Eficiência energética de um trator agrícola utilizando duas configurações de tomada de potência

Frantz, Ulisses Giacomini; Schlosser, José Fernando; Farias, Marcelo Silveira de; Ferigolo, Leticia Frizzo; Ebert, Leandro Correia

doi:10.1590/0103-8478cr20130175

Cited by 7 publications

(8 citation statements)

References 4 publications

(3 reference statements)

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“…The fuel consumption measured in this study showed greater variability than the one estimated by LEITE et al (2011), VALE et al (2011), SPAGNOLO et al (2012, CHIODEROLI et al (2014) and FRANTZ et al (2014), whose variation coefficients were 3.34; 5.73; 1.31; 5.28 and 3.17%, respectively. However, they approached the ones determined by SOUZA et al (2015) between 6.05 and 15.12%.These discrepancies agree with the expected because this study used a larger number of operating conditions for agricultural equipment, given by the three gear and three engine speeds in order to increase the range of fuel consumption, which justifies high fluctuations around the average.…”

Section: Resultscontrasting

confidence: 55%

“…The influence of the technical characteristics of the mechanized set, gears and rotations in the tractor engine on fuel consumption is confirmed on the study of MOUNTAIN et al (2011), VALE et al (2011), SPAGNOLO et al (2012), FRANTZ et al (2014, FEITOSA et al (2015), FIORESE et al (2015) and SOUZA et al (2015). However, in these studies, the functional dependencies to determine this consumption were not established.…”

Section: Introductionmentioning

confidence: 95%

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Estimation of Fuel Consumption in Agricultural Mechanized Operations Using Artificial Neural Networks

et al. 2017

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This study aimed to develop artificial neural networks for the estimation of tractor fuel consumption during soil preparation, according to the adopted system. The multilayer perceptron network was chosen. As input data: the soil mechanical penetration resistance, the mobilized area by implements, the working gear and the tractor engine speed. The number of layers and neurons varied to form different architectures. The adjustment was verified based on various statistical criteria. The values estimated by the networks did not differ significantly from those obtained experimentally. The conclusion was that the networks showed adequate reliability and accuracy to predicting the fuel consumption in each tillage system, in function of the input data and this can be a useful tool for planning and management of agricultural operations.

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Section: Resultscontrasting

confidence: 55%

Section: Introductionmentioning

confidence: 95%

Estimation of Fuel Consumption in Agricultural Mechanized Operations Using Artificial Neural Networks

et al. 2017

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“…Alternative managing options for mechanized agricultural operations are being searched for, more specifically for the main source of power for them, the farm tractor, because of the costs of fuel, pollution generated by their combustion, and the necessity of diminishing energy consumption (Frantz et al, 2014). Tractor use in rural areas is very diverse, and the search for the optimized performance is justifiable (Vale et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Reduction of fuel consumption using driving strategy in agricultural tractor

Farias

Schlosser

Linares

et al. 2019

Rev. bras. eng. agríc. ambient.

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During the acquisition and use of an agricultural tractor, the consumption of fuel by the engine must be taken into account by the user, considering its nonrenewable origin and the marketing price. Appropriate strategies in driving tractors can help users to reduce production costs. In this context, the objective of this study was to evaluate the fuel consumption of an agricultural tractor using different driving strategies. Load levels were imposed, following the instructions from Organization for Economic Co-operation and Development Code 2, using an instrumented dynamometer car, in a concrete test track. Six load levels (30, 40, 50, 60, 70, and 80% of Q0) and three travel speeds (5.16, 7.29, and 10.48 km h -1 ) were applied for the strategies used (Full Throttle and Shifted Up -Throttle Back), where Q0 is the traction force corresponding to the maximum power in the drawbar for each gear selected. Three repetitions were made, which totaled 108 experimental units (2 × 3 × 6 × 3), in a completely randomized experimental design. Shifted Up -Throttle Back mode should be used as a strategy of tractor driving, because it can save up to 29.39% of fuel in relation to the Full Throttle mode, typically used by users. Redução do consumo de combustível usando estratégia de condução em trator agrícola RESUMO: No momento da aquisição e utilização de um trator agrícola, o consumo de combustível pelo motor deve ser levado em conta pelo usuário, tendo em vista sua origem não renovável e o preço de comercialização. Estratégias adequadas de condução de tratores podem auxiliar os usuários a reduzirem os custos de produção. Nesse contexto, objetivou-se avaliar o consumo de combustível de um trator agrícola utilizando diferentes estratégias de condução. Cargas parciais foram impostas, seguindo instruções contidas no Código 2 da Organização para a Cooperação e Desenvolvimento Econômico, utilizando carro dinamométrico instrumentado, em pista de concreto. Foram aplicadas seis cargas parciais (30; 40; 50; 60; 70 e 80% de Q0) e três velocidades de deslocamento (5,16; 7,29 e 10,48 km h -1 ), para as estratégias utilizadas (Aceleração Máxima; e Marcha Longa -Aceleração Reduzida), onde Q0 é a força de tração correspondente à potência máxima na barra de tração, para cada marcha selecionada. Foram realizadas três repetições, que totalizaram 108 unidades experimentais (2 x 3 x 6 x 3), em um delineamento experimental inteiramente casualizado. O modo Marcha Longa -Aceleração Reduzida deve ser utilizado como uma estratégia de condução do trator, pois pode-se economizar até 29,39% de combustível em relação ao modo Aceleração Máxima, normalmente utilizado pelos usuários.Palavras-chave: pista de teste de concreto, cargas parciais, eficiência energética

