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

S. Charan Kumar, Amit Kumar Thakur, J. Ronald Aseer, Sendhil Kumar Natarajan, Rajesh Singh, Neeraj Priyadarshi and Bhekisipho Twala ()
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S. Charan Kumar: Department of Mechanical Engineering, Lovely Professional University, Punjab 144401, India
Amit Kumar Thakur: Department of Mechanical Engineering, Lovely Professional University, Punjab 144401, India
J. Ronald Aseer: Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal 609609, India
Sendhil Kumar Natarajan: Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal 609609, India
Rajesh Singh: Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248012, India
Neeraj Priyadarshi: Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, India
Bhekisipho Twala: Digital Transformation Portfolio, Tshwane University of Technology, Staatsartillerie Rd., Pretoria West, Pretoria 0183, South Africa

Energies, 2022, vol. 15, issue 17, 1-19

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 NO X and HC while increasing CO 2 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 NO X , HC, CO 2 , 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.

Keywords: Artificial Neural Network; biofuels; CI engine; micro-algae spirulina (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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