Achieving sustainability through the temperature prediction of aggregate stockpiles

This paper presents the potential energy savings and how to achieve sustainability by predicting the temperature of aggregate stockpiles in the production process of asphalt mixtures. A possible way to achieve energy efficiency and therefore sustainability is to preheat the mineral mixture, i.e. the aggregate, before it enters the production process in the asphalt mixing plant, thus resulting in lower energy consumption per ton of asphalt. The main objective of the conducted research was to develop and test an artificial neural network (ANN) model and analyse the influence of three independent variables (hour in the day, season, air temperature) on the one dependent variable (temperature of the mineral mixture). The impact of the observed independent variables on the temperature of the mineral mixture is analysed in a standard uncovered aggregate stockpile and in a solar aggregate stockpile. From the obtained modelling results, it can be concluded that it is possible to successfully use ANN in the process of predicting the temperature of aggregate stockpiles in the processes of aggregate production and storage as part of the whole production process of asphalt mixtures. (C) 2019 Elsevier Ltd. All rights reserved.