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“…The growing environmental concern constrains the use of fossil fuels, which are the main responsible for the emission of pollutant gases in the atmosphere (Frantz et al, 2014). Global energy consumption and the adverse environmental effects of its use have had significant increases in the past century and are projected to continue to increase in the future (Janulevicius et al, 2013;Lebedevas et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Effect of air and fuel supercharging on emissions from an agricultural engine

et al. 2018

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Pollutant gases emitted by engines depend mainly on the applied load and engine speed. This study quantifies the emissions of pollutant gases from a diesel-cycle engine, while changing the configuration of the air and fuel supply system. We analyzed the variables particulate matter (g kWh -1 ), carbon monoxide (g kWh -1 ), nitrogen oxides (g kWh -1 ), oxygen (%vol.) and hydrocarbons (g kWh -1 ), in different engine configurations (aspirated; aspirated + service; turbocharged + service; and turbocharged). Dynamometric experiments were carried out by means of the power take-off of an agricultural tractor. The experimental design was completely randomized, in a two-factorial scheme with three replicates. The results indicate that engine supercharging, compared to the original configuration, significantly reduces the particulate matter of gases and carbon monoxide, but increases the levels of nitrogen oxides emitted. The load applied to the engine was directly related to the emissions, except for oxygen. ResumoOs gases poluentes emitidos pelo motor dependem, principalmente, da carga aplicada e de sua rotação de trabalho. Objetivou-se quantificar as emissões de gases poluentes de um motor ciclo Diesel, alterando a configuração do sistema de alimentação de ar e de combustível. As variáveis analisadas foram material particulado (g kWh -1 ), monóxido de carbono (g kWh -1 ), óxidos de nitrogênio (g kWh -1 ), oxigênio (% vol.) e hidrocarbonetos (g kWh -1 ) em diferentes configurações do motor (aspirado, aspirado + serviço, turboalimentado + serviço e turboalimentado). Foram realizados experimentos dinamométricos por meio da tomada de potência de um trator agrícola. O delineamento experimental utilizado foi o inteiramente casualizado, em esquema bifatorial, com três repetições. Os resultados indicam que a sobrealimentação do motor, em relação à configuração original, proporciona redução significativa de material particulado dos gases e do monóxido de carbono, porém aumento dos níveis de óxidos de nitrogênio emitidos. A carga aplicada ao motor esteve diretamente relacionada às emissões, exceto para oxigênio.Palavras-chave adicionais: bomba injetora; gases de efeito estufa; trator agrícola; turbocompressor.

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Eficiência energética de um trator agrícola utilizando duas configurações de tomada de potência

Cited by 7 publications

References 4 publications

Estimation of Fuel Consumption in Agricultural Mechanized Operations Using Artificial Neural Networks

Estimation of Fuel Consumption in Agricultural Mechanized Operations Using Artificial Neural Networks

Reduction of fuel consumption using driving strategy in agricultural tractor

Effect of air and fuel supercharging on emissions from an agricultural engine

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