Abstract
Statistical modelling of industrial production data can lead to improved understanding of the process to benefit process monitoring and control routines. The production data required for such models need however to be synchronized in time, a topic sparsely covered in literature. We propose a strategy for data-driven automated optimization of dynamic synchronization of industrial production data, that optimizes the synchronization per process variable and can be applied for on-line monitoring in real-time. The strategy is tested and validated for two relevant production facilities, each of which has multiple production lines or configurations. For all lines and configurations, models predicting the production quality from process variables improved in accuracy using the presented per-variable optimization strategy. Although the prediction accuracy for two models would still be insufficient for real-time monitoring and control, process operators and engineers may still obtain novel process understanding from applying the presented strategy on these models.
Highlights
Industrialchemical production facilities have to be carefully monitored and controlled to guarantee consistent turnover of high-quality product that meets customer wishes
We have developed a new strategy for automatically optimizing the dynamic synchronization of individual process variables for statistically modelling industrial production data
The method is designed and tested for regression models that predict the production quality from process variables, it could be extended to models of a different nature
Summary
Industrial (bio)chemical production facilities have to be carefully monitored and controlled to guarantee consistent turnover of high-quality product that meets customer wishes. Multivariate latent variable-based methods, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), are commonly used to extract valuable process-specific knowledge from historical production data (Kourti and MacGregor, 1995). Such methods statistically model and identify relationships between physical process variables, such as temperatures, pressures and flow rates, and the production quality of the plant. The information obtained from a multivariate regression model may complement process understanding obtained from an engineering point of view, as it represents the actual operation of the plant closer than the intended operation as designed The use of these models is not limited to analysing historical data only. In cases where the product quality is costly or difficult to measure frequently, they can for instance be used as a soft-sensor to predict that product quality from process measurements that are readily available at high frequency (Lin et al, 2007)
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