Applications of artificial neural networks in prediction of performance, emission and combustion characteristics of variable compression ratio engine fuelled with waste cooking oil biodiesel

2014 ◽  
Vol 37 (3) ◽  
pp. 915-928 ◽  
Author(s):  
K. Muralidharan ◽  
D. Vasudevan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Compression Ratio ◽  
Waste Cooking Oil ◽  
Combustion Characteristics ◽  
Variable Compression Ratio ◽  
Cooking Oil ◽  
Variable Compression Ratio Engine ◽  
Artificial Neural ◽  
Variable Compression

Effect of Variable Compression Ratio on Performance and Emissions in Compression Ignition Engine Fuelled with Waste Cooking Oil with Copper Oxide Nano Fluid Blends

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Durai Kumaran ◽  
S.P. Sundar Singh Sivam
Keyword(s):  
Waste Management ◽  
Copper Oxide ◽  
Compression Ratio ◽  
Alternative Fuels ◽  
Waste Cooking Oil ◽  
Compression Ignition ◽  
Variable Compression Ratio ◽  
Cooking Oil ◽  
Nano Fluids ◽  
Variable Compression

One of the challenging issues in the world today is waste management. Improper waste management could be the main source of environmental pollution. In this context, an attempt has been made to prevent the disposal of large quantities of Waste Cooking Oil (WCO) from hotels and restaurants and utilize them as a fuel in diesel engines. WCO is one of the viable alternative fuels, used by researchers in Compression Ignition (CI) engines due to its low cost, no toxicity, biodegradability and renewability. In this research, copper oxide (CuO) nano fluids were prepared by an one-step chemical synthesis method in different mass fractions of 15 ppm, 25 ppm, 35ppm and 50 ppm and blended with WCO. Based on the fuel stability, WCOCN25 and WCOCN50 test fuels are considered. The diesel and WCO were considered as base fuels. A fully equipped, single cylinder, four stroke, water cooled, direct injection, variable compression ratio diesel engine was used for experimentation. The compression ratio of the engine was varied from 16:1 to 18:1. The engine was loaded at different loading conditions by an eddy current dynamometer to measure the performance and emission parameters for the test fuels. The experimental results have shown that the addition of CuO nano fluids and increasing the compression ratio improved the Brake Thermal Efficiency (BTE) of the engine. It is observed that the combustion parameters have been improved due to the higher ignition delay and catalytic activity of CuO nano fluids. In addition, CuO nano fluids have a major role in controlling hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx) and smoke emissions.

scholarly journals Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends

2018 ◽  
Vol 22 (1) ◽  
pp. 179-205 ◽  
Author(s):  
Mert Gulum ◽  
Funda Kutlu Onay ◽  
Atilla Bilgin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vegetable Oil ◽  
Vegetable Oils ◽  
Diesel Fuel ◽  
Alternative Fuels ◽  
Waste Cooking Oil ◽  
Ternary Blends ◽  
Cooking Oil ◽  
Artificial Neural

Abstract Nowadays, biodiesel and vegetable oils have received increasing attention as renewable clean alternative fuels to fossil diesel fuel because of decreasing petroleum reserves and increasing environmental concerns. However, the straight use of biodiesel and vegetable oils in pure form results in several operational and durability problems in diesel engines because of their higher viscosity than fossil diesel fuel. One of the most used methods for solving the high viscosity problem is to blend them with fossil diesel fuel or alcohol. The reliable viscosity and density data of various biodiesel-diesel-alcohol ternary blends or biodiesel-diesel binary blends are plentifully available in existing literature, however, there is still the scarcity of dependable measurement values on different biodiesel-diesel-vegetable oil ternary blends at various temperatures. Therefore, in this study, waste cooking oil biodiesel (ethyl ester) was produced, and it was blended with fossil diesel fuel and waste cooking oil at different volume ratios to prepare ternary blends. Viscosities and densities of the ternary blends were determined at different temperatures according to DIN 53015 and ISO 4787 standards, respectively. The variation in viscosity with respect to temperature and oil fraction and the change of density vs. temperature were evaluated, rational and exponential models were proposed for these variations, and these models were tested against the density and viscosity data measured by the authors, Nogueira et al. and Baroutian et al. by comparing them to Gupta et al. model, linear model, Cragoe model and ANN (artificial neural networks) previously recommended in existing literature.

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