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In this review, the potential role of machine learning in sustainable energy and SGDs is analyzed; energy forecasting, planning, renewable energy production and storage are covered and an extensive perspective on the future role of ML is provided.

Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

CaO captures CO2 from pyrolysis, forming CaCO3 that activates biochar via decomposition.

Abstract This paper investigates the forced convection of alumina-water nanofluids within helical tubes, maintaining a constant wall temperature and assuming thermal equilibrium between the nanoparticles and the base fluid. The nanofluid model incorporates the effects of alumina (Al2O3) nanoparticle volume fraction, diameter, and temperature on thermophysical properties. The governing equations are solved using the Forward-Time Central-Space Finite Volume method in conjunction with the simple algorithm. Numerical results are validated against experimental data, demonstrating high accuracy. The study explores the effects of pitch size, curvature ratio, nanoparticle volume fraction, nanoparticle diameter, and Reynolds number on velocity contours, temperature profiles, secondary flow, thermophysical properties, friction coefficient, and heat transfer rate. Additionally, the figure of merit evaluates the impact of these parameters on the thermal performance of the system. The results indicate that an increase in Reynolds number and nanoparticle diameter negatively affects thermal performance, while higher nanoparticle volume fraction, curvature ratio, and pitch size enhance it. Furthermore, incorporating nanoparticles in straight tubes proves to be more advantageous compared to helical tubes. This study tested volumetric ratios of 1%, 2%, and 4%, which resulted in increases in heat transfer coefficients of 21%, 32%, and 43%, respectively, compared to pure water under similar conditions, such as Reynolds number and coil pitch.

Abstract Coal-fired power plants are commonly used as adjustable power sources to complement the fluctuating output of wind and solar energy. The investigation is required to determine the flexible peak-shaving capabilities of coal-fired boilers. A modified scheme for a lignite-fired power plant to further improve the primary air temperature using the outlet steam from the low-temperature reheater is studied while increasing the inlet flue gas temperature of the air preheater is considered the conventional scheme. Thermodynamic models of the power plant are constructed using ebsilon software. The operational characteristics of both schemes are compared under 30% turbine heat acceptance (THA)–100%THA conditions and the economic performance of the modified scheme is also evaluated. Results indicate that the modified scheme exhibits superior thermodynamic and economic performances compared to the conventional scheme. The disparity in power generation efficiency between the conventional and modified schemes reaches a maximum of 0.23 percentage points under 75%THA conditions. The net present value of the modified scheme amounts to 4.51 million dollars over the power plant lifespan of 30 years. The modified scheme allows the conventional denitrification catalyst to maintain an optimal temperature range even under 30%THA conditions, resulting in a power generation efficiency only 4.8 percentage points lower than that under 100%THA conditions, thus demonstrating remarkable operational flexibility. This study presents an efficient, cost-effective, and adaptable approach for lignite-fired power plants.

The major aim of this article is to find the analytical solution of the physics-based model for lithium-ion batteries. Because the full-order pseudo two-dimensional model, comprised of a nonlinear algebraic-differential system, has a high computational cost, single particle model (SPM) is considered in this work to lessen the computational burden. First, the governing partial differential equations of the SPM are simplified with the volume average method along with polynomial approximations, and then an exact analytical solution of the simplified differential system is computed with the integrating factor method. The accuracy and reliability of the proposed analytical solution are accessed by comparing discharge curves simulation data with PyBAMM simulations in different operating conditions. It is observed that the error varies between 10 mV–25 mV for low to higher C-rate and the maximum error occurs at the middle and terminal points of the discharge cycling step in most cases. Finally, discharge curves are simulated with the help of the proposed analytical solution for the NMC battery, and then simulation results are compared with the experimental data of the NMC 50 Ah commercial battery. It is observed that the error in simulations is about 1% relative to terminal voltage.

Data-driven methods are widely claimed to be the most promising candidates for online battery health assessment estimation.

Electrodeposition of metallic zinc (Zn) from reverse micellar solutions of cetyltrimethylammonium bromide (CTAB) of different compositions was successfully performed using constant potential electrolysis on copper (Cu) substrate. Electrochemical behavior of ZnSO4 was investigated in reverse micellar solutions of CTAB on a glassy carbon (GC) electrode by cyclic voltammetric technique and the electroreduction of Zn(II) to Zn(0) was suggested to be an electrochemically irreversible diffusion controlled process. Morphology and microstructure of the electrodeposited Zn films onto a Cu substrate were examined by scanning electron microscopy, while elemental characterizations were carried out by energy dispersive X-ray spectroscopic method and X-ray diffraction technique. Electrodeposition of Zn from reverse micellar solutions occurred with definite homogeneous shapes whereas, inhomogeneous/random gross Zn deposition was obtained from aqueous system. Moreover, morphology of the electrodeposited Zn films obtained from reverse micellar solutions was found to be varied with the variation of composition of reverse micellar solutions of CTAB. The corrosion protective behavior of the electrodeposited Zn films evaluated through electrochemical impedance spectroscopy and potentiodynamic polarization techniques with 3.5 wt% NaCl solution as corrosion media showed that Zn deposits obtained from reverse micellar solutions exhibited better corrosion protection behavior compared to that obtained from aqueous solution.

Semiconductors with nanoscale dimensions are indispensable vectors for devising modern-age electronics-enabled technologies. Meeting the rising technological demand of the globally expanding population, while limiting the cost to the ecosystem, necessitates the sustainable development of green semiconductors at the nanoscale. This perspective highlights the state-of-the-art green nano-semiconductors, including metal oxides, organic materials, and hybrid nanosystems, with three key challenges: scalability, stability, and susceptibility. It also discusses alternate solutions integrating modern technologies like artificial intelligence to establish these green nano-semiconductors as a sustainable frontier to revolutionize multidimensional applications such as sensors, medicines, electronics, energy systems, and environmental remediation while minimizing ecological footprints.

Abstract This study investigates the performance of two solar air heater designs, S1-SAH and S2-SAH, through experimental analysis of temperature, energy gain and loss, efficiency, and Thermal-Hydraulic Performance Parameter (THPP) across mass flow rates ranging from 0.013 to 0.061 kg/s in hot climatic conditions of R.E.C Banda. The S2-SAH consistently outperformed the conventional S1-SAH, achieving a maximum energy efficiency of 79.3% at 0.051 kg/s and a peak exergy efficiency of 4.5% at 0.013 kg/s. Additionally, the highest THPP of 2.0 was recorded at 0.051 kg/s. The results highlight the superior performance and enhanced efficiency of the S2-SAH, showcasing the effectiveness of its design for improved heat transfer.