Accounting for disturbances in forecasting in an automated control system for asphalt concrete mixture composition
Abstract
Accounting for disturbances in forecasting in an automated control system for asphalt concrete mixture composition
Incoming article date: 10.02.2024The quality of asphalt concrete mixture at the output of an asphalt concrete plant is unstable due to disturbances that we cannot control or control with significant delay. Disturbances may include factors such as inaccuracies in the existing relationships between the properties of asphalt concrete mixture components and the parameters of the technological process with the quality of the finished product. Disturbances can also be attributed to our lack of knowledge about the relationships between individual indicators and the quality of the mixture. Forecasting these disturbances to determine the actual quality at the output becomes a key task. Previously, determining the optimal length of data series for forecasting was a challenging task. Nowadays, with the use of modern technologies, this problem has been successfully solved. In this article, the authors propose an adaptive forecasting method to determine the optimal length of data series. The research results include forecasting error values with and without adaptation. The adaptive forecasting method demonstrated smaller values of mean absolute error (MAE) compared to the non-adaptive forecasting method (where the length of the time series is always equal to 100). This allows for more efficient and accurate prediction of cumulative disturbances, which is critically important for ensuring high and stable quality of asphalt concrete mixture.
Keywords: asphalt concrete, asphalt concrete mixture, disturbance, control system, autoregressive model, forecasting, adaptive forecasting method, optimal length of series, forecast accuracy, mean absolute error