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Prediction models for performance and emissions of a dual fuel CI engine using ANFIS

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Abstract

Dual fuel engines are being used these days to overcome shortage of fossil fuels and fulfill stringent exhaust gas emission regulations. They have several advantages over conventional diesel engines. In this context, this paper makes use of experimental results obtained from a dual fuel engine for developing models to predict performance and emission parameters. Conventional modelling efforts to understand the relationships between the input and the output variables, requires thermodynamic analysis which is complex and time consuming. As a result, efforts have been made to use artificial intelligence modelling techniques like fuzzy logic, Artificial Neural Network (ANN), Genetic Algorithm (GA), etc. This paper uses a neuro fuzzy modelling technique, Adaptive Neuro Fuzzy Inference System (ANFIS) for developing prediction models for performance and emission parameter of a dual fuel engine. Percentage load, percentage Liquefied Petroleum Gas (LPG) and Injection Timing (IT) have been used as input parameters, whereas output parameters include Brake Specific Energy Consumption (BSEC), Brake Thermal Efficiency (BTE), Exhaust Gas Temperature (EGT) and smoke. In order to further improve the prediction accuracy of the model, GA has been used to optimize ANFIS. GA optimized ANFIS gives higher prediction accuracy of more than 90% for all parameters except for smoke, where there is a substantial improvement from 46.67% to 73.33%, when compared to conventional ANFIS model.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Nitte Mahalinga Adyanthaya Memorial Institute of Technology, Nitte, 574 110, India

    A ADARSH RAI, P SRINIVASA PAI & B R SHRINIVASA RAO

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  1. A ADARSH RAI
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  2. P SRINIVASA PAI
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  3. B R SHRINIVASA RAO
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Correspondence to A ADARSH RAI.

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RAI, A.A., PAI, P.S. & RAO, B.R.S. Prediction models for performance and emissions of a dual fuel CI engine using ANFIS. Sadhana 40, 515–535 (2015). https://doi.org/10.1007/s12046-014-0320-z

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  • DOI: https://doi.org/10.1007/s12046-014-0320-z

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