Understanding chemical production processes by using PLS path model parameters as soft sensors

•PLS path models are a viable method to analyse chemical production processes.•Including process knowledge into the modelling framework revealed previously unknown relationships.•Relating model parameters to batch-specific descriptors resulted in a new type of soft-sensor.•Path models based on PLS can be used to intuitively communicate between operator and researcher. To make industrial processes lean, inclusion of technical process information is required into statistical modelling. Understanding how parts of a process are related to other parts and to output quality is key to understanding and controlling processes. In this work, we show how PLS path modelling can be used to incorporate process knowledge into predictive chemical process analysis. The result is a wealth of information which is not obtained by standard data analytic techniques commonly used by analytical chemists or process engineers. By comparing model parameters across multiple data sets from different batches of the same process, model parameters could be used as soft sensors. Some variables which would normally be discarded as uninformative were highly predictive of production costs. The methodology reported here improves chemical process understanding through the analysis of complementary historical process data, which may serve as the basis for development of improved process conditions and control. [Display omitted]