Abstract
Compression ignition (CI) engines are popular in the transport sector because of their high compression ratio. However, in recent years, it has become a major concern from an environmental point of view because of the emission and depleting fossil fuel. The advanced combustion concept has been a popular research topic in the CI engine. Low-temperature combustion with alternate fuel has helped in reducing the oxides of nitrogen (NOx) and soot emission of the engine. Biogas is a popular substitute of energy especially deduced from biomass because of its clean combustion properties, as well it being a renewable energy source compared to non-renewable diesel resources. In experiments with dual fuel, i.e., conventional diesel and alternate fuel (biogas) were carried out through them. In the present study, an artificial neural network model was used to estimate emissions and check the attributes of performance. Different algorithms and training functions were used to train the models. However, the best training algorithm was Levenberge Marquardt and the training function was Tansig (Hyperbolic tangent sigmoid) and Logsig (logarithmic sigmoid), which showed the best result with regression coefficient (R > 0.98) and Mean square error (MSE < 0.001). The best model was trained by evaluating MSE and regression coefficient. Experimental results and artificial neural network (ANN) prediction showed that the experimental results were similar to each other and lie at the same intervals. The ANN model helped in predicting experimental data that were earlier difficult to experimentally perform using interpolation and extrapolations. It was observed that there was an increase in Brake Specific Energy Consumption (BSEC) and a decrease in Brake thermal efficiency (BTE) with improved biogas flow rate and reduced NOx emission in the combustion chamber. Carbon monoxide (CO) and hydrocarbon (HC) emissions increase linearly with the increase in biogas flow rate, whereas smoke opacity decreases. It could be concluded that this study helps in understanding the effect of dual fuel (diesel-biogas) combustion under different load conditions of the engine with the help of ANN, which could be a substitute fuel and help to protect the environment.
Highlights
In recent years, the interest in alternate fuels and renewable energy [1] has increased because of an increase in energy demand [2] and stringent environmental policies [3], which are because of the depleting fossil fuels, and an increase in the price of fossil fuels for the internal combustion engine [4]
It could be concluded that this study helps in understanding the effect of dual fuel combustion under different load conditions of the engine with the help of artificial neural network (ANN), which could be a substitute fuel and help to protect the environment
This paper examined the performance and emission features under the influence of diesel and biogas used together at varying engine loads at different gas flow rates
Summary
The interest in alternate fuels and renewable energy [1] has increased because of an increase in energy demand [2] and stringent environmental policies [3], which are because of the depleting fossil fuels, and an increase in the price of fossil fuels for the internal combustion engine [4]. In the case of IC engines, gaseous alternate fuel can be considered because of their high compression ratio [12] and good mixing characteristics, which in turn would decrease the emission and increase brake thermal efficiency [13]. CO2 composition of biogas helps in combustion at low temperature, which reduces the chance of NOx emission formation at elevated temperature during combustion in dual fuel mode [26,27]. It was evident that the experimental results and the ANN prediction were similar for the dual-fuel engine in terms of emission and performance [33]. This paper examined the performance and emission features under the influence of diesel and biogas used together at varying engine loads at different gas flow rates. 12H26 comparative analysis of the prediction and actual data are presented in this C paper
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