In this paper, a combined approach of Principal Component Analysis (PCA)-based Extreme Learning Machine (ELM) for boiler output forecasting in a thermal power plant is presented. The input used for this prediction model is taken from the boiler unit of the Yermarus Thermal Power Station (YTPS), India. Calculation of the accurate electrical output of a boiler in an operating system requires the knowledge of hundreds of operating parameters. The dimensionality of the input dataset is reduced by applying principal component analysis using IBM@SPSS Software. In the process of principal component analysis, a dataset of 232 parameters is standardized into 16 principal components. The total dataset collected is divided into training and testing datasets. The extreme learning machine is designed for various activation functions and the number of neurons. Sigmoid and hyperbolic tangent activation functions are studied here. Its generalization performance is examined in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square (RMSE), and Mean Absolute Percentage Error (MAPE). ELM and PCA–ELM are compared. In both the ELM and PCA–ELM models, when the extreme learning machine was designed with a sigmoid activation function with 100 nodes in the hidden layer, RMSE was 5.026 and 4.730, respectively. Therefore, the developed combined approach of PCA–ELM proved as a promising technique in forecasting with reduced errors and reduced time.

In this manuscript, a hybrid system depending on the optimal location of electric vehicle parking lots (PL) and capacitors under voltage profile care and power loss is proposed. The proposed hybrid scheme is the joint execution of both the atomic orbital search (AOS) and arithmetic optimization algorithm (AOA). Commonly it is called the AOSAOA technique. In the paper, the allocation of the parking lot and capacitor is introduced to congestion management with reactive power compensation. To optimally regulate that parking lot size, the AOSAOA technique is adopted. Furthermore, parking lot and capacitor allocation are introduced to congestion management and reactive power compensation. With this proper control, the perfect sitting of capacitor and EV parking lots under the grid, including the deterioration of real and reactive power loss and voltage profiles are optimally chosen. Furthermore, the implementation of the proposed AOSAOA model is developed by the MATLAB/Simulink platform, and the efficiency of the proposed model is likened to other techniques.

This article presents a study on the performance characteristics of a Francis turbine operating with various guide vane openings to determine the best operating point based on unit quantities. The guide vane openings were specified based on the width between the vanes at their exit, i.e., 10 mm, 13 mm, 16 mm, and 19 mm. The performance characteristic curves of the Francis turbine—head versus speed, torque versus speed, discharge versus speed, and efficiency versus speed—were obtained at various input power and guide vane openings. From these data, unit curves were plotted and the corresponding best efficiency points were obtained. The highest efficiency of 50.25% was obtained at a guide vane opening of 19 mm. The values of head, discharge, speed, and output power at BEP were 7.84 m, 13.55 lps, 1250 rpm, and 524 W, respectively.

The most recent developments in Thermal Barrier Coating (TBC) relate to engine performance, manufacturing and other related challenges. TBC on the piston crown and valves to enhance engine characteristics while using diesel and Mahua Methyl Ester (MME) as a petroleum fuel has a great sustainable development. For this utility, a Direct Injection (DI) conventional diesel engine was renewed to an LHR engine by applying 0.5 mm thickness of 3Al₂O₃-2SiO₂ (as TBC) onto the piston crown and valves. The MME is used in the LHR (Low Heat Rejection) engine. For examination, the fuel injector pressure is set at 200 bar. Compared to a standard DI diesel engine, the results demonstrate that the application of TBC boosts brake thermal efficiency to 13.65% at 25% load. The LHR engine’s SFC and BTE significantly improved at full load while using MME fuel. The lower temperature of exhaust gases is achieved by combining MME and diesel fuels with TBC. It was observed that both MME with and without TBC significantly reduced the smoke density. In addition, it was exposed that using MME fuel with TBC very slightly reduced carbon monoxide emissions under all loads. It was also shown that MME with TBC significantly reduced environmental hydrocarbon emissions at all loads.