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

Abstract

Excavators are one of the most energy-intensive elements of earthwork operations. Predicting the energy consumption and CO 2 emissions of excavators is therefore critical in order to mitigate the environmental impact of earthwork operations. However, there is a lack of method for estimating such energy consumption and CO 2 emissions, especially during the early planning stages of these activities. This research proposes a model using an artificial neural network (ANN) to predict an excavator's hourly energy consumption and CO 2 emissions under different site conditions. The proposed ANN model includes five input parameters: digging depth, cycle time, bucket payload, engine horsepower, and load factor. The Caterpillar handbook's data, that included operational characteristics of twenty-five models of excavators, were used to develop the training and testing sets for the ANN model. The proposed ANN models were also designed to identify which factors from all the input parameters have the greatest impact on energy and emissions, based on partitioning weight analysis. The results showed that the proposed ANN models can provide an accurate estimating tool for the early planning stage to predict the energy consumption and CO 2 emissions of excavators. Analyses have revealed that, within all the input parameters, cycle time has the greatest impact on energy consumption and CO 2 emissions. The findings from the research enable the control of crucial factors which significantly impact on energy consumption and CO 2 emissions.

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