinyl chloride, and 1,3-butadiene were all observed within the industrial complex. DPM emissions from port activities, 1,3-butadiene emissions from mobile sources, and benzene emissions from stationary sources were the composite sources with the greatest potential impacts. Over 90% of health risks were due to DPM, and the remaining health risks were due to 1,3-butadiene (6%), benzene (2%), arsenic (1%), and other species (less than 1%). DPM emissions were primarily influenced by port activities (77%), 1,3-butadiene emissions by mobile sources (45%), and benzene emissions by stationary sources (41%). A total of 25% of the area had risk values greater than 10⁻³, and 75% of the area had risk values between 10⁻³ and 10⁻⁴. The risk values in the densely populated areas were all greater than 10⁻⁴. The potential risk hotspots with risk values greater than 10⁻³ were located on the northwest side of the port and downwind of the industrial park. The key pollutants contributing to these hotspots were, in order, DPM (up to 80% cancer risk), formaldehyde, and 1,3-butadiene, all of which were significantly influenced by port activities. This indicates that the control of, and reduction in, HAP emissions from port activities should be prioritized. Full article

Identifying unknown sources of air pollutants is vital for protecting public health, especially in cases involving the emission of toxic substances. The efficiency of this process depends highly on the accuracy of Source Term Estimation (STE) methods and the availability of robust measurements. Therefore, it is important to examine how sensor network characteristics affect STE accuracy. This study investigates the impact of different sensor configurations on STE results for a stationary point source in a complex, urban-like environment. The STE methodology employs the Metropolis–Hastings Markov Chain Monte Carlo (MCMC) algorithm alongside numerical simulations of a Computational Fluid Dynamics (CFD) model. The STE algorithm is applied across several sensor configurations in three distinct release scenarios and real sensor observations from the Michelstadt wind tunnel experiment, assessing both the number of sensors used and the agreement between measured and modeled concentrations. In general, the results indicate that increasing the number of sensors and the model’s accuracy improves the source parameters estimations. However, there is a specific number of sensors in each release scenario where STE outcomes from randomly selected, high-accuracy, and low-accuracy sensors converge to similar solutions. Overall, the findings provide valuable information for designing sensor configurations in urban areas. Full article

The zero drift, interference, and sensitivity of an HCl analyzer based on a non-dispersive infrared (NDIR) technique can be improved to develop a cost-effective solution for continuous emission monitoring systems (CEMSs). To achieve these improvements, the same bandpass filter technique, negligible interference bandpass filter, and optimal path length are applied to the analyzer. Laboratory inspections and long-term field trials are conducted to evaluate the performance of the analyzer. A metalworking factory and a cement factory are selected for field trials. In laboratory inspections, the relative error of the analyzer is less than 1%, aligning closely with the results obtained from standard ion chromatography methods. Moreover, the basic specifications of the proposed analyzer are comparable to those of commercial HCl analyzers. In field trials, the NDIR analyzer shows a significant bias compared to the standard method. However, when considering the difference between HCl emission levels and HCl emission standards, the relative errors are less than 10%. These results suggest the proposed NDIR analyzer is a practical option for the CEMS of metalworking and cement factories. However, seasonal variations should be considered when the temperatures of gas emissions are low. Full article

Hydrogen storage technologies are key enablers for the development of low-emission, sustainable energy supply chains, primarily due to the versatility of hydrogen as a clean energy carrier. Hydrogen can be utilized in both stationary and mobile power applications, and as a low-environmental-impact energy source for various industrial sectors, provided it is produced from renewable resources. However, efficient hydrogen storage remains a significant technical challenge. Conventional storage methods, such as compressed and liquefied hydrogen, suffer from energy losses and limited gravimetric and volumetric energy densities, highlighting the need for innovative storage solutions. One promising approach is hydrogen storage in metal hydrides, which offers advantages such as high storage capacities and flexibility in the temperature and pressure conditions required for hydrogen uptake and release, depending on the chosen material. However, these systems necessitate the careful management of the heat generated and absorbed during hydrogen absorption and desorption processes. Thermal energy storage (TES) systems provide a means to enhance the energy efficiency and cost-effectiveness of metal hydride-based storage by effectively coupling thermal management with hydrogen storage processes. This review introduces metal hydride materials for hydrogen storage, focusing on their thermophysical, thermodynamic, and kinetic properties. Additionally, it explores TES materials, including sensible, latent, and thermochemical energy storage options, with emphasis on those that operate at temperatures compatible with widely studied hydride systems. A detailed analysis of notable metal hydride–TES coupled systems from the literature is provided. Finally, the review assesses potential future developments in the field, offering guidance for researchers and engineers in advancing innovative and efficient hydrogen energy systems. Full article