Energy Generation Processes Research Articles

Reducing Emissions Using Artificial Intelligence in the Energy Sector: A Scoping Review

Global warming is a significant threat to the future of humankind. It is caused by greenhouse gases that accumulate in the atmosphere. CO2 emissions are one of the main drivers of global warming, and the energy sector is one of the main contributors to CO2 emissions. Recent technological advances in artificial intelligence (AI) have accelerated the adoption of AI in numerous applications to solve many problems. This study carries out a scoping review to understand the use of AI solutions to reduce CO2 emissions in the energy sector. This paper follows the PRISMA-ScR guidelines in reporting the findings. The academic search engine Google Scholar was utilized to find papers that met the review criteria. Our research question was “How is artificial intelligence used in the energy sector to reduce CO2 emissions?” Search phrases and inclusion criteria were decided based on this research question. In total, 186 papers from the search results were screened, and 16 papers fitting our criteria were summarized in this study. The findings indicate that AI is already used in the energy sector to reduce CO2 emissions. Three main areas of application for AI techniques were identified. Firstly, AI models are employed to directly optimize energy generation processes by modeling these processes and determining their optimal parameters. Secondly, AI techniques are utilized for forecasting, which aids in optimizing decision-making, energy transmission, and production planning. Lastly, AI is applied to enhance energy efficiency, particularly in optimizing building performance. The use of AI shows significant promise of reducing CO2 emissions in the energy sector.

Advances in circular economy models for sustainable energy supply chains

The transition to sustainable energy systems has placed increasing importance on the adoption of circular economy (CE) models, particularly within energy supply chains. Circular economy practices emphasize the efficient use of resources, waste minimization, and the reuse, recycling, or repurposing of materials, promoting long-term sustainability. In energy supply chains, this model aims to reduce environmental impact, enhance resource efficiency, and improve overall economic performance. Recent advancements in circular economy models have facilitated the integration of renewable energy sources such as solar, wind, and hydropower into energy supply chains, significantly lowering carbon emissions. Energy producers are now focusing on maximizing the lifecycle of resources and optimizing energy generation and distribution processes. One of the core components of CE in energy is the shift toward closed-loop systems, where waste from one process becomes the input for another, minimizing environmental degradation and resource depletion. Furthermore, waste-to-energy technologies and energy storage systems are becoming crucial in reducing energy wastage and improving grid reliability. Key innovations include the development of energy-efficient technologies, such as smart grids, and advances in digital tools that enable real-time monitoring of energy consumption and emissions. These technologies allow energy companies to optimize their operations, reduce inefficiencies, and enhance supply chain resilience. Additionally, digitalization and data analytics are supporting the tracking of resource flows, carbon footprints, and waste streams, driving transparency and accountability across the supply chain. However, the widespread implementation of circular economy models in energy supply chains still faces significant challenges, including technological limitations, financial constraints, and regulatory barriers. Governments and industries must collaborate to create frameworks that support circular practices and incentivize investment in sustainable solutions. Despite these challenges, the potential of CE models to drive the transformation toward low-carbon, resource-efficient energy systems is undeniable. By advancing these models, energy supply chains can contribute significantly to global sustainability goals, reducing the carbon footprint and fostering long-term resilience in the energy sector. Keywords: Circular Economy, Sustainable Energy, Energy Supply Chains, Renewable Energy, Resource Efficiency, Carbon Emissions, Waste-To-Energy, Smart Grids, Digitalization.

Reactivation of Abandoned Oilfields for Cleaner Energy Generation: Three-Dimensional Modelling of Reservoir Heterogeneity and Geometry

