Combustion analysis of a compression-ignition engine fuelled with an algae biofuel blend and diethyl ether as an additive by using an artificial neural network

Experimental tests were performed on a single-cylinder, four-stroke, direct-injection, naturally aspirated diesel engine at a constant engine speed of 1500 rpm and at various loading conditions to evaluate its performance, emission, and combustion characteristics. When DEE was added to the algal biofuel blend, the maximum braking thermal efficiency of the engine increased by 7.2% and the minimum brake specific fuel consumption reduced by 6.3%. A reduction in the HC (12%) and CO (19%) emissions was observed with a marginal increase in the NOx (3%) emissions compared with diesel, without any modifications. Moreover, the algae biofuel with diethyl ether (DEE) exhibited combustion results similar to those exhibited by diesel fuel. However, a shortened ignition delay was observed due to their early combustion. The Levenberg-Marquardt backpropagation feedforward neural network (BPNN) training algorithm with a tangent sigmoid transfer function provides an optimum model for effectively predicting engine characteristics. In this study, the overall mean square error, mean absolute percentage error, and regression coefficient for the developed BPNN model were 0.011%, 4.81%, and 0.96, respectively. The experimental results indicated that small content by volume of the algae biofuel blend (BD20) with 5% DEE as a fuel additive exhibited satisfactory results and could be used as a moderate substitute for conventional fuel.