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.

Comprehensive Study on the Effect of CuO Nano Fluids Prepared Using One-Step Chemical Synthesis Method on the Behavior of Waste Cooking Oil Biodiesel in Compression Ignition Engine

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Ramanathan Velmurugan ◽  
Jaikumar Mayakrishnan ◽  
S. Induja ◽  
Selvakumar Raja ◽  
Sasikumar Nandagopal ◽  
...  
Keyword(s):  
Chemical Synthesis ◽  
Vegetable Oil ◽  
Diesel Fuel ◽  
Synthesis Method ◽  
Waste Cooking Oil ◽  
Compression Ignition ◽  
Cooking Oil ◽  
One Step ◽  
Ci Engine ◽  
The Impact

Vegetable oil is considered as one among the promising alternatives for diesel fuel as it holds properties very close to diesel fuel. However, straight usage of vegetable oil in compression ignition (CI) engine resulted in inferior performance and emission behavior. This can be improved by modifying the straight vegetable oil into its esters, emulsion, and using them as a fuel in CI engine showcased an improved engine behavior. Waste cooking oil (WCO) is one such kind of vegetable oil gained a lot of attraction globally as it is generated in a large quantity locally. The present investigation aims at analyzing various parameters of single cylinder four stroke CI engine fueled with waste cooking oil biodiesel (WCOB), waste cooking oil biodiesel water emulsion (WCOBE) while the engine is operated with a constant speed of 1500 rpm. Furthermore, an attempt is made to study the impact of nanofluids in the behavior of the engine fueled with WCOB blended with nanofluids (WCOBN50). This work also explored a novel method of producing nanofluids using one-step chemical synthesis method. Copper oxide (CuO) nanofluids were prepared by the above mentioned method and blended with waste cooking oil biodiesel (WCOBN50) using ethylene glycol as a suitable emulsifier. Results revealed that brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) of WCOBN50 are significantly improved when compared to WCOB and WCOBE. Furthermore, a higher reduction in oxides of nitrogen (NOx), carbon monoxide (CO), hydrocarbon (HC), and smoke emissions were observed with WCOBN50 on comparison with all other tested fuels at different power outputs. It is also identified that one-step chemical synthesis method is a promising technique for preparing nanofluids with a high range of stability.

Predicting the Cetane Number, Yield Sooting Index, Kinematic Viscosity, and Cloud Point for Catalytically Upgraded Pyrolysis Oil Using Artificial Neural Networks

2020 ◽  
Author(s):  
Travis Kessler ◽  
Thomas Schwartz ◽  
Hsi-Wu Wong ◽  
J. Hunter Mack
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cloud Point ◽  
Diesel Fuel ◽  
Kinematic Viscosity ◽  
Fast Pyrolysis ◽  
Cetane Number ◽  
Alternative Methods ◽  
Pyrolysis Oil ◽  
Artificial Neural

Abstract The conversion of biomass using fast pyrolysis has the potential to be significantly less expensive at scale compared to alternative methods such as fermentation and gasification. Selective upgrading of the products of fast pyrolysis through chemical catalysis produces compounds with lower oxygen content and lower acidity; however, identifying the specific catalytic pathways for producing viable fuels and fuel additives often requires a trial-and-error approach. Specifically, key properties of the compounds must be experimentally tested to evaluate the viability of the resultant compounds. The present work proposes predictive models constructed with artificial neural networks (ANNs) for cetane number (CN), yield sooting index (YSI), kinematic viscosity (KV), and cloud point (CP), with blind test set median absolute errors of 5.14 cetane units, 3.36 yield sooting index units, 0.07 millimeters squared per second, and 4.89 degrees Celsius, respectively. Furthermore, the cetane number, yield sooting index, kinematic viscosity, and cloud point were predicted for over three hundred expected products from the catalytic upgrading of pyrolysis oil. It was discovered that 130 of these compounds have predicted cetane numbers greater than 40, with four of these compounds possessing predicted yield sooting index values significantly less than that of diesel fuel and predicted viscosities and cloud points comparable to that of diesel fuel.

scholarly journals Study on Particulate Matter of Diesel Engine Using Waste Cooking Oil

2015 ◽  
Vol 773-774 ◽  
pp. 420-424 ◽  
Author(s):  
Nur Fauziah Jaharudin ◽  
Nur Atiqah Ramlan ◽  
Mohd Herzwan Hamzah ◽  
Abdul Adam Abdullah ◽  
Rizalman Mamat
Keyword(s):  
Particulate Matter ◽  
Diesel Engine ◽  
Diesel Fuel ◽  
Alternative Fuels ◽  
Engine Speed ◽  
Direct Injection ◽  
Waste Cooking Oil ◽  
Manufacturing Cost ◽  
Alternative Source ◽  
Cooking Oil

Particulate matter (PM) is one of the major pollutants emitted by diesel engine which have adverse effects on human health. Accordingly, many researches have been done to find alternative fuels that are clean and efficient. Biodiesel is preferred as an alternative source for diesel engine which produces lower PM than diesel fuel. However, the manufacturing cost of biodiesel from vegetable oil is expensive. Therefore, using waste cooking oil (WCO) for biodiesel would be more economical and sustainable solution. The characteristics of direct injection diesel engine in term of the PM have been investigated experimentally in this study. The experiments were conducted using single cylinder diesel engine with different speed (1200 rpm, 1500 rpm, 1800 rpm, 2100 rpm, 2400 rpm) at constant load. PM emission of WCO B100 and diesel fuel was compared and the effect of PM components such as soluble organic fraction (SOF) and soot were studied. The result showed WCO B100 reduces the PM emission at all engine speed. Furthermore, both fuels showed highest reduction of PM concentration at moderate engine speed of 1500 rpm.

scholarly journals THE INTELLIGENT SYSTEM FOR AUTOMOTIVE FUELS QUALITY DEFINITION

2013 ◽  
Vol 3 (3) ◽  
pp. 11-13
Author(s):  
Volodymyr Drevetskiy ◽  
Marko Klepach
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diesel Fuel ◽  
Octane Number ◽  
Intelligent System ◽  
Cetane Number ◽  
Cetane Index ◽  
Hydrodynamic Method ◽  
Artificial Neural ◽  
Automotive Fuels

An intelligent system, based on hydrodynamic method and artificial neural networks usage for automotive fuels quality definition have been developed. Artificial neural networks optimal structures for the octane number of gasoline, cetane number, cetane index of diesel fuel definition have been substantiated and their accuracy has been analyzed. The implementation of artificial neural networks by means of microcontroller-based systems has been considered.

Estimation of Vegetable Oil-Based Ethyl Esters Biodiesel Densities Using Artificial Neural Networks

2008 ◽  
Vol 8 (17) ◽  
pp. 3005-3011 ◽  
Author(s):  
Saeid Baroutian ◽  
Mohamed Kheireddin ◽  
Abdul Aziz Abdul Rama ◽  
Nik Meriam Nik Sulaim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vegetable Oil ◽  
Ethyl Esters ◽  
Artificial Neural
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