• Energy Research

Background: Thermal spray coatings have emerged as a pivotal technology in materials engineering, primarily for augmenting the characteristics related to wear and tribology of metallic substrates. Methods: This study aims to develop into applying High-Velocity Oxygen Fuel (HVOF) thermalsprayed WC-Co nanocoatings on Titanium Grade-5 alloy (Ti64). The coating process, utilizing nano-sized WC-Co powder, undergoes systematic optimization of HVOF parameters, encompassing the flow rate of carrier gas, powder feed rate, and nozzle distance. Experimental assessments via Pin-on-Disc (PoD) tests encompass Loss of Wear (WL), Friction Coefficient (CoF), and Frictional Force (FF). Later, an exhaustive optimization of responses is conducted using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method and the golden jack optimization algorithm (GJOA) Results: Outcomes show a substantial increase in WL, CoF, and FF with a rise in the carrier gas and powder feed rate. However, with increasing spraying distance of powder, the WL, CoF, and FF tend to lower due to higher bonding, which leads to increased wear resistance. The ideal parametric settings achieved from TOPSIS and GJOA are 245 mm of spray distance, 30 gpm rate of powder feed, and 11 lpm of carrier gas flow rate. The powder feed rate contributes 88.99% to the control action, as seen from ANOVA. Conclusion: The confirmation experiment presents that the WL, CoF, and FF output responses are 42.33, 27.97, and 9.38% less than the mean of experimental data. These results highlight the HVOF process in spraying WC-Co nanocoatings to fortify the durability and performance of Ti64 alloy that can be patented for diverse engineering applications.

The consumption of a significant quantity of energy in buildings has been linked to the emergence of environmental problems that can have unfavourable effects on people. The prediction of energy consumption is widely regarded as an effective method for the conservation of energy and the improvement of decision-making processes for the purpose of lowering energy use. When it comes to the generation of positive results in prediction tasks, the Machine Learning (ML) technique can be considered the most appropriate and applicable strategy. This article presents a Modified Wild Horse Optimization with Deep Learning approach for Energy Consumption Prediction (MWHODL-ECP) model in residential buildings. The MWHODL-ECP method that has been provided places an emphasis on providing an up-to-date and precise forecast of the amount of energy that residential buildings consume. The MWHODL-ECP algorithm goes through several phases of data preprocessing in order to achieve this goal. These steps include merging and cleaning the data, converting and normalising the data, and converting the data. A model known as deep belief network (DBN) is used here for the purpose of predicting energy consumption. In the end, the MWHO algorithm is utilised for the hyperparameter tuning procedure. The results of the experiments demonstrated that the MWHODL-ECP approach is superior to other existing DL models in terms of its performance. The MWHODL-ECP model has improved its performance, with effective prediction results of MSE-1.10, RMSE-1.05, MAE-0.41, R-squared-96.28, and Training time-1.23.

In the current study, anaerobic digestion method efficiency on biogas production and chemical oxygen demand (COD) degradation was assessed through a sequence of laboratory-scale batch experimentations to compute the role of chosen process parameters, viz., solid concentration (5–15%), pH (5–9), temperature (30–60 °C), and co-digestion (0–40% of poultry manure). Biogas production and COD degradation were significantly dependent on the selected process parameters with independent conditions to accomplish active performance of the process. Central composite design (CCD)-based response surface methodology (RSM) was adopted for evaluation and optimizing of the combined performance of system considering two responses. Among various combinations, it was observed that solid concentration of 7.38%, pH value as 7, temperature at 48.43 °C, and co-digestion as 29% produce biogas of 6344 ml and COD degradation as 38%. Confirmation experiment performed shows a deviation of 4.93% maximum between the predicted and experimental results.

Energy management in power grids becomes essential to reduce the cost for the consumer and improve the power supply reliability. The microgrid is a vital part of the smart grid and it requires intelligent power management approach for effective functioning. Presently, delivering demand load and sustaining energy are two major challenges that exist in the power system. To resolve these problems, short-term load forecasting (STLF) models have been presented as an effective management and energy supply mode in power systems. The recently developed deep learning (DL) and machine learning (ML) models can be employed for accurate STLF in microgrids. In this view, this study presents an intelligent wild geese algorithm with deep learning driven short term load forecasting (IWGADL-STLF) model for sustainable energy management in microgrids. The proposed IWGADL-STLF model intends to accurately and rapidly predict the STLF in the microgrids. To accomplish this, the IWGADL-STLF model uses attention based Bi-directional long short term memory (ABiLSTM) model which involves the input parameters as formation of household and commercial load profiles with commercial load profile of the microgrid as output. The proposed IWGADL-STLF model identifies the behavioural patterns of parameters and models the behaviour in short time period for effective prediction process. Since hyper -parameters play a vital role in the DL models, in this study, WGA is applied as a hyperparameter optimizer of the ABiLSTM model. The IWGADL-STLF approach has shown effective results with low MAE, MAPE, and R2 values. A comprehensive experimental analysis reported the enhanced performance of the presented model over the other existing approaches under several aspects.

