On the neurocomputing based intelligent simulation of tractor fuel efficiency parameters

Shafaei, S.M.; Loghavi, M.; Kamgar, S.

doi:10.1016/j.inpa.2018.02.003

Cited by 16 publications

(13 citation statements)

References 25 publications

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“…Also, the DMRT results showed that there were highly significant differences among their means. This agreed with the findings of Adewoyin and Ajav (2013), and Shafaei et al (2018).…”

Section: Combined Effects Of Ridging Speed and Height On Fuel Consumpsupporting

confidence: 93%

“…Also, the DMRT results showed there were highly significant differences among their means. This agreed with the findings of Ahaneku et al (2011), Adewoyi and Ajav (2013), Balami et al (2015), Almaliki et al (2016a and b) and Shafaei et al (2018). Figure 5 shows the effect of ridge height on fuel consumption during ridging.…”

Section: Effect Of Ridging Speed On Fuel Consumptionsupporting

confidence: 91%

“…Also, DMRT results showed highly significant differences among their means. This is in line with the findings of Fathollahzadeh et al (2009Fathollahzadeh et al ( , 2010, Gulsoylu et al (2012), Adewoyin and Ajav (2013), Moitzi et al (2014) and Shafaei et al (2018).…”

Section: Effect Of Ridge Height On Fuel Consumptionsupporting

confidence: 88%

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Predicting tractor fuel consumption during ridging on a sandy loam soil in a humid tropical climate

Igoni¹,

Ekemube²,

Nkakini³

2019

J. Eng. Technol. Res.

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A predictive model for the determination of optimum fuel consumption of a tractor during ridging operation has been developed. The model development involved field experimentations to determine the various parameters affecting tractor fuel consumption, like draught, speed, depth of cut, soil moisture content, cone index and width of cut. The field investigations were carried out at the farm of Rivers State Agricultural Development Programme in Port Harcourt, Nigeria. The field experimental design adopted was the Factorial in Randomized Complete Block Design (RCBD). It consisted of 9 experimental treatments with three replications. The experimental land area was 160 m by 32.5 m (5200 m 2), which was subdivided into three blocks of 9 plots each, measuring 50 m by 2.5 m, with an interplot spacing of 1 m provided for different treatment options. The experimental fuel consumption was determined from measuring the actual amount of fuel used by the tractor per unit time. The model equation was then formulated using the Buckingham pi theorem. The model showed that tractor fuel consumption during ridging is directly proportional to the draught, ridging speed, height of ridge and moisture content; and inversely proportional to the penetration resistance and width of cut. The model was validated by graphical comparison and with root mean square error and paired t-Test. The results obtained showed that there was no significant difference between the measured and predicted values at 95 and 99% confidence limits; and the model can accurately predict tractor fuel consumption during ridging operations using a disc ridger.

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“…Also, the DMRT results showed that there were highly significant differences among their means. This agreed with the findings of Adewoyin and Ajav (2013), and Shafaei et al (2018).…”

Section: Combined Effects Of Ridging Speed and Height On Fuel Consumpsupporting

confidence: 93%

Section: Effect Of Ridging Speed On Fuel Consumptionsupporting

confidence: 91%

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Predicting tractor fuel consumption during ridging on a sandy loam soil in a humid tropical climate

Igoni¹,

Ekemube²,

Nkakini³

2019

J. Eng. Technol. Res.

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“…They concluded that the ANN technique was adequately reliable and accurate in predicting tillage fuel consumption. Shafaei et al (2018) also found that an ANN model could predict tractor fuel efficiency parameters during tillage. Abbaspour-Gilandeh et al (2020) found that ANN model could predict the draft force of a rigid tine chisel cultivator.…”

Section: Introductionmentioning

confidence: 90%

“…Feedforward ANNs and error-backpropagation algorithms are common in the field of agricultural engineering (Shafaei et al, 2018). We selected a multilayer perceptron-based network for this study.…”

Section: Building the Ann Modelmentioning

confidence: 99%

Predicting the Performance Parameters of Chisel Plow Using Neural Network Model

2020

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This study examines the capability of an artificial neural network (ANN) approach using a backpropagation-learning algorithm to predict performance parameters for a chisel plow at three field sites with differing soils. The draft force, effective field capacity (EFC), fuel consumption rate (FC), overall energy efficiency (OEE), and rate of plowed soil volume (SVR) were predicted at varying plowing speeds, plowing depths, soil moisture contents, soil bulk densities, soil texture indexes, and tractor powers. Collected field data was divided into a training set (for predicting the required parameters) and testing set (for model validation). For the ANN algorithm, the number of hidden layers, neurons, and transfer functions were varied to construct different ANN architectures, which were then verified using various statistical criteria, such as mean absolute error. The results showed that an ANN with one hidden layer and 15 neurons was ideal. The developed ANN model predicted the draft force, EFC, FC, OEE, and SVR of the chisel plow with a mean absolute error of 3.23 kN, 0.80 hah-1 , 3.04 Lh-1 , 2.78% and 1.06 m 3 h −1 , respectively in the testing phase.

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On the Reliability of Intelligent Fuzzy System for Multivariate Pattern Scrutinization of Power Consumption Efficiency of Mechanical Front Wheel Drive Tractor

2021

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On the neurocomputing based intelligent simulation of tractor fuel efficiency parameters

Cited by 16 publications

References 25 publications

Predicting tractor fuel consumption during ridging on a sandy loam soil in a humid tropical climate

Predicting tractor fuel consumption during ridging on a sandy loam soil in a humid tropical climate

Predicting the Performance Parameters of Chisel Plow Using Neural Network Model

On the Reliability of Intelligent Fuzzy System for Multivariate Pattern Scrutinization of Power Consumption Efficiency of Mechanical Front Wheel Drive Tractor

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