Developing a Logging Unit for Measuring and Recording Power Data Using Arduino Board

Hemeda, Samy G.; Aboukarima, Abdulwahed M.; Minyawi, Mamdouh

doi:10.21608/mjae.2017.96360

Cited by 5 publications

(3 citation statements)

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“…Five loading stages of 0, 25, 50, 75 and 100% were selected for every test. The engine started up at no loading stage with maximum speed; dynamometer sensors and Daytronic (model 10k4, https://www.daytronic.com/resources/software-manuals/ ) was used to record measured data by the pick-up frequency and torque sensor according to 28 , which engaged to the hydraulic dynamometer. The torque sensor comprises of a full whinstone bridge of strain gage for record the torque exerted on PTO shaft during loading test.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the performance and gas emissions of a tractor diesel engine using blended fuel diesel and biodiesel to determine the best loading stages

Emaish

Abualnaja

Kandil

et al. 2021

Sci Rep

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Fossil fuels are the main energy sources responsible for harmful emissions and global warming. Using biodiesel made from waste deep-frying oil as an alternative fuel source in diesel engines has drawn great attention. This biodiesel is produced using the transesterification process and blends with mineral diesel at Faculty of Agriculture Saba Basha, Alexandria University, Egypt. The turbocharged diesel engine of a Kubota M-90 tractor was tested. The objectives of this work are to test tractor as a source of power in the farm using waste deep-frying oil biodiesel to utilize waste frying oils (WFO) in clean energy production on the farm and determine the best engine loading stages to maximize engine efficiencies for different fuel blends and reduce the environmental impact of gas emissions from tractor diesel engines in the farms. The experiment design was factorial, with two factors, where the first was the engine load (0%, 25%, 50%, 75%, and 100%) and the second was fuel blend (0%, 5%, 20%, and 100% biodiesel), and the effects of loading stages and biodiesel percentage on engine performance indicators of engine speed, power take off torque, power take off power, brake power, brake mean effective pressure, brake thermal efficiency, brake specific fuel consumption, and gas emissions were studied. The experimental results indicated that engine load percentage and fuel blend percentage significantly affected all studied characters, and the best engine loading stages were between 25 and 75% to maximize engine efficiency and minimize the specific fuel consumption and gas emissions. Increasing the biodiesel percentage at all loading stages resulted decreasing in Engine brake power (BP), brake thermal efficiency, Power take-off (PTO) torque, and brake mean effective pressure and increases in brake specific fuel consumption. Increasing the engine load resulted in decreases in O2 emissions and increases in CO2, CO, NO, and SO2 emissions. Increasing the biodiesel percentage in the blended fuel samples resulted in increases in O2 and NO emissions and decreases in CO2, CO, and SO2 emissions. The use of biodiesel with diesel fuel reduces the environmental impact of gas emissions and decreases engine efficiency.

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Section: Methodsmentioning

confidence: 99%

Evaluation of the performance and gas emissions of a tractor diesel engine using blended fuel diesel and biodiesel to determine the best loading stages

Emaish

Abualnaja

Kandil

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The horizontal force (draft) was acquired in Abies area using a locally manufactured strain-gauge pull meter [27,28] with a 10 kN capacity that was joined between the two tractors. However, an Arduino Uno board arrangement was developed to acquire draft signals [29]. In addition, a fabricated locally made hydraulic pull meter, which consisted of a cylinder-piston system, connected to a Bourdon tube gage [30] was used to obtain draft data during field experiments at Meet El Deeba Province and El Gemmaiza Research Station sites.…”

Section: Plowing-field Experimentsmentioning

confidence: 99%

Evaluation of Artificial Neural Network to Model Performance Attributes of a Mechanization Unit (Tractor-Chisel Plow) under Different Working Variables

2022

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The focal objective of the current research is to apply artificial neural network (ANN) and multiple linear regression (MLR) methods for modeling the performance attributes of a mechanization unit (tractor-chisel plow) during the plowing process under both different soil types and working variables. Two different parameters to represent working conditions and soil type were considered as potential input parameters. The first parameter represented soil type by calculating soil texture index as a combination of clay, silt, and sand. The second one was constructed into one dimensionless parameter, namely the field working index. This index linked most working variables such as plowing speed, plow width, soil moisture content, soil bulk density, tractor power, and plowing depth. The performance of the created ANN and MLR models was appraised by computing mean-absolute-error criterion for the testing dataset. The mean absolute error values for draft force, effective field capacity, fuel consumption, drawbar power, overall energy efficiency, rate of plowed soil volume, and loading factor, were 1.44 kN, 0.03 ha/h, 1.17 L/h, 2.28 kW, 0.68%, 73.97 m3/h, and 0.08 (decimal), respectively, when the ANN model was applied. In addition, coefficient of determination (R2) acted as a criterion for judging the performance of the developed models, and their values when ANN was applied were 0.569, 0.384, 0.516, 0.454, 0.486, 0.777, and 0.730 for the same performance attributes, respectively. When the MLR model was applied, the corresponding values of R2 were 0.239, 0.358, 0.352, 0.429, 0.511, 0.482, and 0.422, respectively, for the same attributes. The current study adds to the standing literature by contributing data and information regarding the performance attributes of a tractor-chisel plow unit under specific working variables and soil types. In addition, the models developed for plowing operations in different soil texture and under the field working index are recommended for use in budgeting for diesel consumption, in calculating operation cost, in matching mechanization units of tractor-chisel plows, in estimating energy requirements of tractor-chisel plow combinations, etc.

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“…The auxiliary tractor was a Lamborghini tractor model 1106 with a diesel engine of 110 kW at 2500 rpm. A Kubota M1 tractor (70 kW) and Belarus (67 kW) were used as main and auxiliary tractors, respectively, at the first experimental site, where the draft was measured using a locally made strain gauge (Elashry et al, 1994;Younis et al, 2010) with a 10 kN capacity that was connected between the two tractors using Arduino board arrangements (Hemeda et al, 2017). A fabricated locally made hydraulic pull meter (Mohamed et al, 2004) was used at the other two sites.…”

Section: Field Experimentsmentioning

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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Developing a Logging Unit for Measuring and Recording Power Data Using Arduino Board

Cited by 5 publications

References 0 publications

Evaluation of the performance and gas emissions of a tractor diesel engine using blended fuel diesel and biodiesel to determine the best loading stages

Evaluation of the performance and gas emissions of a tractor diesel engine using blended fuel diesel and biodiesel to determine the best loading stages

Evaluation of Artificial Neural Network to Model Performance Attributes of a Mechanization Unit (Tractor-Chisel Plow) under Different Working Variables

Predicting the Performance Parameters of Chisel Plow Using Neural Network Model

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