Improve Energy Production Research Articles

Improvement of antioxidant protection and energy production in neurodegenerative processes in the brain caused by long-term alcoholism under the influence of Lophantus chinensis Benth extract

Long-term alcohol abuse causes psycho-emotional and cognitive impairment, including severe dementia. Oxidative stress is considered one of the main mechanisms in the cognitive disorders pathophysiology caused by long-term alcoholism. In this regard, the search for substances capable of correcting mitochondrial dysfunction and oxidative stress that arise as a result of prolonged ethanol consumption is relevant. Of particular interest in the complex treatment of alcoholic encephalopathy is Chinese hyssop (Lophanthus chinensis), used in Tibetan and Mongolian traditional medicine for liver diseases, as well as a means of improving the functional state of the body and metabolism, slowing down the aging process. Material and methods. Alcohol intoxication was modeled in Wistar rats by per os administration of a 40 % ethanol solution in a volume of 10 ml/kg for six weeks. The L. chinensis dry extract at a dose of 100 mg/kg was administered to animals per os starting from the third week, an hour after ethanol. On day 45, the content of malondialdehyde (MDA), reduced glutathione (GSH), activity of catalase, superoxide dismutase (SOD), glutathione peroxidase (GPx), glutathione reductase, NADH dehydrogenase and succinate dehydrogenase complex, concentration of ATP werei determined spectrophotometrically in the animal brain homogenate. Results. It was found that L. chinensis extract increases the activity of catalase, superoxide dismutase, glutathione peroxidase, glutathione reductase (by 10 %, р = 0.016, by 31 %, р = 0.001, by 30 %, р = 0.041, by 29 %, р = 0.009, respectively) and GSH content (by 24 %, р = 0.019), and also reduces the concentration of MDA (by 20 %, р = 0.014) in brain tissue. A decrease in the intensity of oxidative stress enhances the functioning of enzymatic complexes I (by 23 %, р = 0.017) and II of the mitochondrial respiratory chain (by 72 %, р = 0.001) and increases the ATP content (by 23 %, р = 0.029) in the rat’s brain. Conclusions. L. chinensis extract exhibits antioxidant effects and stimulates energy processes in neurodegenerative processes in the brain caused by long-term alcoholism.

Use of Computational Fluid Dynamics to investigate heat exchanges in Floating Solar Plate Systems

This study aims to investigate the temperature gradient between floating and non-floating solar panels, with the purpose of comparing results and enhancing the efficiency of solar energy production by reducing the temperature of the solar modules. This modeling is crucial to inform future development and implementation of more effective and sustainable solar systems, as well as to pursue the implications caused by these installations.Thermal analyses between floating and non-floating solar modules were conducted using Computational Fluid Dynamics (CFD) via the Finite Volume Method to obtain the variables of interest. These finite volumes are interconnected by points called nodes. The set of all these elements and nodes is called a mesh, produced by the discretization of the geometry. All stages of this simulation were performed by entirely open-source software, from geometry and mesh generation to result visualization.The simulation revealed a significant difference between the temperatures of the floating and non-floating panels, approximately 6 K (6°C), considering only one photovoltaic module. This discrepancy tends to have a positive impact on energy production, as the temperature of the solar cell affects its electrical current. Thus, reducing the temperature of the solar cells, as observed in the floating panel, results in an improvement in energy production. However, it is important to note that the increase in temperature resulting from the installation of a photovoltaic array on a large scale may lead to an increase in water temperature.In summary, the experimental data presented by the simulation, corroborated by previous experiments, highlights the relevance of research in the field of solar energy, emphasizing the potential of innovative solutions, such as solar panel floating, to boost the efficiency and sustainability of photovoltaic energy generation. All of this supports our results and encourages us to continue investigating innovative solutions in the field, with the learning and improvement of simulations via computational methods and the perpetuation of the use of open-source tools.

Management of the lignocellulosic structure of agricultural straw and improving energy productivity

Management of the lignocellulosic structure of agricultural straw and improving energy productivity

Impact of Physical Activity on Cellular Metabolism Across Both Neurodegenerative and General Neurological Conditions: A Narrative Review.