With the changing picture of global energy supplies and the shift toward the energy transition, it has never been more important to look for alternative sources of energy. Globally there are tens of thousands of abandoned oil fields with considerable reserves left behind. These have the potential to be reactivated to become an energy supply that is cleaner than conventional oil and gas. This can be achieved by the use of in situ combustion and the subsequent exploitation of the inherent increase in temperature and pressure to produce geothermal energy, allied to sequestration of the mixture of produced fluids. In situ combustion (ISC) has conventionally been used as an enhanced oil recovery technique, with a high failure rate that has been recently attributed to poor reservoir selection and project design. We suggest that the failure of many earlier ISC projects is due to insufficient appreciation of how the subsurface geology affects the process. With the use of computer numerical modelling, we aim to ascertain how the geometry and heterogeneity of the reservoir control the success of the process. Here we employ simple three-dimensional sector models to assess a variety of different petrophysical heterogeneities, within a set of different reservoir geometries, on the temperature, velocity, propagation stability and enthalpy rate. These models illustrate that the biggest impact on success of the ISC process for geothermal energy generation, as a function of temperature and enthalpy, is the location of the wells relative to the heterogeneities and the scale of heterogeneities. Metre-scale heterogeneities do not have a significant effect on this. Instead, the biggest contributor to the propagation stability and direction of the fire front is the presence of a large-scale (10 s to 100 s of metres) heterogeneities, such as channels, or the geometry of a tilted fault block; both have a strong control over the direction of the propagation, and therefore are important factors with regards to well placement.

Optimal Commitment Strategy of the Wind-battery-hydrogen Hybrid System in the Spot Market

In this paper, we consider the problem of making the advance power commitments for the wind farms and the hydrogen supply plan for the hydrogen spot market, in the presence of the hybrid energy storage system with battery based and hydrogen based, mean-reverting price pocesses of power and hydrogen and the auto regressive energy generation process from winds, which extends the model in (Kim & Powell, 2011) and (Finnah & Gönsch, 2021). The problem is solved with dynamic programming algorithm with backward induction and the infinite horizon analysis. We obtained an optimal energy commitment policy and an optimal hydrogen supply plan under the above assumptions, which are indeed the extensions of the conclusion in (Kim & Powell, 2011) and (Finnah & Gönsch, 2021). Finally the property of stationary process of the hybrid storage levels corresponding to the optimal policy and plan are proved.

Методика оценки технического состояния цилиндропоршневой группы судового дизеля на основе теории графов и математического анализа

Carrying out diagnostics of the ship power plant and estimation of the technical condition still remain not fully resolved issues. This is due, first of all, to a large number of active external factors, as well as to the complex process of interaction of the constituent plant elements. The cylinder-piston group of the ship diesel engine is especially sensitive to the effects of the environment. The operating conditions of this mechanism are under the influence of high temperatures and loads. The operational condition of the mating elements of the cylinder-piston group also largely depends on the physical and chemical characteristics of the working media – participants in the energy generation process. Forecasting the occurrence of both increased wear and the timing of natural aging of materials has been a task of engineering science since the advent of technology. The modern methods used in most scenarios imply the need to stop the engine, disassemble it and defect it (to determine the actual condition of the equipment and establish the amount of wear on the parts). This practice is extremely inconvenient for the prompt assessment of the technical condition under sailing conditions. Non-disassembly diagnostics is an alternative. The usage of four approaches to non-disassembly diagnostics of the marine diesel engine consists of sampling and oil analysis; vibration diagnostic control; thermographic research; and the usage of logic-oriented approaches. The method for non-disassembly diagnostics based on a logic-oriented approach to assessing the technical condition of the cylinder-piston group of a marine power plant is described, which will be equipped with the necessary mathematical and software-analytical devices. The method should allow indirectly determining the current state of the cylinder-piston group of a marine engine and predicting the development of potentially possible defects and failures.

Sustained energy generation from unusable waste steel through microbial assisted fuel cell systems

In the present study, a novel green energy generation process assisted with Microbial Fuel Cell (MFC) principle for generation of electricity from used or wasted steel is explored. Through a unique approach, unused and other steel waste are recast by simple re-melting with a flexible wide composition for generation of green energy. A microbial-assisted electron transfer derived from the degradation of the steel material is utilized for production of green energy in a microbial galvanic reactor system. A. ferrooxidans acts as biocatalysts, facilitating the oxidization of ferrous ion (Fe2+) to ferric ion (Fe3+). The reaction takes place on the biofilm matrix which results in oxidised reactive zones that endorses further degradation or dissolution of Fe anode. This consequently results in achieving the highest power density as high as 4.92 ± 0.03 mW/cm2 at a current density of 0.01 ± 9 mA/cm2. The total cost of the unusable steel anode and other accessories are roughly estimated to be 7.94 $/m2 and 178.54 $/m2 and the gain from unit power generation is estimated to be 3.79 $/W, assuming continuous operation of 4.92 ± 0.03 mW/cm2. The present study presents a potent methodology/strategy for the generation of sustainable bioenergy from low-cost, unusable steel materials, which cannot be anyway used as such in other battery systems, say iron air cells/batteries.

