• Energy Research

This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R2), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R2 obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R2 is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.

The effect of the addition of different proportions of silver (Ag) nanoparticles and alcohols in milk scum oil methyl ester on the performance of engine and emission are studied. B20 blend is added with 5% of ethanol, n-butanol, and iso-butanol as ternary additives for the experimental analysis from no load to full load. Furthermore, at a fixed load, operating conditions such as injection pressure (12 and 15 bar) and injection timing (23° and 26°) are varied without and with the addition of 0.8 vol% of Ag (silver) nanoparticles to the fuel blends. Also, the concentrations of Ag nanoparticles are increased from 0.2 to 1 vol% and comparisons are made with diesel and B60 blend. Mathematical models are developed for selected features of engine performance which fits with the experimental values for the purpose of optimization using the Dragon fly algorithm (DA) by considering these models as the objective functions. The concentration of nanoparticles lowers the BSFC significantly and helps in reducing the emission with an increased percentage. Using full biodiesel, 16.6% reduction in BTE was obtained, while use of alcohols prevented this reduction approximately by 5%. A highest of 4.6% improvement was obtained with the addition of Ag nanoparticles. 4.5% reduction in HC and 13% in NOx emission using nanoparticles are obtained. The DA algorithm provided the same optimized value at the end of 30 iterations in different cycles of execution. Nanoparticle addition and use of pressure in the range of 20 bar gives the lowest emission from the engine.

Abstract There is a demand for economic, affordable cost manufacturing and Improvement in power conversion energy of DSSC has made most researches finding for alternative ways to optimize each components of the cell to improve its efficiency. This paper reviews alternative ways to manufacture and synthesizing purpose for DSSC. The DSSC is one of the example for renewable energy power generation. This paper reviews the manufacturing or method by utilizing the FTO coated glass (Dr. Blade method) for applying the paste on glass substrate for effective sunlight harvesting.Photoanode prepared by ZnO powder, Counter electrode prepared by the combination of Electrolyte (Ki + I2 + Acetonitrile). Scanning electron microscopy revealed a material with an crust and trough structures like morphology. With X-ray diffraction analysis, With the help of thermal imager found the emittance and temperature on the DSSC surface , X-ray Florescence’s used for finding components present in the semi conductiveoxide. We can measure the Open circuit voltage is 35.5 and Short circuit current 0.024 by connecting in series with DSSC by using Multimeter, fill factor is 1.477 and efficiency is 0.164.

2D nanomaterials‐based heat transfer fluids show excellent thermal properties due to their large specific surface area; hence, they find large‐scale applications in automobile industries and cooling processes. Therefore, it is very essential to study the environmental and economic aspects of these 2D nanomaterial‐based nanofluids. In this review, we have discussed the environmental impact of 2D nanomaterial‐based heat transfer nanofluids under various conditions. The environmental impact analysis of these materials has shown excellent capability in reducing the energy consumption for heat transfer operations. Moreover, the possibility of nanomaterials and base fluid recovery makes it a sustainable alternative. In addition, health risk assessment on humans, cytotoxicity, and life cycle analysis have also been explored. The price‐performance index has been successfully used to study the economic impact of 2D nanomaterial‐based heat transfer fluids. The overall economic impact of 2D nanomaterial‐based heat transfer nanofluids provides an optimistic perspective over conventional heat transfer fluids. Moreover, graphene production, market trend, and commercialization obstacles were also discussed.