Regular physical activity plays a crucial role in modulating cellular metabolism and mitigating the progression of neurodegenerative diseases such as Alzheimer's, Parkinson's, and Multiple Sclerosis. The objective of this review is to evaluate the molecular mechanisms by which exercise influences cellular metabolism, with a focus on its potential as a therapeutic intervention for neurological disorders. A comprehensive literature review was conducted using peer-reviewed scientific articles, with a focus on the period between 2015 and 2024, to analyze the effects of exercise on mitochondrial function, oxidative stress, and metabolic health. The findings indicate that exercise promotes mitochondrial biogenesis, enhances oxidative phosphorylation, and reduces reactive oxygen species, contributing to improved energy production and cellular resilience. These metabolic adaptations are associated with delayed disease progression and reduced symptoms in patients with neurodegenerative conditions. Additionally, integrating exercise with nutritional strategies may further enhance therapeutic outcomes by addressing metabolic disturbances comprehensively. This review concludes that personalized exercise protocols should be developed to optimize metabolic benefits for patients with neurological diseases, while future research should focus on biomarker development for individualized treatment approaches. These findings highlight the importance of non-pharmacological interventions in managing neurodegenerative diseases.

Growth Performance, Dietary Energetics, Blood Metabolites, Carcass Traits, Meat Quality, and Gene Expression of Lambs Supplemented with a Polyherbal Phytogenic Additive.

This study aimed to evaluate the effects of supplementation with a polyherbal phytogenic additive (PPA) on the productive performance, dietary energetics, blood metabolites, carcass traits, meat quality, and gene expression of finishing lambs. Thirty-six male Pelibuey lambs (23.61 ± 0.57 kg body weight (BW)) were housed in individual pens and assigned to four treatments (n = 9) with different doses of PPA: 0 (CON), 2.5 (PPAL), 5 (PPAM), and 7.5 (PPAH) g of PPA/kg of DM for 56 days. Average daily gain, dry matter intake, and observed dietary net energy for maintenance and weight gain increased linearly (p < 0.05) in lambs supplemented with PPAH. A linear reduction (p = 0.02) in FCR was detected in lambs fed PPAM and PPAH. The PPAH supplementation linearly increased (p < 0.001) Longissimus dorsi muscle area, but other carcass traits were not affected (p > 0.05) by PPA doses. The physicochemical characteristics of the meat and the hematological parameters of the lambs were not affected (p > 0.05) by the PPA doses. The glucose, uric acid, creatinine, and bilirubin serum concentrations decreased linearly (p < 0.05) in lambs supplemented with PPAM and PPAH. Gene ontology analyses showed that nine biological processes were modified (p < 0.05), including DNA replication, drug metabolism-cytochrome P450, oxidative phosphorylation, and chemical carcinogenesis-reactive oxygen species. In conclusion, high doses (7.5 g/kg DM) of PPA can improve growth performance and dietary energy utilization efficiency in finishing lambs. Likewise, gene expression analysis indicates that supplementation with high doses of PPA could improve energy production and antioxidant status in finishing lambs.

Metabolic Adaptations in Phosphine-Resistant Tribolium castaneum Driven by Mitochondrial Enzyme Variability and Gene Expression.

Phosphine fumigation is essential for controlling storage pests like Tribolium castaneum, but its frequent application has resulted in resistance, primarily due to mutations in the Dihydrolipoamide dehydrogenase (DLD) gene associated with the rph2 allele. This study demonstrates that the Patiala population exhibits homozygous resistance variations across populations, contrasting with the susceptibility observed in laboratory cultures. Our assessment of mitochondrial DLD and Cytochrome c oxidase (COX) activities showed significantly elevated levels in the Patiala population, with increases of approximately sevenfold for DLD and 6.92-fold for COX, indicating mitochondrial adaptations for enhanced energy production. Kinetic analyses of DLD in the resistant Patiala population showed a higher Vmax (0.005 mmol/min) and a significantly increased Km (16.66 mM), indicating variations in maximal enzyme activity and substrate affinity. Furthermore, resistant T. castaneum populations displayed substantial upregulation of DLD and COX gene expression, with DLD expression increasing by 10.53-fold and COX expression peaking at 102.57-fold in Patiala. Pearson correlation analysis indicated strong positive correlations (r > 0.8) between enzymatic activity and gene expression for both DLD and COX, suggesting a coordinated role in resistance mechanisms. The PCA biplot illustrated distribution patterns of enzymatic activity and gene expression among field-resistant populations, highlighting the association between increased resistance and elevated enzymatic activity and gene expression levels. Therefore, the upregulation of DLD and COX activities in resistant populations underscores their critical roles in counteracting phosphine, reflecting metabolic reprogramming for improved energy production under stress.

