Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices

Multi-criteria optimization model to investigate the energy waste of off-road vehicles utilizing soil bin facility

“…6 that increase of velocity and wheel load lead to the increment of energy waste. Increase of wheel load is reported in literature [5,10,11] to increase rolling resistance which results in increase of energy waste. Furthermore, from Eq.…”

Differences in tractor performance parameters between single-wheel 4WD and dual-wheel 2WD driving systems

“…Draft of a wheeled tractor, which is an important index of power efficiency, is a result of stress-strain interaction between the tractor wheels and the topsoil. Research shows that 20-55% of tractor's power can be lost in the process of interaction between the tires and the topsoil because of the slip and the rolling resistance (Taghavifar and Mardani, 2014a;Taghavifar et al, 2014;Š merda and Č upera, 2010;Muhsin, 2010). This is not simply a wasted power -it creates a soil compaction, which may be detrimental to crop production (Grečenko and Prikner, 2014;Patel and Mani, 2011).…”

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

“…Other special applications of ANN include modelling, control [63] and accident identification in nuclear power plants [64], design optimization of ORC (organic Rankine cycle) geothermal power plants [65], optimization of regenerative Clausius and ORC cycles [66], modelling of polymeric electrolyte membrane fuel cells [67] and solid oxide fuel cells [68], size optimization of industrial solar heating systems [69], performance prediction of refrigeration systems using solar-driven ejector-absorption technology [70], power output forecast of wind power plants [71] with application of evolutionary Imperialistic Competitive Algorithm for the optimization of ANN training process [72], hybrid modelling with Kalman filter algorithm for wind speed forecasting [73], control improvement in hybrid wind-diesel power generators [74], power generation modelling [75] and maximization [76] of photovoltaic systems and prediction of their performance [77], output power and fuel consumption determination of photovoltaic-diesel power systems [78], wave power modelling [79], bioelectricity output estimation of microbial fuel cells [80], fuel consumption prediction of heating and air conditioning systems in public buildings [81], electric demand modelling in bioclimatic buildings [82], energy consumption prediction in the tertiary sector (supermarkets) and comparison between present consumption and predicted baseline [83], online monitoring of voltage stability [84] and minimization of voltage deviations in electric power systems [85], hybrid PLS modelling for thermodynamic performance prediction in scroll compressors [86], performance and energy efficiency assessment of driven-wheel systems [87].…”