Environmental Science and Pollution Research

, Volume 26, Issue 3, pp 2281–2296 | Cite as

An experimental investigation of performance and emission of thumba biodiesel using butanol as an additive in an IDI CI engine and analysis of results using multi-objective fuzzy-based genetic algorithm

  • Rabisankar DebnathEmail author
  • Gadepalli Ravi Kiran Sastry
  • Ram Naresh Rai
Research Article


The present work studies the effect of butanol in thumba (Citrullus colocynthis) biodiesel in an IDI CI engine at varying percentages of 5 and 10% in 15 and 10% thumba biodiesel respectively with 80% diesel in each blend. Another blend was introduced with 80% diesel and 20% biodiesel without any additive. The experiment was conducted in a single cylinder four-stroke IDI CI engine at 1500 rpm varying from 25% to full-load (100%) conditions. The results showed diesel with less bio diesel and higher butanol in percentage gives good performance and emission compared to diesel at higher loads. Blend containing 10% bio diesel, 10% butanol, and 80% diesel (D80B10Bu10) showed higher cylinder pressure, heat release rate, BThE, and less NOx. Biodiesels gave less UHC, CO emissions. In this work, multi-objective fuzzy-based genetic algorithm was introduced for the best fit result. Four parameters were used for optimization (BSFC, BThE, CO, NOx). The result from genetic algorithm was taken for validation and the optimized result was found adequate after validation.


Biodiesel Butanol Performance Emission Fuzzy MPCI 



Diesel 100%



BL 1

D80B10Bu10—diesel 80% thumba biodiesel 10% butanol 10%

BL 2

D80B15Bu5—diesel 80% thumba biodiesel 15% butanol 5%

BL 3

D80B20—diesel 80% thumba biodiesel 20%


probability of crossover


mutation probability



indirect injection


pure diesel


brake specific fuel consumption


brake thermal efficiency


unburned hydrocarbon


carbon monoxide


oxides of nitrogen


vegetable oil


multi-performance characteristic index








extremely bad


very very bad


very bad


not good nor bad


very good


very very good


extremely good


genetic algorithm



The authors would like to acknowledge the National Institute of Technology Agartala, India for the technical support for the work, also to the technical staffs who were associated to the work.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Production EngineeringNational Institute of Technology AgartalaTripuraIndia
  2. 2.Department of Mechanical EngineeringNational Institute of Technology Andhra PradeshTadepalligudemIndia

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