Adapting to climate change in arid agricultural systems: An optimization model for water-energy-food nexus sustainability

Sustainable management of water, energy, and food (WEF) under climate change will be a significant challenge for arid agricultural systems. This study developed a fractional non-linear multi-objective programming (FNLMOP) model to optimize resource allocation and improve agricultural sustainability in these systems under climate change. The model was designed in the framework of the WEF nexus to simultaneously improved energy productivity (profit/energy), and water productivity (profit/water), while mitigating environmental damage (damage to groundwater resources/output) and ensuring food security in an arid watershed in Iran. The long Ashton research station weather generator (LARS-WG) and the coupled model intercomparison project 6 (CMIP6) were employed to project climate parameters for both future dry and wet conditions. The sustainability of the optimal solutions was then assessed using a hybrid criteria importance through intercriteria correlation (CRITIC)-VIKOR approach. The optimal solutions revealed a reduction in the land under cultivation and produced less water-intensive crops. The optimization model can ensure WEF security, enhancing agricultural system sustainability by optimizing crop cultivation patterns and resource allocation. Current crop choices were highly inefficient with the bigger changes being from the current crops to optimal crops. Climate change showed a substantial but lesser influence on optimal crop choice.

Modeling and optimization for arrays of water turbine OWC devices

The large-scale implementation of wave energy conversion requires the installation of parks of devices. We study the problem of optimizing park layout and control for wave energy converters of the oscillating water column type. As a test case, we consider a device with a Wells turbine working in water. First, a novel model based on a nonlinear ordinary differential equation is derived to describe the behavior of the water column and used to estimate the power matrix. Then, its linearization is derived in order to enable the fast simulation of large parks of devices. The choice of the hydrodynamic model allows obtaining the gradient of the power with respect to the positions through an adjoint approach, making it especially convenient for optimization. We consider in particular the case of interaction with the piles of a floating wind energy plant. The results from the developed computational framework allow us to draw interesting conclusions about park layout design. In particular, we observe that mutual interaction effects can be significant even in parks made up of devices of small size, and that wave reflection from the piles of an offshore structure can improve energy production.

Optimizing solar power generation forecasting in smart grids: a hybrid convolutional neural network -autoencoder long short-term memory approach

Incorporating zero-carbon emission sources of energy into the electric grid is essential to meet the growing energy needs in public and industrial sectors. Smart grids, with their cutting-edge sensing and communication technologies, provide an effective approach to integrating renewable energy resources and managing power systems efficiently. Improving solar energy efficiency remains a challenge within smart grid infrastructures. Nonetheless, recent progress in artificial intelligence (AI) techniques presents promising opportunities to improve energy production control and management. In this study, initially, we employed two different Machine learning (ML) models: Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM), to forecast solar power plant parameters. The analysis revealed that the LSTM model performed better than RNN in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). Following a review of the LSTM model’s graphical results, it was further enhanced by combining Autoencoder with LSTM, creating the Autoencoder LSTM (AELSTM) model. Next, a new hybrid model was introduced: Convolutional Neural Network-Autoencoder Long Short-Term Memory (HCAELSTM), designed to boost prediction accuracy. These models were trained on a one-year real-time solar power plant dataset for training and performance assessment. Ultimately, the hybrid HCAELSTM model surpassed the AELSTM model in terms of MAPE, MAE, and MSE. It excelled in MAPE scores for Daily Power Production, Peak Grid Power Production, and Solar Radiance, achieving low scores of 1.175, 2.116, and 1.592 respectively, demonstrating superior accuracy. The study underscores the importance of AI and ML, in particular, the hybrid model HCAELSTM, in enhancing the smart grid’s ability to integrate renewable energy sources. The hybrid model excels at accurately forecasting key measurements, improving solar power generation efficiency within the smart grid system which also plays a key role in the broader shift toward the fourth energy revolution.

