An Experimental Analysis and ANN Based Parameter Optimization of the Influence of Microalgae Spirulina Blends on CI Engine Attributes
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...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/17/6158 |
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| author | S. Charan Kumar Amit Kumar Thakur J. Ronald Aseer Sendhil Kumar Natarajan Rajesh Singh Neeraj Priyadarshi Bhekisipho Twala |
| author_facet | S. Charan Kumar Amit Kumar Thakur J. Ronald Aseer Sendhil Kumar Natarajan Rajesh Singh Neeraj Priyadarshi Bhekisipho Twala |
| author_sort | S. Charan Kumar |
| collection | DOAJ |
| description | 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<sub>X</sub> and HC while increasing CO<sub>2</sub> 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<sub>X</sub>, HC, CO<sub>2</sub>, 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. |
| first_indexed | 2024-03-10T01:52:45Z |
| format | Article |
| id | doaj.art-3c09117392d649e1a3829d4485cd88c5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T01:52:45Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3c09117392d649e1a3829d4485cd88c52023-11-23T13:01:19ZengMDPI AGEnergies1996-10732022-08-011517615810.3390/en15176158An Experimental Analysis and ANN Based Parameter Optimization of the Influence of Microalgae Spirulina Blends on CI Engine AttributesS. Charan Kumar0Amit Kumar Thakur1J. Ronald Aseer2Sendhil Kumar Natarajan3Rajesh Singh4Neeraj Priyadarshi5Bhekisipho Twala6Department of Mechanical Engineering, Lovely Professional University, Punjab 144401, IndiaDepartment of Mechanical Engineering, Lovely Professional University, Punjab 144401, IndiaDepartment of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal 609609, IndiaDepartment of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal 609609, IndiaUttaranchal Institute of Technology, Uttaranchal University, Dehradun 248012, IndiaDepartment of Electrical Engineering, JIS College of Engineering, Kolkata 741235, IndiaDigital Transformation Portfolio, Tshwane University of Technology, Staatsartillerie Rd., Pretoria West, Pretoria 0183, South AfricaIn 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<sub>X</sub> and HC while increasing CO<sub>2</sub> 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<sub>X</sub>, HC, CO<sub>2</sub>, 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.https://www.mdpi.com/1996-1073/15/17/6158Artificial Neural NetworkbiofuelsCI enginemicro-algae spirulina |
| spellingShingle | S. Charan Kumar Amit Kumar Thakur J. Ronald Aseer Sendhil Kumar Natarajan Rajesh Singh Neeraj Priyadarshi Bhekisipho Twala An Experimental Analysis and ANN Based Parameter Optimization of the Influence of Microalgae Spirulina Blends on CI Engine Attributes Energies Artificial Neural Network biofuels CI engine micro-algae spirulina |
| title | An Experimental Analysis and ANN Based Parameter Optimization of the Influence of Microalgae Spirulina Blends on CI Engine Attributes |
| title_full | An Experimental Analysis and ANN Based Parameter Optimization of the Influence of Microalgae Spirulina Blends on CI Engine Attributes |
| title_fullStr | An Experimental Analysis and ANN Based Parameter Optimization of the Influence of Microalgae Spirulina Blends on CI Engine Attributes |
| title_full_unstemmed | An Experimental Analysis and ANN Based Parameter Optimization of the Influence of Microalgae Spirulina Blends on CI Engine Attributes |
| title_short | An Experimental Analysis and ANN Based Parameter Optimization of the Influence of Microalgae Spirulina Blends on CI Engine Attributes |
| title_sort | experimental analysis and ann based parameter optimization of the influence of microalgae spirulina blends on ci engine attributes |
| topic | Artificial Neural Network biofuels CI engine micro-algae spirulina |
| url | https://www.mdpi.com/1996-1073/15/17/6158 |
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