An Experimental Analysis and ANN Based Parameter Optimization of the Influence of Microalgae Spirulina Blends on CI Engine Attributes

Abstract

In this present investigation, emittance and performance attributes of a diesel engine using micro-algae spirulina blended biodiesel mixtures of various concentrations (20%, 35%, 50%, 65%, 80%, and 100%) were evaluated. An optimization model was also developed using an Artificial Neural Network (ANN) to characterize the experimental parameters. Experimental findings demonstrated significant improvement in brake specific fuel consumption (BSFC) using varied blends. Furthermore, brake thermal efficiency (BTE) is decreased gradually for biodiesel blends as compared to diesel. Micro-algae spirulina blends have shown lower concentrations of NOX and HC while increasing CO2 relative to pure diesel. To develop the model, three sets of optimizers, namely, adam, nadam, and adagrad, along with activation functions, such as sigmoid, softmax, and relu, were selected. The results revealed that sigmoid activation function with adam learning optimizer by using 32 hidden layer neurons has given the least value of mean squared error (MSE). Hence, the ANN approach was proven to be capable of predicting engine attributes with a least mean squared error of 0.00013, 0.00060, 0.00021, 0.00011, and 0.00104 for NOX, HC, CO2, brake thermal efficiency, and brake specific fuel consumption, respectively. The Artificial Neural Network approach is capable of predicting CI engine attributes with accuracy and ease of investigation.