Dependability Assessment of a Dual-Axis Solar Tracking Prototype Using a Maintenance-Oriented Metric System

This study presents a numerical method for evaluating the maintainability of a dual-axis solar tracking system that can be deployed in residential areas for improved energy production. The purpose of this research manuscript is threefold. It targets the following objectives: (i) First, we present the construction of a self-sufficient dual-axis solar tracking system based on a low-power electronic schematic that requires only one motor driver to control the azimuth and elevation angles of the photovoltaic (PV) panel. The automated system’s main electronic equipment comprises 1 × Arduino Mega2560 microcontroller unit (MCU), 1 × TB6560 stepper driver module, 2 × stepper motors, 2 × relay modules, 1 × solar charge controller, 1 × accumulator, and 1 × voltage convertor. Additional hardware components such as photoresistors, mechanical limit switches, rotary encoders, voltage, and current sensors are also included to complete the automation cycle of the solar tracking system. (ii) Second, the Arduino Mega 2560 prototyping board is replaced by a custom-made and low-cost application-specific printed circuit board (ASPCB) based on the AVR controller. The MCU’s possible fault domain is then further defined by examining the risks of the poor manufacturing process, which can lead to stuck-at-0 (Sa0) and stuck-at-1 (Sa1) defects. Besides these issues, other challenges such as component modularity, installation accessibility, and hardware failures can affect the automated system’s serviceability. (iii) Third, we propose a novel set of maintenance-oriented metrics that combine the previously identified variables to provide a maintainability index (MI), which serves as a valuable tool for evaluating, optimizing, and maintaining complex systems such as solar tracking devices. The experimental data show that the computed MI improves the system’s maintainability and enhances repair operations, increasing uptime.

Evaluating the influence of calcined eggshells and ultrasonication in the Co-digestion of avoidable and unavoidable Food Waste and OLS regression analysis of the reactor system

The technological advancements in the bio-conversion of food waste (FW) into biogas signify substantial advancements in the field of waste management. However, persistent operational challenges like rapid acidification and difficulties in maintaining the C/N ratio in mono-digestion remain, despite extensive research efforts. The present study aims to evaluate the reactor performance in co-digestion, specifically using a 1:1 ratio of unavoidable food waste (UAFW) and avoidable food waste (AFW), with the addition of calcined eggshells as an additive. Further, the study employs Ordinary Least Square (OLS) regression modeling to predict the biogas production from the co-digestion process. The experimental process involves ultrasonication pretreatment, focusing on calcined eggshells for their ability to preserve carbohydrates via Ca(OH)2, an alkaline material. The semi-continuous operation of the reactor spanned for 120 days and was divided into three phases with varying Organic Loading Rates (OLR) from 0.6 to 2.2 gVS/l/d. Characterization studies such as SEM, FTIR and TGA-DTA were performed to validate the pretreatment method and addition of egg-shells. Results indicated increased OLR led to higher Volatile Solids (VS) reduction. This was attributed to the calcined eggshells' role in adsorbing NH4+ and NO3− ions while maintaining pH levels, resulting in significantly higher biogas production. The multiple regression models used in the study yielded promising results, surpassing simple linear regression, with an adjusted R-squared value > 0.9 and p-value <0.05. The model effectively anticipated the specific methane yield, demonstrating the capability to improve energy production by co-digesting AFW and UAFW. The study presents an opportunity to divert organic waste from landfills, reduce greenhouse gas emissions, and promote sustainable waste management by using bio-waste additives, reducing reliance upon synthetic chemicals.

Green energy policies and energy poverty in Europe: Assessing low carbon dependency and energy productivity

This study examines the dynamics of the transition to green economies in relation to energy poverty in European countries. By employing augmented mean group (AMG) and fully modified ordinary least squares estimations (FMOLS), we find empirical evidence that indicates the influence of variables representing the processes of transitioning to green economies on energy poverty. To further elucidate these relationships, Granger causality analysis was conducted. The findings of this research contribute to the ongoing discourse surrounding the transition to green economies from the perspective of energy productivity. It is observed that improvements in energy productivity are associated with a reduction in the prevalence of energy poverty. Additionally, a noteworthy relationship is identified between energy poverty and the consumption of green energy, whereby an increase in energy poverty corresponds to an increase in the proportion of green energy within the overall energy consumption mix. Accordingly, it is recommended that public policies complement the transition to green societies with measures aimed at ensuring adequate energy provision for the population.

