Selection of Oil and best Bio-diesel Blend based on Performance and Emission Characteristics of IC Engine: An Integrated CRITIC-TOPSIS Approac

Saravanakumar, K ; Robinson, Y ; Madhu, P ; Manoj, Mathew

Abstract

Selection of optimum bio-diesel blend for internal combustion (IC) engine is crucial. The process of selecting the ideal blend requires a multidimensional analysis. In order to tackle the challenge, an efficient decision-making strategy is required. This paper uses the Multi-Criteria Decision-Making (MCDM) method to offer the selection of a suitable oil and bio-diesel blend based on the performance of the diesel engine under various load circumstances. In order to measure the weights of evaluating criteria, Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) are used. At first, seven different oils and seven assessment parameters, namely kinematic viscosity, cetane number, heating value, cloud point, pour point, flash point and density are attempted to select the acceptable oil for making bio-diesel. Next, the ranking of bio-diesel blends is performed based on the evaluation criteria, namely Brake Thermal Efficiency (BTE), Exhaust Gas Temperature (EGT), nitrogen oxide (NOx), smoke, carbon monoxide (CO), carbon dioxide (CO2) and hydrocarbon (HC) emissions. The results show that hemp seed oil is closer to diesel and higher in ranking. The recommended order of blend is B20 > Diesel > B40 > B60 > B80 > B100. The study indicated that B20 is the optimum blend for diesel engines. In order to meet the economy and pollution standards for the green revolution, decision-makers can use the new insights into MCDM approaches described in this article. This study also demonstrates that the suggested methods for choosing the best bio-diesel blend differ from the existing literature.


Keyword(s)

Engine analysis, MCDM, Ranking, Suitability, Vegetable oils

Full Text: PDF (downloaded 538 times)

Refbacks

  • There are currently no refbacks.
This abstract viewed 947 times