Online Batch/Fed-Batch Process Performance Monitoring, Quality Prediction, and Variable-Contribution Analysis for Diagnosis

Abstract

An integrated online multivariate statistical process monitoring (MSPM), quality prediction, and fault diagnosis framework is developed for batch processes. Batch data from I batches, with J process variables measured at K time points generate a three-way array of size I × K × J. Unfolding this three-way array into a two-way matrix of size IK × J by preserving the variable direction is advantageous for developing online MSPM methods because it does not require estimation of future portions of new batches. Two different multiway partial least squares (MPLS) models are developed. The first model (MPLSV) is developed between the data matrix (IK × J) and the local batch time (or an indicator variable) for online MSPM. The second model (MPLSB) is developed between the rearranged data matrix in the batch direction (I × KJ) and the final quality matrix for online prediction of end-of-batch quality. The problem of discontinuity in process variable measurements due to operation switching (or moving to a different phase) that causes problems in alignment and modeling is addressed. Control limits on variable contribution plots are used to improve fault diagnosis capabilities of the MSPM framework. Case studies from a simulated fed-batch penicillin fermentation illustrate the implementation of the methodology.

Chemometrics applications in biotech processes: A review

“…20,21 They include analysis of NIR spectral information for an antibiotic production process, multivariate statistical process monitoring for processing of pharmaceutical granules, the assessment of seed inoculum quality from a manufacturing process, and development of an integrated on-line multivariate statistical process monitoring, product attributes prediction, and fault diagnosis framework for a fed-batch penicillin fermentation. [22][23][24][25] A flexible process monitoring method has been applied for analysis of pilot plant cell culture data for fault detection and diagnosis. 26 A PCA model was constructed from 19 batches, and the model was shown to successfully detect abnormal process conditions and diagnose root causes.…”

Process analytical technology (PAT) for biopharmaceutical products: Part II. Concepts and applications

“…It was shown that changes in the spectral information that correspond to variations in the bioprocess can be identified and that the loadings and score plots can assist with process diagnosis and rapid assessment of process performance. Undey et al (2003) developed an integrated on-line multivariate statistical process monitoring, quality prediction, and fault diagnosis framework for batch processes and applied it on simulated fed-batch penicillin fermentation. They found that unfolding the three-way data array by preserving the variable direction allowed on-line monitoring without requiring future value estimation.…”

Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization

“…A fault diagnosis system in which an artificial neural network structure and a knowledge-based expert system are combined was put forward for batch chemical plants by Ruiz et al [13]. Undey et al [14] developed a framework that consists of the multivariate statistical monitoring, fault diagnosis and quality prediction for batch processes. In Ref.…”