Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

摘要: Excavators are one of the most energy-intensive elements earthwork operations. Predicting energy consumption and CO2 emissions excavators is therefore critical in order to mitigate environmental impact However, there a lack method for estimating such emissions, especially during early planning stages these activities. This research proposes model using an artificial neural network (ANN) predict excavator’s hourly under different site conditions. The proposed ANN includes five input parameters: digging depth, cycle time, bucket payload, engine horsepower, load factor. Caterpillar handbook’s data, that included operational characteristics twenty-five models excavators, were used develop training testing sets model. also designed identify which factors from all parameters have greatest on based partitioning weight analysis. results showed can provide accurate tool stage excavators. Analyses revealed that, within parameters, time has emissions. findings enable control crucial significantly