Experimental based comparative exergy analysis of a spark‐ignition Honda GX270 Genset engine fueled with LPG and syngas

Abstract The present study investigates three different fuels such as gasoline, liquefied petroleum gas (LPG), and syngas in spark‐ignition Honda GX270 Genset engine under wide‐open throttle position on its performance, combustion characteristic as well as availability analysis. The results showed that when the engine operated with gasoline fuel, the brake thermal efficiency was higher than that of LPG and syngas by 6.2% and 7.4%, respectively, throughout the engine load condition. Brake‐specific fuel consumption of the engine with syngas (660 g/kW h) and LPG fuel (812 g/kW h) was higher than that of the gasoline fuel (510 g/kW h) at the 4.5 kW of engine load. The engine emission results showed syngas operation caused a significant reduction in NOx by 58.4%, CO by 16.5%, HC by 23.2% compared to gasoline fuel at peak load conditions. On the other hand, exergy analysis concludes the exergy efficiency for all the test fuels increases with an increase in engine load due to a high rise in shaft output. At a 4.5 kW power output, the exergy efficiency of the engine was improved to 46.45% from 45.62% and 29.73% with syngas, gasoline, and LPG, respectively. The maximum exhaust gas availability has been observed as 24.51% of availability input for syngas at 100% load condition

Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel

While retaining environmental friendliness, robust modelling and enhancing spark ignition engine efficacy can be done using improved innovative fuel and unconventional robust hybrid tools. This study is the first to employ Al techniques such as artificial neural networks (ANN) and gene expression programming (GEP) to predict the performance and emissions of a gasohol/hydrogen-powered SI engine. The ANN was adopted to correlate the engine variables viz. engine speed and gasohol/hydrogen mix vs. responses namely brake thermal efficiency (BTE), brake specific energy consumption (BSEC), carbon monoxide (CO), hydrocarbon (HC), oxides of nitrogen (NOx) and carbon dioxide (CO2). GEP model was further employed to predict BTE, BSEC, CO, HC, NOx and CO2. To examine the prediction efficacy of both AI techniques, a set of advanced statistical approaches was used. A set of advanced statistical techniques were employed to test the prediction efficiency of both AI techniques. It was revealed that ANN outperformed the GEP since the values for R in the case of ANN were 0.9864–0.9998 whereas the values for R in the case of GEP were 0.9864–0.9994. Similarly, in the instance of R2, ANN outperformed GEP. Furthermore, Kling-Gupta efficiency was greater in the case of ANN (0.9684–0.9999) than in GEP (0.8912–0.9991). Both AI approaches, however, displayed great prognostic effectiveness in forecasting engine performance and emissions

Reducing the energy demand of cellulosic ethanol through salt extractive distillation enabled by electrodialysis

One of the main challenges when a biochemical conversion technique is employed to produce cellulosic ethanol is the low concentration of ethanol in the fermentation broth, which increases the energy demand for recovering and purifying ethanol to fuel grade. In this study, two design cases implementing salt extractive distillation – with salt recovery enabled by a novel scheme of electrodialysis and spray drying – along with heat integrated distillation techniques of double-effect distillation and direct vapor recompression are investigated through process simulation with Aspen Plus® 2006.5 for reducing the thermal energy demand. Conventional distillation along with molecular sieve based dehydration is considered as the base case. Salt extractive distillation along with direct vapor recompression is found to be the most economical ethanol recovery approach for cellulosic ethanol with a thermal energy demand of 7.1 MJ/L (natural gas energy equivalents, higher heating value), which corresponds to a thermal energy savings of 23% and cost savings of 12% relative to the base case separation train thermal energy demand and total annual cost