Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses

Abstract

This experimental work presents a parametric investigation of Calophyllum inophyllum methyl ester (CIME)-diesel engine operations and artificial neural network (ANN) applied forecast of the engine out responses. The engine tests were performed for five test fuels from idle to full load conditions with the stipulated increment of 25% of the load for every run at three selected injection timings (21°, 23° and 25° CA bTDC) for 220bar, 260bar and 300bar injection pressures. The experimental outcomes indicated that twenty percentage blend of the biodiesel in neat diesel (CIME20) showed the highest brake thermal efficiency (BTE) among the CIME-diesel operations for 300bar injection pressure at 23° CA bTDC injection timing whereas BTE for the test fuels reduced at advanced and retarded injection timings at full load. CO, UBHC, dry soot and engine out O2 emissions were reduced at the advanced injection timing whereas NO and CO2 emissions increased. Using steady state experimental data, separate ANN models were proposed to forecast performance (BTE, BSEC, EGT) and emission (CO, CO2, UBHC, NO, dry soot and engine out O2) parameters with percentage load, blend percentage, injection pressure and injection timing as selected input control variables. The proposed ANN models indicated an impressive agreement as correlation coefficient (R) and mean absolute percentage error (MAPE) values perceived in the range of 0.99879–0.99993 and 0.87–4.62% respectively with remarkably lower root mean squared errors. Besides, lower values of mean squared relative error (MSRE) and noteworthy Nash-Sutcliffe coefficient of Efficiency (NSE) indices reasonably demonstrated robustness of the proposed models. Moreover, observed values of forecasting uncertainty Theil U2 indicated more effective outcomes for a credible model forecasting ability.