Opportunities for biogas production from algal biomass

Energy security is a critical global challenge in the transition to sustainable development. Anaerobic digestion (AD) offers a promising renewable energy solution that mitigates environmental impacts. Algae, as biomass feedstock, have shown significant potential for bioenergy production; however, their complex chemical composition poses challenges to the efficiency of the AD process. To address these limitations, various pretreatment methods have been applied to enhance biogas production. In this study, we performed a comprehensive meta‐analysis to evaluate the effects of different pretreatments on methane (CH₄) yields from both microalgae and macroalgae. Our results demonstrate that biological, physical, and combined chemical–physical pretreatments significantly improve CH₄ production in microalgae, with increases of up to 141%, 125%, and 151%, respectively. For macroalgae, physical pretreatments were the most effective, leading to a 129% increase in CH₄ yield. We also estimate that utilizing just 10% of the global algal biomass production (3.6 Mt) could generate over 5.5 TWh y−1 of energy. This potential could be doubled with the application of appropriate pretreatment strategies. These findings highlight the role of algae in advancing renewable energy production and contribute to the growing body of knowledge on optimizing AD processes for cleaner energy generation.

Modeling of Liquefied Natural Gas Cold Power Generation for Access to the Distribution Grid

Cold energy generation is an important part of liquefied natural gas (LNG) cold energy cascade utilization, and existing studies lack a specific descriptive model for LNG cold energy transmission to the AC subgrid. Therefore, this paper proposes a descriptive model for the grid-connected process of cold energy generation at LNG stations. First, the expansion kinetic energy transfer of the intermediate work mass is derived and analyzed in the LNG unipolar Rankine cycle structure, the mathematical relationship between the turbine output mechanical power and the variation in the work mass flow rate and pressure is established, and the variations in the LNG heat exchanger temperature difference, seawater flow rate, and the turbine temperature difference in the cycle system are investigated. Secondly, based on the fifth-order equation of state of the synchronous generator, the expressions of its electromagnetic power, output AC frequency, and voltage were analyzed. Finally, the average equivalent models of the machine-side and grid-side converters are established using a direct-fed grid-connected structure, thus forming a descriptive model of the overall drive process. The ORC model is built in Aspen HYSIS to obtain the time series expression of the torque output of the turbine; based on the ORC output torque, the permanent magnet synchronous generator (PMGSG) as well as the direct-fed grid-connected structure are built in MATLAB/Simulink, and the active power and current outputs of the grid-following-type voltage vector control method and the grid-forming-type power-angle synchronous control method are also verified.

A life cycle assessment of CCU process to produce a nanocomposite from ethanol plant CO2 emission

The use of rubber septa for controlled release of semiochemicals has raised important discussions about their efficiency and environmental impact since they are composed of fossil raw material. The life cycle assessment (LCA) of the synthesis of a nanocomposite type calcium nanocarbonate/Kraft lignin (NC-CN-KL) obtained from CO2 capture aimed to quantify the environmental loads involved in the production process. The synthesis evaluated by the LCA was performed on a laboratory scale, since it is a synthetic route classified as technological readiness level 4 (TRL-4) and is in the study and development stage. The LCA was performed according to the principles of the ISO 14044/2006 series of standards, from 101.854 g of nanocomposite as a functional unit. The limitations of the study arose from its synthesis scale, absence of LCA data on the rubber septa and other nanocomposites. The results obtained in the LCA identified electricity and other energy generation processes as the largest contributors to environmental loads for all environmental impact categories studied and suggest that research should focus on these inputs when choosing the sources used in energy nanocomposite formulation processes. LCAs for this synthesis to obtain NC-CN-KL should be carried out on a pilot scale, and it is expected that this work will contribute to the formulation of the material and decision-making, especially regarding the choice of the energy matrix.