Measuring wake deflection from SCADA data during wake steering using machine learning

As wake steering is implemented on large wind plants, methods for evaluating how well these systems optimize plant performance are needed. The data analysis from experiments conducted so far generally focuses on evaluating the improvement in power or energy production of the plant and comparing that to the predicted improvement. In this study, we explore a new approach to provide additional insight and validation of optimization tools by measuring the wake deflection experimentally using only the downstream turbine as a sensor. This approach is demonstrated using the SMARTEOLE wind plant wake steering experimental data. Light gradient boosting machines (LGBM) and Gaussian process (GP) machine learning models are trained and then interrogated by making a set of predictions under defined conditions to determine the wake deflection observed at the downstream turbine. The data set is sparse, particularly for larger yaw angles, and noisy due to factors not captured in the SCADA leading to relatively high uncertainty in the predictions. The GP model is better suited to smoothly fit this data. Using the GP model, a wake deflection of 0.35 rotor diameters at 3.7 rotor diameters downwind is estimated for a yaw error of 20 degrees on the upwind turbine.

Development of Cycloid-Shaped Roll Charging Chute for Sintering Process for Energy Decarbonization and Productivity Improvement in Steel Plants

The global steel industry is rapidly transitioning towards energy decarbonization to address the climate crisis. Sintering is one of the main sources of greenhouse gas emissions from steel mills. Traditional sintering processes use straight inclined chutes to feed raw materials into the sinter machine. However, this design suffers from insufficient horizontal momentum, resulting in poor segregation of the layered materials. This study proposes an improved charging chute design profile that uses a cycloid curve and rolls to enhance segregation, thus reducing coal consumption and increasing productivity. To achieve this, we first modeled a charging chute using the cycloid curve. Secondly, building upon the cycloid concept, we created a roll-type chute by strategically placing rollers along the cycloid trajectory. Finally, the cycloid roll-type charging chute, integrating the cycloid trajectory with the roll-shaped charging chute, was simulated. Pilot tests comparing the cycloid roll-type and straight chute models demonstrated a significant increase in dispersion for the cycloid roll-type design, with a 65% improvement in the Strand-ward segregation (Sw) index compared to the straight chute. Furthermore, actual filed implementation in a sintering process achieved a 2.9% increase in operational productivity and a 6% reduction in fuel consumption. This study is significant not only for proposing an optimal chute design, but also for successfully implementing it in a full-scale steel mill, contributing to a reduction in fuel consumption and carbon reduction in steel mills.

Resource utilization potential of municipal solid waste affects the sustainable development goals progress in China

In the past decades, municipal solid waste (MSW) has generally been recognized as a substance that poses risks to human health and the ecosystem. With the emergence of the sustainable idea, MSW began to be recognized as a renewable resource and technological methods were used to make MSW as a resource utilization potential (RUP) source. SDGs realization relies on MSW's sustainable management. However, how the MSW's RUP affects to SDGs and how the two are coupled still needs to be explored. In this study, we took China, the largest MSW producer in developing countries, as an example. First, we selected 17 SDG indicators (including 83 nested indicators) and assessed China's SDG progress from 2005 to 2021 based on the SDG weighting calculation method. Regression analysis and structural equation modeling (SEM) were then used to explore the direct and indirect impacts of MSW's RUP on the SDGs. Finally, a counterfactual analysis was used to determine the regional heterogeneity of the impact of MSW's RUP on SDGs. The results show that China made greater progress in its SDG scores overall between 2005 and 2021, with particularly strong progress in SDG1 (poverty eradication) and SDG3 (healthy living). The MSW's RUP contributes positively to SDGs 1, 3, 6, 8, 9, 10, 11, and 17 (economy, health, water, industry, community, and global partnerships). The response of SDGs 2, 7, and 16 (zero hunger, energy, and justice) to MSW's RUP showed a threshold when the MSW's RUP value was 0.3 GJ. The MSW's RUP has a negative impact on SDG13 and SDG15 (climate change and ecosystems). Regional heterogeneity exists in the relationship between MSW's RUPs and the SDGs, therefore the promotion of MSW's RUPs is targeted to different provinces. The MSW's RUP provides significant potential for improvement in energy production and material production, and adherence to managing MSW using a sustainable mindset will contribute to the goal of carbon neutrality by 2060.

