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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
  • 55 Downloads

Abstract

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.

Keywords

Biodiesel Butanol Performance Emission Fuzzy MPCI 

Nomenclature

D100

Diesel 100%

BL

Blend

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%

Pc

probability of crossover

Pm

mutation probability

Abbreviation

IDI

indirect injection

PD

pure diesel

BSFC

brake specific fuel consumption

BTHE

brake thermal efficiency

UHC

unburned hydrocarbon

CO

carbon monoxide

NOx

oxides of nitrogen

VO

vegetable oil

MPCI

multi-performance characteristic index

B

bad

A

average

Good

good

EB

extremely bad

VVB

very very bad

VB

very bad

NGNB

not good nor bad

VG

very good

VVG

very very good

EG

extremely good

GA

genetic algorithm

Notes

Acknowledgments

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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