The focus of this study is on the potential of using oils extracted from food waste that ended up in landfills. These waste oils were tested to see how they affected performance and emissions in diesel engines. The study's results are analysed and compared with models created using intelligent hybrid prediction approaches including adaptive neuro-fuzzy inference system (ANFIS), response surface methodology (RSM) - genetic algorithm (GA), and ANFIS - non sorting genetic algorithm (NSGA-II). The analysis takes into account engine load, blend percentage, nanoadditive concentration, and injection pressure, and the desired responses are the thermal efficiency and specific energy consumption of the brakes, as well as the concentrations of carbon monoxide, unburned hydrocarbon, and oxides of nitrogen. Root-mean-square error (RMSE) and the coefficient of determination (R2) were used to assess the predictive power of the model (R2). Comparitevely to AI and the RSM-GA model, the results provided by ANFIS-NSGA-II are superior. This is because it achieved a pareto optimum front of 24.45 kW, 2.76, 159.54 ppm, 4.68 ppm, and 0.020243% for BTE, BSEC, NOX, UBHC, and CO. Combining the precision of ANFIS's prediction with the efficiency of NSGA-optimization II's gives a reliable and thorough evaluation of the engine's settings.

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The primary objective of this study is to investigate the solar-powered combined-cycles system for converting the available solar energy to its truest potential and for generating electrical power. This combined-cycles system consists of a solar power tower, steam turbine cycle, and organic Rankine cycle. The study focuses on recovering the waste heat that is obtained from the exit of a steam turbine and using it to operate the Rankine cycle with refrigerants R-113, R-11, and R-1233zd. The analysis also predicts the effects of solar irradiance for a mass flow rate of molten salt and steam, turbine inlet pressure, and turbine inlet temperature on first and second law efficiencies in the combined-cycles system. The novel concept of uncertainty analysis is also introduced in this work in order to provide precise accurate results and remove all errors, which are found to be in the desired range of 3.81%. The results also show that as the direct normal irradiation (DNI) increases from 600 W/m 2 to 1000 W/m 2 , first law efficiency is obtained in the range of 32.31% to 37.99% and second law efficiency from 24.14% to 25.51% after employing the organic Rankine cycle (ORC) system. Further, the results indicate the maximum exergy destruction that occurs in the central receiver to be around 42%, in the heliostat to be 31%, in the steam generator to be 10%, and in the heat exchanger to be 3.6%.