A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy

Soft sensors are widely used to estimate process variables that are difficult to measure online. However, their predictive accuracy gradually decreases with changes in the state of the plants. We have been constructing soft sensor models based on the time difference of an objective variable, y, and that of explanatory variables (time difference models) for reducing the effects of deterioration with age such as the drift without model reconstruction. In this paper, we have attempted to improve and estimate the prediction accuracy of time difference models, and proposed to handle multiple y-values predicted from multiple intervals of time difference. A weighted average is a final predicted value and the standard deviation is an index of its prediction accuracy. This method was applied to real industrial data and then, could predict more number of data with higher predictive accuracy and estimate the prediction errors more accurately than traditional ones.

► Soft sensors are widely used to estimate process variables in chemical plants.
► Our goals are to improve and estimate the prediction accuracy of soft sensor models.
► We focused on soft sensor models based on time difference of process variables.
► We propose to handle values predicted from multiple intervals of time difference.
► The performance of the proposed method was confirmed with real industrial data.