Solar PV tracking system using arithmetic optimization with dual axis and sensor

Presently, PV system is considered the best renewable energy source for electricity production. In order to maximize energy output in photovoltaic systems, a system for tracking the sun's position and adjusting panel positions was created. Despite the fact that several models for tracking solar radiation have been suggested to improve energy production, it faces challenges in continuous tracking and power consumption. This paper proposes a novel sensor-based solar tracking system with numerical optimization to increase photovoltaic systems' energy output. The initial model was for a two-axis tracking system based on sensors. Solar panel and sun positions are detected by this system using ultraviolet and microelectromechanical sun sensors. To improve tracking movements and photovoltaic energy production, we recommend using solar sensors to construct a novel two-axis solar tracking device. This technology benefits from increased solar radiation and solar energy harvesting capabilities. The main disadvantage of dual-axis active solar tracking systems is that the drive mechanism frequently uses up the output power of the solar panels. As a result, the net power gain of the solar panel is less than its maximum. Further, a control system was developed with proportional integral derivative (PID) controller and arithmetic optimization (AO) to adjust the panel position relative to suns movement. The AO technique tunes the PID controller gains until it reaches its desired level to enhance the production of power. With the use of sensors, the tracking system can use mathematical optimization algorithms and the sun's position to minimize power consumption while still maintaining optimal solar panel positioning. The developed model was designed in the MATLAB software and the outcomes are analyzed. The paper also presents an energy analysis of the system, which evaluates the energy input, output and overall system efficiency. We recommend using solar sensors to create a unique dual-axis solar tracking system. This technology benefits from increased solar radiation and solar energy harvesting capabilities.

Black TiO2-infused polydimethylsiloxane composites for efficient solar-assisted water evaporation and thermoelectric energy generation

Solar assisted interfacial water evaporation through the strategic localization of solar- thermal energy conversion by relying on the air–water interface has garnered significant interest owing to its enhanced efficiency in a variety of applications, from water purification to energy generation. Nevertheless, achieving utterly improved thermal management to enhance the efficiency of solar heat consumption is still quite complex. Herein, we created special composites of black titanium dioxide and polydimethylsiloxane (B-TiO2/PDMS) aiming effectively harvest a broad range of spectrum of solar radiation for the benefit of improving energy production and aid for water purification. The B-TiO2/PDMS sponge possesses an impressive optical to thermal transformation as an outcome, keeping a constant surface temperature of 66 °C, as opposed to 41.6 °C for PDMS sponge at typical 1 sun intensity for 10 min. This solar evaporation system constitutes a greater evaporation rate of 1.39 Kg m−2h−1 with a photothermal conversion efficiency of 70.8 % under 1 sun illumination. To accomplish integrated thermoelectric power generation during solar evaporation, the thermoelectric (TE) module is used as an insulating medium, succeeding an optimal yield of 30 mV under 1 sun. Furthermore, the as prepared B-TiO2/PDMS sponge achieved notable mechanical resilience and incredible adaptability, offering a viable approach for thermal management and cooperative way to utilize solar powered evaporation to produce potable water and energy.

Carbon trade-off and energy budgeting under conventional and conservation tillage in a rice-wheat double cropping system