Mechanisms of muscle tissue adaptation in response to the influence of low-dose ionizing radiation

Mechanisms of interaction of ionizing radiation with biological objects are a chain of successive physical and physico-chemical changes, which manifest themselves in the form of excitation, primary and secondary ionization of molecules. Biosynthesis of ATP, which is carried out by a system of oxidation-reduction enzymes localized in the inner membrane of mitochondria - the respiratory chain, belongs to the vital processes that are directly disturbed under the action of ionizing radiation. The high degree of damage to this system is due to the significant radiosensitivity of metal-containing enzymes. The purpose of the work is to study the formation of the adaptive response of muscle tissue of sexually mature rats to the influence of ionizing radiation at a dose of 0.5 Gy. It was concluded that a day after irradiation with the dose of 0.5 Gy, the content of contractile proteins in skeletal muscle decreases slightly. On the 15th day, the content of contractile proteins began to decrease. By the 30th day, the content of contractile proteins decreased by 22.9% for myosin, by more than 11% for actin, and by 7 and 8% for troponin and tropomyosin, respectively, compared to the values of the intact group. A similar picture is observed in the cardiac muscle. Mg2+,Ca2+-ATP-ase activity of actomyosin, starting from the 1st day was shown to be increased in both the skeletal and cardiac muscles, reaching its peak in the cardiac muscle on the 15th day, in contrast to the skeletal muscle, where this indicator reached its peak on the 7th day. The authors conclude that irradiation of sexually mature animals with the dose of 0.5 Gy forms an adaptive response that is accompanied by an increase in Mg2+,Ca2+-ATP-ase activity due to the formation of a strong form of binding between F-actin and myosin, actin monomers go into the typical for actomyosin “turned on stage”, and the myosin heads acquire an ordered orientation in the muscle fiber. According to author’s idea, the data obtained indicated the benefit and reasonability of using in post-radiation dysfunctions complex pharmacological treatment drugs that are able to normalize intracellular homeostasis, eliminate probable acidic changes initiated by radiation exposure, activate the processes of intramuscle energy generation and which have protective properties in relation to the muscular system.

Technical and economic feasibility of hybrid power generation from urban solid waste associated with solar energy, considering the impact of the sale of carbon credits

In the global context of energy transition, from a model dependent on fossil fuels to a low-carbon matrix, hybrid plants, those that combine more than one energy source, prove to be an effective strategy for the increasing of the use of renewable energy sources. Furthermore, the creation of instruments that consider the environmental benefits of energy sources, such as the carbon credit market, which allows the trading of avoided CO2 emissions in the energy generation process, can contribute to the economic viability of low emission sources. In this sense, the objective of this research was to analyze the technical and economic feasibility of hybrid generation from municipal solid waste (MSW) associated with photovoltaic energy, to meet the demands of small city councils in Brazil, considering the commercialization of carbon credits. The economic indicators Net Present Value (NPV), Internal Rate of Return (IRR), Capital Payback Time (PAYBACK) and Levelized Cost of Energy (LCOE) were calculated for four different MSW management scenarios of the Intermunicipal Consortium of Municipalities of the Alto Sapucaí for Sanitary Landfill (CIMASAS, in portuguese), considering different percentages of recycling of dry and organic materials. The generation modalities Self-Production (APE, in portuguese) and Mini and Micro Distributed Generation (MMGD, in portuguese), provided for in the Brazilian regulatory framework, were considered. The results demonstrated that there are technical and economic viability in the proposed hybrid generation in all scenarios analyzed, and as the percentage of recovery of recyclable materials increases, there is an improvement in economic indicators. On average, in the APE modality, the NPV was 49.10 % higher than in MMGD, the IRR was 21.66 % higher, the payback was 51.68 % lower and the LCOE was 40.80 % lower than in MMGD. The inclusion of revenue from the sale of carbon credits promoted an average increase of 47.33 % in NPV, 24.09 % in IRR, and a decrease of, on average, 28.17 % in payback and 40.57 % in LCOE, in APE modality. In the MMGD modality, there was an average increase of 134.99 % in NPV, 39.46 % in IRR, and a decrease of, on average, 38.61 % in payback and 36.98 % in LCOE. These results demonstrate how the incorporation of environmental valuation in energy generation can contribute to increasing the economic attractiveness of hybrid generation from renewable sources.