Solar distillation converts salt water into drinkable water, requiring minimal maintenance and energy-saving. However, the desalination process has drawbacks because the system's slow evaporation and condensation rate leads to low freshwater output. Consequently, this method is not widely utilized due to its limited productivity. To address this issue, the study's primary aim was to enhance the productivity of the single-slope solar still. This was achieved by altering the water depth from 3 cm to 6 cm and incorporating an external reflector. The experiments were conducted in Coimbatore, Tamil Nadu, India (11.0168° N, 76.9558° E), with a condensing cover inclined at 11 degrees. The research occurred on varying days between October and November 2023, with water depths ranging from 3 to 6 cm. A comprehensive analysis investigated the influence of different factors on daily production, such as ambient temperature, solar intensity, and inner and outer glass temperatures. The experimental results indicate that the solar still with a single basin, operating at a water depth of 3 cm, achieved the highest water productivity (2.68 L/day) and displayed the best efficiency (30.52%) compared to 4, 5, and 6cm depths. Furthermore, incorporating an external reflector into the solar system still demonstrated a notable elevation in temperature, resulting in a significant boost in water productivity of 3.085 liters per day. This improvement also led to an increase in efficiency of 35.1%.

Introduction: Two-phase hybrid mode thermal interface materials were created and characterized for mechanical properties, thermal conductivity, and wear behaviour. Therefore, the ultimate goal of this current research was to use alkali-treated glass fibre and other allotropes to produce high-performance two-phase thermal interface materials that can be patented for engineering applications. Methods: Three different polymer composites were prepared to contain 20 vol.% alkalies (NaOH) treated e-glass fibre (E) and epoxy as a matrix with varying proportions of multi-walled carbon nanotube (MWCNT), graphene (G), copper oxide (C). The one-phase material contained epoxy+20%e-glass+1%MWCNT (EMGC1), the two-phase hybrid composite contained epoxy+20%e-glass+1%MWCNT+1%graphene+1%CuO (EMGC2), and two-phase material contained epoxy+20%e-glass+1%graphene+1%CuO (EMGC3). Vacuum bagging method was used for fabricating the composites. Results: The higher thermal conductivity observed was 0.3466 W/mK for EMGC2, the alkalitreated glass fibre/hybrid mode nanofillers epoxy matrix composite was mechanically tougher than the other two composites (EMGC1 & EMGC3). Scanning electron microscopy analysis revealed the fine filler dispersion and homogenous interaction with the glass fibre/epoxy resin composite of the upper and lower zone, which also revealed the defective zone, fibre elongation, fibre/filler breakages, and filler leached surfaces. Conclusion: Finally, it was concluded that the hybrid mode two-phased structure EMGC2 epoxy matrix composite replicated the maximum thermal conductivity, mechanical properties, and wear properties of the other two specimens.

In this study, biogas production through anaerobic digestion of food waste was investigated by applying four factor-four level Taguchi design under experimental conditions such as solid concentration (5%–12.5% TS), pH (6–9), temperature (30–60 °C), and co-digestion of poultry waste (10%–40%). Multi-objective techniques such as grey relational analysis (GRA) and multi-variate principal component analysis (PCA) were used to determine the optimal level of process parameters. The interactive effect of process conditions on the biogas yield was studied with multi-response performance index. The obtained results were analyzed by analysis of variance and the percentage contributions of each parameter were determined. Optimum conditions for maximizing the biogas yield and volatile solid removal efficiency were determined using hybrid GRA-PCA technique and it was found to be solid concentration of 7.5% TS, pH of 7, temperature of 50 °C, and co-digestion of 30%. Supremacy of the chosen hybrid technique is confirmed from the confirmation experimental output.

Abstract In a practical scenario, only a modest amount of distilled water can be generated each day by a basic solar still with a single basin. Fin-type solar ponds, fin-type solar stills, and integrated fin-type solar stills with finned ponds are investigated. The theoretic performance and experimental studies on the proposed systems were carried out in Pongalur near Tirupur (10.9729° N, 77.3698° E), a region with a latitude of 10° north. Single basin solar still (SBSS), single basin solar still with fin, single basin solar still with pond, single basin solar still with finned pond, and integrated single basin fin-type solar still with a finned solar pond were developed. Adding fins to the small solar pond enhanced the thermal performance of SBSS, by increasing the daily water collection. The pace at which heat is transmitted from the basin to water has risen due to the fins. According to this study, the amount of water collected by single basin solar still with fin, single basin solar still with finned pond, and integrated single basin solar still with fins and finned pond grew by 46, 48, and 52% for each of these systems.