RESUMEN
The current energy challenges in agriculture, industry, and transportation are aggravated by insufficient liquid petroleum fuels, strained by rapid depletion, and higher demand in the international market. Existing environmental pollution due to higher fossil fuel consumption, certainly draws the attention of many researchers to identify a better alternative fuel concerning engine efficiency and exhaust emissions. Waste plastic oil (WPO) derived by thermo-catalytic pyrolysis is found to be a promising alternative fuel due to it's similar fuel properties to diesel. WPO contains long-chain hydrocarbons and high-molecular-weight aromatics which can be eliminated by fractional distillation, resulting in the production of distilled waste plastic oil (DPO). Ethanol is added in addition to DPO in the diesel fuel mixture in order to improve combustion for better performance and reduce emissions. The current study focused on the preparation of homogenous fuel mixtures (DPO/ethanol/diesel) to evaluate it's engine efficiency and exhaust emissions as compared to pure diesel and confirmed that it has the potential to be an alternate fuel for the CI engine. Test engine trials were performed to determine the potential engine characteristics, for instance, thermal efficiency, specific fuel consumptions, and exhaust temperature, by using various fuel mixtures (80D10DPO10E, 70D15DPO15E, 60D20DPO20E, 50D25DPO25E) under different loading conditions of the test engine. Major pollutants including unburned hydrocarbon, carbon monoxide, and nitrogen oxides were measured by a standard emission analyzer. The BTE was increased by 3.7%, and the BSFC was 16.67% less for the 60D20DPO20E mixture so as to diesel at full load. CO emission was found to comparatively increase at higher concentrations and decrease at higher loads. Compared to diesel, the NOx and HC emission were shown to be lowered at low loads and increased at higher loads. The study concluded that the fuel mixture of 60D20DPO20E showed the best engine performance and reduced emissions as compared to diesel.
RESUMEN
In the present research work, artificial neural network (ANN) is used to model the performance and emission parameters in a four-stroke, single-cylinder diesel engine combusting a blended fuel of diesel and catalytic co-pyrolysis oil produced from seeds of Pongamia pinnata, waste LDPE, and calcium oxide catalyst. The optimum yield of oil obtained was 92.5% at 500 °C temperature. Physical properties of the obtained oil, such as calorific value (44.85 MJ/kg) and density (797 kg/m3), level it by that of diesel while the flash point and fire point were found to be lower than that of pure diesel. An ANN model was then generated for the prediction of performance characteristics (BTE and BSFC) and emission characteristics (NOx and smoke) under varying loads, braking power, brake mean effective pressure, and torque as inputs using the Levenberg-Marquardt back-propagation training technique. The regression coefficients (R2) for BTE, BSFC, smoke, and NOx predictions were determined to be close to unity at 0.99859, 0.99814, 0.96129, and 0.92505, respectively (all values being close to unity). It has been discovered that ANN makes an effective simulation and prediction tool for blended fuels in CI engines. It is also suggested to predict the mechanical efficiency, volumetric efficiency, and CO, CO2, HC emissions using ANN in its future work.
RESUMEN
The current study focuses on the engine performance and emission analysis of a 4-stroke compression ignition engine powered by waste plastic oil (WPO) obtained by the catalytic pyrolysis of medical plastic wastes. This is followed by their optimization study and economic analysis. This study demonstrates the use of artificial neural networks (ANN) to forecast a multi-component fuel mixture, which is novel and reduces the amount of experimental effort required to determine the engine output characteristics. The engine tests were conducted using WPO blended diesel at various proportions (10%, 20%, 30% by volume) to acquire the required data for training the ANN model, which enables better prediction for the engine performance by making use of the standard back-propagation algorithm. Considering supervised data obtained from repeated engine tests, an artificial intelligence-based model of ANN was designed to select different parameters of performance and emission as output layers; at the same time, engine loading and different blending ratios of the test fuels were taken as the input layers. The ANN model was built up making use of 80% of testing outcomes for training. The ANN model forecasted engine performance and exhaust emission with regression coefficients (R) at 0.989-0.998 intervals and a mean relative error from 0.002 to 0.348%. Such results illustrated the effectiveness of the ANN model for estimating emissions and the performance of diesel engines. Moreover, the economic viability of the use of 20WPO as an alternative to diesel was justified by thermo-economic analysis.