Amid rising energy crises and greenhouse gas (GHG) emissions, designing energy efficient, GHG mitigation and profitable conservation farming strategies are pertinent for global food security. Therefore, we tested a hypothesis that no-till with residue retaining could improve energy productivity (EP) and energy use efficiency (EUE) while mitigating the carbon footprint (CF), water footprint (WF) and GHG emissions in rice-wheat double cropping system. We studied two tillage viz., conventional and conservation, with/without residue retaining, resulting as CT0 (puddled-transplanted rice, conventional wheat -residue), CTR (puddled-transplanted rice, conventional wheat + residue), NT0 (direct seeded rice, zero-till wheat -residue), and NTR (direct seeded rice, zero-till wheat + residue). The overall results showed that the NTR/NT0 had 34% less energy consumption and 1.2-time higher EP as compared to CTR/CT0. In addition, NTR increased 19.8% EUE than that of CT0. The grain yield ranged from 8.7 to 9.3 and 7.8–8.5 Mg ha−1 under CT and NT system, respectively. In NTR, CF and WF were 56.6% and 17.9% lower than that of CT0, respectively. The net GHG emissions were the highest (7261.4 kg CO2 ha−1 yr−1) under CT0 and lowest (4580.9 kg CO2 ha−1 yr−1) under NTR. Notably, the carbon sequestration under NTR could mitigate half of the system's CO2-eq emissions. The study results suggest that NTR could be a viable option to offset carbon emissions and water footprint by promoting soil organic carbon sequestration, and enhancing energy productivity and energy use efficiency in the South Asian Indo-Gangetic Plains.

High-Speed Rail and Energy Productivity: Evidence from China

Using the difference-in-differences method combined with the propensity score matching, this study identifies the causal relationship between high-speed rail (HSR) and energy productivity in China. Furthermore, we investigate the mechanism through which HSR affects energy productivity, as well as the heterogeneity of the impact across quantiles and distances. The results show that HSR connection contributes to the improvement of energy productivity. This finding is consolidated after a potential endogeneity problem is addressed using the instrumental variable method and a variety of potential confounders are controlled through a series of robustness checks. On average, the marginal impact of HSR on energy productivity is approximately 9%. Moreover, HSR connection cannot be completely substituted by traditional railway and aviation in improving energy productivity. The heterogeneity analysis suggests that the positive energy productivity effect of HSR gradually decreases with an increasing distance to the nearest HSR station. In addition, HSR network accessibility has a significant positive effect on energy productivity, while technological innovation mediates the relationship between HSR development and energy productivity. We propose that to achieve the long-term improvement of energy productivity, policymakers should comprehensively consider both transit-oriented development and ecology-oriented development modes.

Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids

This study investigates the optimization of the size of a solar-wind hybrid microgrid using Particle Swarm Optimization (PSO) to improve energy production efficiency, economic feasibility, and overall sustainability. By using past solar and wind resource data, load demand profiles, and system component specifications, the PSO algorithm effectively maximized the capabilities of solar panels and wind turbines. The findings indicate a significant rise in daily energy production, with a 15% enhancement in solar panel capability and a 12% boost in wind turbine capability. The increased energy production plays a crucial role in dealing with the natural irregularity of renewable resources, hence enhancing the resilience and self-reliance of the microgrid. The economic calculations demonstrate significant improvements in the economic feasibility of the microgrid designs. The Levelized Cost of Energy (LCOE) undergoes a significant 10% decrease, suggesting a more economically efficient energy generation. Moreover, the payback time for the original expenditure is reduced by 15%, indicating faster returns on investment. The economic improvements highlight the practical advantages of using PSO for microgrid size, in line with the goal of creating sustainable energy solutions while minimizing economic costs. The improved performance of Particle Swarm Optimization (PSO) is shown by a thorough comparison study with other optimization approaches, such as Genetic Algorithms (GA) and Simulated Annealing (SA). The superior convergence rate of PSO, together with a 15% enhancement in solution quality relative to GA and SA, underscores the efficiency and efficacy of PSO in traversing the complex solution space associated with microgrid size. PSO’s comparative advantage makes it an effective tool for tackling the intricacies of integrating renewable energy, highlighting its potential for extensive use in microgrid design and optimization. The sensitivity evaluations demonstrate that the solutions optimized by the PSO are resilient even when important parameters vary, thereby highlighting the stability and dependability of the approach. In addition to technical and economic factors, the study evaluates the environmental consequences and social aspects of the optimum microgrid designs. The land use efficiency has seen a 10% enhancement, demonstrating the optimum application of area for renewable energy infrastructure. In addition, there is a 7% improvement in community approval, which demonstrates the algorithm’s ability to effectively handle social aspects and promote a comprehensive and socially acceptable approach to renewable energy projects.