How torrefaction impacts minimal fluidization velocity from different biomasses and their mixtures

Torrefaction has emerged as a promising technology for optimizing the efficiency of thermochemical processes. However, many challenges still need to be addressed regarding its integration with other processes, such as gasification. This work investigates the potential of torrefaction to transform the properties of biomass mixtures, improving operations in fluidized bed reactors. Experimentally, hazelnut shells and olive pit mixtures were torrefied at 280 °C for 45 min to analyze how torrefaction affected the minimum fluidization velocity as a bulk density function. The results were compared to other biomasses from the literature. Our findings show that torrefaction can stabilize minimum fluidization velocity at 0,45 m/s for mixtures of biomasses with bulk densities below 700 kg/m3 and particle size range (T) of 1,7 < T < 2,36 × 10−3 m. These findings collectively emphasize the potential of torrefaction as an effective technology for utilizing agro-industrial residues in energy generation processes not only for the improvement of conversion efficiency but also for operation stability.

Microencapsulation of Zn-10 mass% Al alloy phase change material via dry synthesis method

Thermal energy storage (TES) via latent heat storage (LHS) using metal alloy phase change material (PCM) offers advantages in medium-to-high temperature operation around 400 °C and high heat storage capacity. Microencapsulation of alloys as PCMs can mitigate issues of reactivity and improve handling properties of PCM. This research presents the novel development of microencapsulated PCM (MEPCM) with a core of Zn–10 mass% Al that operates around 400 °C. The MEPCMs were synthesized using high-speed impact blending (HIB), where fine AlOOH particles mechanically coat the surface of Zn–10 mass% Al alloy microspheres, resulting in robust surface modification. Subsequent heat-oxidation treatment at 800 °C was performed to obtain stable oxide coating. The HIB treatment resulted in an AlOOH coating containing Zn nanoparticles, which the heat-oxidation process transformed into α-Al2O3 and ZnO nanoparticles. The MEPCMs exhibited heat storage densities, including latent and sensible heat (ΔT=200 °C), that were 1.9 times higher than the capacity of solar salt, which is commonly used as a sensible heat storage material. Furthermore, the MEPCMs maintained their shape and latent heat capacity after 300 and 1000 cyclic tests. These superior MEPCM characteristics hold the potential to significantly advance the development of LHS-based TES systems operating around 400 °C, with applications in energy generation and industrial processes.

Financial multidimensional assessment of a green hydrogen generation process via an integrated artificial intelligence-based four-stage fuzzy decision-making model

It is widely accepted that there is an urgent need to make green hydrogen (GH2) projects financially viable to reduce global warming. However, any form of improvements to these GH2 projects lead to substantial cost increase. Due to this cost increase, making many improvements negatively affects the financial profitability of hydrogen projects. This is why there is a need for new advanced financial priority analysis tools so that it is easier to develop GH2 projects globally. Accordingly, the aim of this study is to identify and then define the most important factors affecting GH2 generation projects. To achieve this aim, this work proposes a new fuzzy multi-criteria decision-making model based on artificial intelligence (AI). First, experts are weighted with AI technique. Second, the missing evaluations are filled via a recommender system. Third, criteria weights are calculated by the M-SWARA technique integrated with quantum picture fuzzy rough (QPFR) sets. Finally, GH2 energy generation processes are listed by the QPFR-VIKOR approach. Overall, the main contribution of this study is the generation of a comprehensive AI oriented fuzzy decision-making model to make a detailed evaluation with respect to the financial potential improvements of the GH2 generation projects. The main originality of this model is the consideration of AI to calculate the weights of the criteria. Similarly, another benefit of the proposed model, that increases its superiority against other models, is the completion of missing evaluations by experts thanks to the recommender system. It is concluded that the most important criterion affecting green hydrogen investments is organizational effectiveness.

The performance comparison of machine learning methods for solar PV power prediction

