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    Collective influence and optimization of 1-hexanol, fuel injection timing, and EGR to control toxic emissions from a light-duty agricultural diesel engine fueled with diesel/waste cooking oil methyl ester blends
    (Institution of Chemical Engineers, 2023) De Poures, Melvin Victor; Dillikannan, Damodharan; Kaliyaperumal, Gopal; Thanikodi, Sathish; Ağbulut, Ümit; Hoang, Anh Tuan; Mahmoud, Z.
    This study attempts to utilize a ternary blend comprising diesel, biodiesel, and 1-hexanol in a direct injection (DI) diesel engine. A response surface methodology (RSM) based optimization with the full factorial experimental design was used to optimize the fuel injection timing and exhaust gas recirculation (EGR) with an objective to maximize the performance of the engine with minimum emissions. Three injection timings and three EGR rates were used. Multiple regression models developed using RSM for the responses were found to be statistically significant. Interactive effects between injection timing and EGR on responses for the blends were studied. From a desirability approach, a HX20 blend (diesel 50 v/v% + biodiesel 30 v/v% + 1-hexanol 20 v/v%) injected at lesser fuel injection timing and EGR rate delivered optimum emission and performance characteristics. Confirmatory tests validated the models to be adequate. With reference to diesel, at optimum conditions, there was a significant reduction in nitrogen oxides (NOx) emission with a marginal increase in smoke, hydrocarbon (HC) and carbon monoxide (CO) emissions. Also, it was found that there was minimal loss in brake thermal efficiency (BTE) of the engine. With respect to waste cooking oil methyl ester operation, the blend reduced nitrogen oxides (NOx), smoke, carbon monoxide (CO) and hydrocarbon (HC) emissions significantly with marginal loss in BTE. © 2023 The Institution of Chemical Engineers
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    Enhancement of R600a vapour compression refrigeration system with MWCNT/TiO2 hybrid nano lubricants for net zero emissions building
    (Elsevier, 2023) Senthilkumar, A.; Prabhu, L.; Sathish, T.; Saravanan, R.; Jeyaseelan, G. Antony Casmir; Agbulutc, Umit; Mahmoud, Z.
    Net zero emissions building is widely investigated with great environmental care. In the case of refrigeration selection for net zero emissions building (NZEB), the ozone depletion potential is the primary criterion to choose the refrigerant. For achieving the NZEB, the R600a was preferred as it possesses the potential for global warming lower and zero potential for ozone depletion. This paper aims to amplify the coefficient of performance by utilizing MWCNT/TiO2 hybrid Nano lubricants in the R600a vapour compression refrigeration system. As numerous factors and equations are involved in the study and prediction of the Coefficient of Performance in vapour compression refrigeration systems which is comparatively complex and takes more time for promoting the development of precise prediction and results. Artificial neural networks (ANN) and adaptive neuro-fuzzy interface systems (ANFIS) are the two techniques mainly concentrated in this study which were not properly implemented previously. By using the ANFIS technique enhanced cooling effect of 200 W with a 50 % increment was obtained with 0.4 g/L of MWCNT/TiO2 hybrid nano lubricants which is better in comparison with ANN and experimental results. The minimum energy utilization of 90 W was obtained with the ANFIS technique. This method also predicted the enhanced COP of 3.7 with a 32 % increase in comparison to the ANN prediction method. When compared to the ANN prediction model, the ANFIS model's estimated least training error value. The results indicate that when compared to ANN prediction the ANFIS predicted values produced results that were more accurate and were the proper approach for predicting COP parameters and consumed 35 % less energy.