Mark-John Bruwer

A Framework for the Development of Design and Control Spaces

We present a framework for the development of design and control spaces that simultaneously considers the raw material property space (Z), the critical to quality process variable space (X), and the critical quality attribute space (Y). The importance of jointly defining all of these spaces and simultaneously considering the eventual process feedforward–feedback control system is illustrated. It is shown that changes in any one of these spaces or in the control system will greatly affect the other spaces. Justification is provided for the use of multivariate principal component analysis and projection to latent structures methods to define more meaningful raw material design spaces and the use of statistical process control concepts to redefine control spaces.

Modeling and Optimization of a Tablet Manufacturing Line

This paper investigates an approach to modeling and optimizing an industrial tablet manufacturing line for different API and excipient formulations. Multi-block partial lease square (PLS) models are built from historical data on a given class of drug products. The data blocks consisted of data on the mass fractions of API and 11 excipients used in the different formulations, the roller compaction process variables, the tablet press settings and the measured final product quality attributes (tablet weight, hardness, and disintegration time). More than 400 runs are used in the modeling. The multi-block PLS models are first used to show which process blocks and which variables in each of the process blocks are most influential on the product quality variables. An optimization is then performed in the latent variable space of the PLS model to find the optimal combination of settings to use for the critical to quality roller compaction and tablet press variables in order to achieve the desired final tablet properties for a specified drug formulation. This optimization can be used to set up the tableting line prior to running a new formulation or can be used in an on-line mode for making small corrections to the operation of the tablet presses in response to small variations in formulations, raw material properties, and roller compaction operation.

Latent Variable MPC for trajectory tracking in batch processes: Role of the model structure

The Multiphase Latent Variable Model Predictive control (MLV-MPC) is developed based on the Principal component analysis (PCA) model. The proposed control methodology is capable of trajectory tracking as well as disturbance rejection. The model that is used in the course of MPC is a multiphase PCA model that is constructed based on the available data from the measurements on the process. Different data arrangements are studied and their effects on the performance of the control algorithm are evaluated.

A novel approach for EIT regularization via spatial and spectral principal component analysis.

Electrical impedance tomography, EIT, is an imaging modality in which the internal conductivity distribution of an object is reconstructed based on voltage measurements on the boundary. This reconstruction problem is a nonlinear and ill-posed inverse problem, which requires regularization to ensure a stable solution. Most popular regularization approaches enforce smoothness in the inverse solution. In this paper, we propose a novel approach to build a subspace for regularization using a spectral and spatial multi-frequency analysis approach. The approach is based on the construction of a subspace for the expected conductivity distributions using principal component analysis. It is shown via simulations that the reconstructed images obtained with the proposed method are better than with the standard regularization approach. Using this approach, the percentage of misclassified finite elements was reduced up to twelve fold from the initial percentages after five iterations. The advantage of this technique is that prior information is extracted from the characteristic response of an object at different frequencies and spatially across the finite elements.