A data-driven process control strategy aligned with quality by design using a local linear modelling method and fault detection & diagnosis for twin-screw granulation

The commercial implementation of continuous granulation requires an intelligent control framework compliant with Quality by Design (QbD) principles. To satisfy regulatory requirements this 'control system' cannot simply vary multiple operational variables simultaneously to regain quality attributes but should recognize the source of a disturbance and make the appropriate correction. A Fault Detection and Diagnosis (FDD) method is proposed as a novel element for such a framework, automating identification of the root cause within a non-linear design space. The disclosed method is considered a first step to realizing a QbD control system by presently assuming only one disturbance can occur at a time to highlight the value of this new approach, not producing a ready-to-use product. The design space for twin-screw granulation was modelled using linear Partial Least Squares (PLS) with data collected using the Prediction Reliability Enhancing Parameter (PREP) method, which navigates non-linear data, producing a more comprehensive dataset. Particle size distribution (PSD) was the primary output used for assessing the granulation process. To address the inherently complex design space, local models were dynamically generated around the operation/disturbance point to reduce the error of fit for the FDD algorithm. Fault detection involved the verification of a disturbance and perturbation of a verified input whereas the fault diagnosis phase employed an optimization framework comparing observed and predicted PSDs associated with the deviations. This design helps overcome the interdependent behavior of process inputs and enables systematic isolation of the correct root cause. The algorithm is evaluated in this study by three case studies.