ANN modeling for forecasting of VCR engine performance and emission parameters fuelled with green diesel extracted from waste biomass resources

• Published in: Environmental science and pollution research international Vol. 29; no. 34; pp. 51183 - 51210
• Main Authors: Manimaran, Rajayokkiam, Mohanraj, Thangavelu, Venkatesan, Moorthy
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2022; Springer Nature B.V
• Subjects: Aquatic Pollution; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Biodiesel fuels; Biofuels; Biomass; Brakes; Carbon monoxide; Compression; Correlation coefficient; Correlation coefficients; Diesel; Diesel engines; Diesel fuels; Earth and Environmental Science; Ecotoxicology; Emissions control; Energy consumption; Environment; Environmental Chemistry; Environmental Health; Environmental science; Esterification; Exhaust gases; Forecasting; Fuel consumption; Gas temperature; Green Energy for Environmental Sustainability; Mathematical models; Neural networks; Nitric oxide; Opacity; Parameters; Performance prediction; Physical properties; Polymer blends; Regression analysis; Seeds; Thermodynamic efficiency; Trichosanthes cucumerina; Variable compression ratio; Waste Water Technology; Water Management; Water Pollution Control; ANN; VCR engine; Performance; Green diesel; Prediction; Emissions
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-022-19500-8

In this research work, the experimental tests were conducted on a single-cylinder, constant speed, variable compression ratio (VCR) engine fuelled with green diesel. Initially, bio-oil was extracted from waste Trichosanthes cucumerina fruit seeds using the Soxhlet apparatus. The acquired bio-oil is used to make green diesel through the trans-esterification process. The fuel blends were prepared with different proportions of Trichosanthes cucumerina biodiesel (TCB) in diesel fuel (30%, 50%, and 70%) for the experimental test, and their thermo-physical properties were evaluated according to ASTM standards. At full load condition, the TCB30 blend with CR 18:1 gives closer engine performance of brake thermal efficiency (33.52%), brake specific fuel consumption (0.27 kg/kWh), and exhaust gas temperature (389.56 °C) and reduced emission levels of unburned hydrocarbon by 13.51%, carbon monoxide by 10.82%, smoke opacity by 16.87%, and the penalty of nitric oxide by 17.56% equated with neat diesel fuel. The engine performance and emission parameters are predicted using multiple regression artificial neural network (ANN) models. A database generated from the experimental results is used to train the ANN model. The average correlation coefficient ( R ) of the trained ANN model is 0.99673, which is closer to 1. It indicates that the proposed ANN model can generate the exact correlation between input factors and output responses. As a result, the application of ANN is a better forecasting tool for predicting VCR engine performance and emission characteristics.