Monitoring Chemical Processes - Sponsored Whitepaper

Multivariate Statistical Process Monitoring (MSPM) has been established as a valuable tool for ensuring reliable product quality in the process industry. However,many organizations today are still not fully utilizing its potential to make significant improvements in their production environment. The MSPM approach to process monitoring involves the use of multivariate models to simultaneously capture the information from as few as two process variables, up to thousands. The methodology provides means for increased process understanding, fault detection and on-line prediction, all typical tasks for the process engineer and production manager.

With MSPM approaches, it is possible to not only control the final product quality data, but also all of the available process variable data in terms of the underlying systematic variations in the process. The variables measured in a process are often correlated to a certain extent, e.g. when several tempertures are measured in a distillation column. This means that the events or changes in a process can be visualized in a smaller subspace that may give a direct chemical or physical interpretation. If we want to keep such a process ”in control”, traditional univariate control charts – due to the covariance or interaction between variables - may not assure this efficiently. Figure 1 exemplifies a situation where two process variables are both inside their univariate control limits given as two standard deviation, but fails to detect that the general trend of correlation between these two variables is broken for the sample shown in red.

The most frequently applied methods are Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). The PCA answers the question “Is the process under control?” but does not provide a Figure 1 Illustration why multivariate methods are needed for process monitoring quantitiave model for the final product quality. Typical applications of PCA for this purpose are raw materal identification , adulteration in the food industry, counterfiet products, and on-line testing of product quality. . The PLS regression provides in addition to the monitoring aspect, also quantitative prediction of the final product quality based on all or a subset of the process variables. One vital aspect in this context is to reduce the off-line laboratory work, both to have the prediction at an early stage as the product properties are not available on-line, and to reduce the labour-intensive work.

Critical statistical limits can be derived from the empirical data chosen to establish a model when the process is under control. One limit is based on the space defined by the model, the so-called Hotelling T2 statistic. Thus, this statistic indicates if there is too high or too low concentration of the quality variable of interest. The other limit is based on the distance to the model, meaning there is something new e.g. there is a change in the raw material. We refer to Jackson (ref. 1) for a detailed description.

Multivariate statistical methods are also excellent tools to develop processes further. With these methods we can look inside the process to gain the necessary information for optimising them (PLSR).