Purpose The energy generation process through photovoltaic (PV) panels is contingent upon uncontrollable variables such as wind patterns, cloud cover, temperatures, solar irradiance intensity and duration of exposure. Fluctuations in these variables can lead to interruptions in power generation and losses in output. This study aims to establish a measurement setup that enables monitoring, tracking and prediction of the generated energy in a PV energy system to ensure overall system security and stability. Toward this goal, data pertaining to the PV energy system is measured and recorded in real-time independently of location. Subsequently, the recorded data is used for power prediction. Design/methodology/approach Data obtained from the experimental setup include voltage and current values of the PV panel, battery and load; temperature readings of the solar panel surface, environment and the battery; and measurements of humidity, pressure and radiation values in the panel’s environment. These data were monitored and recorded in real-time through a computer interface and mobile interface enabling remote access. For prediction purposes, machine learning methods, including the gradient boosting regressor (GBR), support vector machine (SVM) and k-nearest neighbors (k-NN) algorithms, have been selected. The resulting outputs have been interpreted through graphical representations. For the numerical interpretation of the obtained predictive data, performance measurement criteria such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R2) have been used. Findings It has been determined that the most successful prediction model is k-NN, whereas the prediction model with the lowest performance is SVM. According to the accuracy performance comparison conducted on the test data, k-NN exhibits the highest accuracy rate of 82%, whereas the accuracy rate for the GBR algorithm is 80%, and the accuracy rate for the SVM algorithm is 72%. Originality/value The experimental setup used in this study, including the measurement and monitoring apparatus, has been specifically designed for this research. The system is capable of remote monitoring both through a computer interface and a custom-developed mobile application. Measurements were conducted on the Karabük University campus, thereby revealing the energy potential of the Karabük province. This system serves as an exemplary study and can be deployed to any desired location for remote monitoring. Numerous methods and techniques exist for power prediction. In this study, contemporary machine learning techniques, which are pertinent to power prediction, have been used, and their performances are presented comparatively.

Numerical analysis of dual-fuel diesel engines in compression ignition engines: a review

Hydrogen gas is currently used in many daily uses because of its chemical properties that help in combustion and energy generation processes. Hydrogen can be easily used in diesel engines without major design modifications. In additions, its high flame velocity and calorific value make it a desirable option for dual-fuel mode in diesel engines. The trend in developing car engines has become to use alternative fuels to diesel and gasoline. The types of injections currently used, their development technology in diesel engines, and the advantages of using them to develop and raise engine efficiency are discussed. A complete study is being conducted on the use of hydrogen as a dual fuel in diesel engines by changing the engine speed and the amount of hydrogen used in combustion, generating including energy and heat resulting from the use of hydrogen as a dual fuel. This study aims to determine the optimal conditions for using hydrogen as a dual fuel in diesel engines, taking into account both performance and environmental impact. The results of this research could potentially lead to more efficient and sustainable transportation options in the future. Furthermore, the study will also evaluate the emissions produced when using hydrogen as a dual fuel in diesel engines, to ensure that the environmental impact is minimized. By optimizing the use of hydrogen in combustion, this research has the potential to revolutionize the way we power vehicles and reduce our reliance on traditional fossil fuels.

Electricity generation via metal oxide-air moist interaction

Harvesting electricity from ambient moisture and water droplets through the innovative technology of hydrovoltaics has lately gained attraction as it only requires water molecules' intrinsic energy and is applicable in different weather conditions. We present a device prototype for electricity generation from air humidity. The study investigates the complex interplay of water-solid interactions, focusing on band gap differences of n-type and p-type couple metal oxides and their performance in electricity generation. The research develops Moist-Electric Generators (MEGs) using ZnO as an n-type metal oxide with various p-type metal oxides (MnOx, CuO, and CoOx) on carbon paper substrates. Among these, the ZnO/CoOx MEG exhibits superior performance, producing an open circuit voltage of nearly 0.5 V, a current of around 9.5 µA for more than 7 days, with an areal and maximum power density of 1.48 µWcm2- and 4.5 µWcm2- at 95 % RH. Detailed characterization techniques, including XRD, XPS, and SEM, confirm the successful synthesis of ZnO and CoOx electrodes. The study demonstrates the sensitivity of the ZnO/CoOx MEG to changes in relative humidity and investigates its long-term stability. The results highlight the significance of water dissociation and minor redox processes for efficient energy generation, paving the way for advanced applications in renewable energy technologies.

