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Predicting the engine trade-off study and performance characteristics using different blends of methyl Ester fish oil and higher alcohol with aid of artificial neural network based multi objective optimization

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Abstract

The present works focus on the preparation of a novel bio-diesel from Methyl esters fish oil and assessment in a compression ignition engine. The process is optimized with different oil-methanol ratio and catalyst concentration to obtain a maximum yield of 99%. With that purpose, B20, B30, and B40 (by volume) of biodiesel and isobutanol was added to diesel to make different blends, and its properties were estimated based on ASTM standard and compared with diesel oil. Based on the assessment on the Kirloskar TV1 model computerized direct injection compression ignition engine is has been observed that BTE increases by 12% for B20 fuel on par with diesel fuel. In contrast, HC and NOx have been decreased for lower blends of biodiesel as observed from the trade of plot. The results of different fuel blend operation have clearly indicated that there is an abundant possibility to pursue an optimization study where in the different input parameters can be set suitably to gain the utmost profit from biodiesel- diesel additive combination. Taguchi proposed an experimental design which involves the use of orthogonal array to organize the levels and the parameters influencing the processes. Besides, phase-based tagging Artificial neural network (ANN) is used to predict the best input mix, which resulted in the best performance characteristics. This multi-objective optimization technique shows that blend B20 has an optimal MPCI value of 0.68, which is the best blend to get higher performance and better emission characteristics.

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Abbreviations

deg CA:

degree Crank angle

DI:

Direct injection

HRR:

Heat release rate (J/ deg CA)

ASTM:

American Society for Testing Materials

CO:

Carbon monoxide

NOx:

Oxides of nitrogen

HC:

Hydrocarbon

EGR:

Exhaust gas recirculation

CI:

Compression ignition

BTE:

Brake thermal efficiency

EGT:

Exhaust gas temperature

CC:

Combustion chamber

NA:

Naturally aspirated

A-F:

Air-fuel ratio

ANN:

Artificial neural networks

MAPE:

Mean Absolute Percentage Error

MSE:

Mean Squared Error

MIMO:

Multiple-input Multiple-Output

RMSE:

Root mean Squared error

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, India

    S. K. Gugulothu & J. Ramachander

  2. Department of Mechanical Engineering, GITAM University, Hyderabad, India

    A. Kiran Kumar

Authors
  1. S. K. Gugulothu
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  2. J. Ramachander
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  3. A. Kiran Kumar
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Correspondence to S. K. Gugulothu.

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Gugulothu, S.K., Ramachander, J. & Kumar, A.K. Predicting the engine trade-off study and performance characteristics using different blends of methyl Ester fish oil and higher alcohol with aid of artificial neural network based multi objective optimization. Heat Mass Transfer 57, 1121–1138 (2021). https://doi.org/10.1007/s00231-020-03013-6

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