Environmental Science and Pollution Research

, Volume 25, Issue 29, pp 29115–29128 | Cite as

Optimization in the performance and emission parameters of a DI diesel engine fuelled with pentanol added Calophyllum inophyllum/diesel blends using response surface methodology

  • Purnachandran Ramakrishnan
  • Ramesh Kasimani
  • Mohamed Shameer Peer
Research Article


The primary objective of this work was to enhance the performance and emission of the computerized variable compression ratio (VCR) diesel engine fuelled with pentanol/Calophyllum inophyllum (CI)/diesel fuel blends. Based on the prerequisite for the current research, response surface methodology (RSM), an optimization technique, was adopted for the process parameters compression ratio (CR), load and fuel blends, and the optimized responses like brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), oxides of nitrogen (NOx), carbon monoxide (CO), carbon dioxide (CO2), hydrocarbon (HC), and smoke were revealed with the help of Derringer’s desirability approach. From the results, it is notified that pentanol-fuelled engine showed better performance and emissions at 17.5 CR, P20C20 (pentanol 20%+Calophyllum inophyllum 20%+diesel 60%) blend and 2.5 bmep (brake mean effective pressure) load conditions. The observed mathematical models and validation experiments show that the VCR diesel engine exhibits maximum efficiency and minimum emissions at the optimized input parameters.


Pentanol Calophyllum inophyllum Performance Emission Combustion Compression ratio Response surface methodology 



Calophyllum inophyllum


Direct injection


Carbon monoxide


Carbon dioxide


Oxides of nitrogen


Nitric oxide




Variable compression ratio


Pentanol 10%+Calophyllum inophyllum 20%+diesel 70%


Pentanol 15%+Calophyllum inophyllum 20%+diesel 65%


Pentanol 20%+Calophyllum inophyllum 20%+diesel 60%


Calophyllum inophyllum 20%+diesel 80%


Brake thermal efficiency


Brake specific fuel consumption


Brake mean effective pressure


Compression ratio


Modular diagnostic system


Response surface methodology


Artificial neural network


Design of experiments


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Copyright information

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

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, Research ScholarGovernment College of TechnologyCoimbatoreIndia
  2. 2.DindigulIndia
  3. 3.Department of Mechanical Engineering, Faculty of EngineeringGovernment College of TechnologyCoimbatoreIndia
  4. 4.Department of Mechanical Engineering, Faculty of EngineeringVV College of EngineeringTirunelveliIndia

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