Experimental investigation on dispersing graphene-oxide in biodiesel/diesel/ higher alcohol blends on diesel engine using response surface methodology

Lower alcohols have long been the figureheads of diesel/biodiesel additives in characterizing renewable fuels. Next-generation alcohol like n-octanol occupied the reified position due to their better fuel properties. In this paper, combustion, performance and, emission of different graphene-oxide nanoparticles (nanoGO) added jatropha biodiesel, n-octanol and petrodiesel blends are investigated in a 4-stroke DI diesel engine. This article also aims to optimize the engine inputs accountable for better performance and emission characteristics of a diesel engine running with nanoGO dispersed biodiesel/diesel/higher alcohol blends. Full Factorial Design-based Response Surface Methodology (RSM) is utilized to model the experiments using Design-Expert software to optimize engine responses. Validation of the developed model is carried out using sophisticated error and performance metrics, namely, TheilU2, Kling-Gupta Efficiency (K-G Eff), and Nash-Sutcliffe coefficient of efficiency (N-S Eff) along with the conventional statistical database. The model optimized engine inputs of 3.898% n-Octanol, and 49.772 ppm nanoGO at 99.2% load with a desirability index of 0.997 as the optimum engine parameters. The experimental validation revealed that the model optimized blend at full load witnessed a reduction of 15.6% CO, 21.78% HC.u, and 3.26% NOx emission compared to petrodiesel. However, a slight increase in brake specific energy consumption (2.95%) is also recorded because of the lower heating value of the blend.

An experimental investigation of performance and emission of thumba biodiesel using butanol as an additive in an IDI CI engine and analysis of results using multi-objective fuzzy-based genetic algorithm

The present work studies the effect of butanol in thumba (Citrullus colocynthis) biodiesel in an IDI CI engine at varying percentages of 5 and 10% in 15 and 10% thumba biodiesel respectively with 80% diesel in each blend. Another blend was introduced with 80% diesel and 20% biodiesel without any additive. The experiment was conducted in a single cylinder four-stroke IDI CI engine at 1500 rpm varying from 25% to full-load (100%) conditions. The results showed diesel with less bio diesel and higher butanol in percentage gives good performance and emission compared to diesel at higher loads. Blend containing 10% bio diesel, 10% butanol, and 80% diesel (D80B10Bu10) showed higher cylinder pressure, heat release rate, BThE, and less NO_x. Biodiesels gave less UHC, CO emissions. In this work, multi-objective fuzzy-based genetic algorithm was introduced for the best fit result. Four parameters were used for optimization (BSFC, BThE, CO, NO_x). The result from genetic algorithm was taken for validation and the optimized result was found adequate after validation.

Experimental analysis of performance and emission on DI diesel engine fueled with diesel-palm kernel methyl ester-triacetin blends: a Taguchi fuzzy-based optimization

The energy situation and the concerns about global warming nowadays have ignited research interest in non-conventional and alternative fuel resources to decrease the emission and the continuous dependency on fossil fuels, particularly for various sectors like power generation, transportation, and agriculture. In the present work, the research is focused on evaluating the performance, emission characteristics, and combustion of biodiesel such as palm kernel methyl ester with the addition of diesel additive "triacetin" in it. A timed manifold injection (TMI) system was taken up to examine the influence of durations of several blends induced on the emission and performance characteristics as compared to normal diesel operation. This experimental study shows better performance and releases less emission as compared with mineral diesel and in turn, indicates that high performance and low emission is promising in PKME-triacetin fuel operation. This analysis also attempts to describe the application of the fuzzy logic-based Taguchi analysis to optimize the emission and performance parameters.