Measurement of performance and emission distinctiveness of Aegle marmelos seed cake pyrolysis oil/diesel/TBHQ opus powered in a DI diesel engine using ANN and RSM. (October 2019)

• Record Type: Journal Article
• Authors: Baranitharan, P.; Ramesh, K.; Sakthivel, R.
• Is Part Of: Measurement. Volume 144(2019)
• Page Start: 366
• Page End: 380
• Publication Date: 2019-10
• Subjects: Aegle marmelos -- Bio-oil -- TBHQ -- CI engine test -- ANN -- RSM; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.05.037
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Pyrolysis oil is derived from AM de-oiled seed cake. Effect of AM bio-oil/diesel/TBHQ blends on CI engine adaptability was studied. VCR engine characteristics were analysed by using ANN and RSM models. Dominance of ANN in capturing the nonlinear behaviour of the system was analysed. RSM possess advantages of sensitivity analysis and optimization. Abstract: The present investigation focuses on Artificial neural network (ANN) and Response surface methodology (RSM) modelling of a CI (Compression ignition) engine powered by Aegle marmelos (AM) pyrolysis oil/diesel/Tert-butyl hydroxyl quinone antioxidant (TBHQ) blend as a test fuel to predict and optimize the engine behaviour. Bio-oil is derived from AM de-oiled seed cake in a fixed bed pyrolysis reactor at 600 °C under the heating rate of 30 °C/min. To obtain data for testing and training the suggested RSM and ANN models, a direct injection, single cylinder CI engine was fuelled with proposed test fuel 80% diesel + 20% AM bio-oil + 1000 ppm TBHQ (A20D80T). The A20D80T has been assessed for the combined effects of varying compression ratio (CR = 16:1–17.5:1) and engine load (W = 25%–100%) in variable compression ratio (VCR) diesel engine through experimental investigation and ANN prediction and RSM optimization techniques. Using the experimental data for training, an ANN replica was developed according to feed forward back propagation algorithm (FFBP). Multi-layer perception (MLP) network was used for non-linear mapping between the experimental and predicted values. Engine process parameters were accurately predicted by trained ANN. The optimal values of engine performance (brake specific fuel consumption (BSFC) = 0.33 kg/kWh and brake thermal efficiency (BTE) = 22.01%) and emission behaviour (carbon monoxide (CO) = 0.67%, hydro carbon (HC) = 244 ppm, carbon dioxide (CO2 ) = 8.33% and oxides of nitrogen (NOx) = 351 ppm) were obtained by RSM optimization. The compression ratio of 17.5:1 at peak load condition was found to be superior engine characteristics through experimental assessment and ANN, RSM models. In the predicted ANN model the mean absolute average error (MAAE) was 0.552% and optimized RSM model MAAE was 1.231%. The ANN and RSM models gave the average correlation coefficient (R) of 0.998 and average coefficient of a determination (R 2 ) of 0.991 respectively. The experimental, ANN and RSM analysis results depict that A20D80T blend delivered the enhanced performance and better emission behaviours compared with neat diesel fuel (D).