Improving the Robustness of Particle Size Analysis by Multivariate Statistical Process Control

Abstract

The robustness of online particle size analysis in wet processes is improved by applying data based modeling methods to the control of the sample preparation and measurement sequence of the particle size analyzer. The aim is to find a more accurate and reliable method of determining the end of the particle size integration period using multivariate statistical process control (MSPC).The studied approach is tested on analyzers installed at two mineral processing plant sites and validated using two validation tests. Research shows that the proposed method works with two very different slurry types. The main advantage of the adapted approach is that there are no adjustable parameters that have to be set by the user.

A PAT Approach to Enhance Process Understanding of Fluid Bed Granulation Using In-line Particle Size Characterization and Multivariate Analysis

“…Theory of multivariate batch statistical process modeling has been widely discussed in literature, and is still evolving [25][26][27][28][29][30]. There are two common batch modeling approaches, depending on how batch data is organized prior to principal component analysis (PCA) or partial least squares (PLS).…”

“…In recent years, especially after FDA's Process Analytical Technology initiative, the pharmaceutical industry has seen more and more applications of multivariate tools such as multivariate/batch statistical process control (MSPC/ BSPC) [19][20][21][22][23][24][25]. Multivariate tools are becoming increasingly essential to extract useful information from complex data generated by process analyzers.…”

Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control

“…To handle and interpret the measurements of the sensors above in a process monitoring context, MSPC is a well-established methodology for statistical process control and fault diagnosis and identification [9,10]. However, MSPC tends to be used either on the original multivariate sensor information, e.g., spectroscopic information [1,[11][12][13] or on sets of univariate process sensors, e.g., temperature, flow, etc. [14][15][16][17], but the combination of both kinds of sensors is seldom found.…”

In-Depth Evaluation of Data Collected During a Continuous Pharmaceutical Manufacturing Process: A Multivariate Statistical Process Monitoring Approach

“…Batch SPM (BSPM) is the extension of MSPM suitable for the monitoring of batch processes. 12 Several examples of the application of MSPM or BSPM can be found in literature for the monitoring of analytical methods, 13,14 bioprocesses, 15,16 chemical processes, 10,17,18 and processes from several other industries such as the food, 19 paper, 20 metallurgical, 21 and petrochemical 22 industries, among others. However, few reports exist for MSPM/BSPM of pharmaceutical production processes.…”