Enhancing solar radiation predictions through COA optimized neural networks and PCA dimensionality reduction

The inherent variability and uncertainty of solar radiation, influenced by factors such as seasons, weather, and cloud cover, pose significant challenges in accurately forecasting energy output from solar power systems. Robust forecasting models that can capture the dynamic behavior of solar radiation are crucial for optimizing energy generation, distribution, and storage processes. This study proposes an integrated approach that combines neural networks, metaheuristic optimization algorithms, and principal component analysis (PCA) for precise solar radiation forecasting. The methodology uses one year of solar radiation data from November 2022 to October 2023 from Vellore district, India. PCA is employed for dimensionality reduction, mitigating the curse of dimensionality. The reduced dataset is segregated into winter, summer, and monsoon subsets to capture distinct seasonal patterns. Neural networks are trained on these seasonal datasets, with their weights optimized by metaheuristic algorithms, including particle swarm optimization (PSO), gray wolf optimization (GWO), whale optimization algorithm (WOA), and coati optimization algorithm (COA). A comprehensive comparative study evaluated the performance of ANN-PSO, ANFIS-GWO, ANN-WOA, ANN-COA, and traditional ANN models. The ANN-COA model, integrating ANN with COA for weight optimization and PCA for dimensionality reduction, achieved the most accurate forecasts, outperforming other approaches. It attained optimal RMSE of 12.06 for winter, 6.72 for summer, and 9.49 for monsoon season, MAE of 6.66, 5.50, and 6.25, MAPE of 3.58 %, 1.68 %, and 2.31 %, and R2 of 0.973, 0.982, and 0.985 for winter, summer, and monsoon seasons respectively, demonstrating its robustness and suitability for time-series solar radiation forecasting.

TURNING FOOD WASTE INTO GREEN ENERGY A BIO-GAS REVOLUTION

The Bio Gas Revolution stands as a beacon of sustainable innovation, revolutionizing waste management by converting discarded food into valuable green energy through biogas production. This abstract encapsulates the multifaceted approach of this revolution, encompassing waste food diversion strategies, cutting-edge anaerobic digestion technologies, renewable energy generation processes, circular economy principles, and the ensuing environmental-economic sustainability benefits. It underscores the transformative potential of harnessing waste food as a fundamental resource for renewable energy, heralding a new era of environmental preservation, resource optimization, and economic viability in the global energy land scape. It highlights the transformative potential of utilizing waste food as a critical resource for renewable energy, leading to significant environmental benefits, resource efficiency gains, and economic opportunities.. It emphasizes the transformative potential of harnessing waste food as a valuable resource for renewable energy production, contributing significantly to environmental preservation, resource efficiency, and economic viability” Key Words: Food Waste Management, Green Energy, Biogas Production, Sustainable Waste Disposal Submit Research Paper

The metabolic characteristics and changes of chondrocytes in vivo and in vitro in osteoarthritis

Osteoarthritis (OA) is an intricate pathological condition that primarily affects the entire synovial joint, especially the hip, hand, and knee joints. This results in inflammation in the synovium and osteochondral injuries, ultimately causing functional limitations and joint dysfunction. The key mechanism responsible for maintaining articular cartilage function is chondrocyte metabolism, which involves energy generation through glycolysis, oxidative phosphorylation, and other metabolic pathways. Some studies have shown that chondrocytes in OA exhibit increased glycolytic activity, leading to elevated lactate production and decreased cartilage matrix synthesis. In OA cartilage, chondrocytes display alterations in mitochondrial activity, such as decreased ATP generation and increased oxidative stress, which can contribute to cartilage deterioration. Chondrocyte metabolism also involves anabolic processes for extracellular matrix substrate production and energy generation. During OA, chondrocytes undergo considerable metabolic changes in different aspects, leading to articular cartilage homeostasis deterioration. Numerous studies have been carried out to provide tangible therapies for OA by using various models in vivo and in vitro targeting chondrocyte metabolism, although there are still certain limitations. With growing evidence indicating the essential role of chondrocyte metabolism in disease etiology, this literature review explores the metabolic characteristics and changes of chondrocytes in the presence of OA, both in vivo and in vitro. To provide insight into the complex metabolic reprogramming crucial in chondrocytes during OA progression, we investigate the dynamic interaction between metabolic pathways, such as glycolysis, lipid metabolism, and mitochondrial function. In addition, this review highlights prospective future research directions for novel approaches to diagnosis and treatment. Adopting a multifaceted strategy, our review aims to offer a comprehensive understanding of the metabolic intricacies within chondrocytes in OA, with the ultimate goal of identifying therapeutic targets capable of modulating chondrocyte metabolism for the treatment of OA.