Retrospective study on performance of constancy check device in Linac beam monitoring using Statistical Process Control

AimTo examine the application of Statistical Process Control (SPC) and Ishikawa diagrams for retrospective evaluation of machine Quality Assurance (QA) performance in radiotherapyBackgroundSPC is a popular method for supplementing the performance of QA techniques in healthcare. This work investigates the applicability of SPC techniques and Ishikawa charts in machine QA.Materials and MethodsSPC has been applied to recommend QA limits on the particular beam parameters using the QUICKCHECK^webline QA portable constancy check device for 6 MV and 10 MV flattened photon beams from the Elekta Versa HD linear accelerator (Linac). Four machine QA parameters – beam flatness, beam symmetry along gun target direction and left-right direction, and beam quality factor (BQF) – were selected for retrospective analysis. Shewhart charts, Exponentially Weighted Moving Average (EWMA) charts and Cumulative Sum (CUSUM) charts were obtained for each parameter. The root causes for a failure in machine QA were broken down into an Ishikawa diagram enabling the user to identify the root cause of error and rectify the problem subsequently.ResultsShewhart charts and EWMA charts applied could detect loss in control in one variable in the 6 MV beams and in all four variables in 10 MV beams. CUSUM charts detected offsets in the readings. The Ishikawa chart exhaustively included the possible errors that lead to loss of control.ConclusionSPC is proven to be effective for detection of loss in control in machine QA. The Ishikawa chart provides the set of probable root causes of machine error useful while troubleshooting.     

Implementing Process Analytical Technology for the Production of Recombinant Proteins in Escherichia coli Using an Advanced Controller Scheme

The performance of a bioreactor in meeting process goals is affected by the microorganism used, medium composition, and operating conditions. A typical bioreactor uses a supervisory control and data acquisition (SCADA) system for control, and a combination of software and hardware tools for real‐time data analysis. However, when the process is disrupted by utility or instrumentation failure, typical process controllers may be unable to reinstate normal operating conditions before the cells in the reactor shift to unfavorable metabolic regimes. The objective of this study is to examine how the response of a controller affects process recovery when disruptive incidences occur under a process analytical technology (PAT) framework. The process used for this investigation is the production of lethal toxin‐neutralizing factor (LTNF) by Escherichia coli (E. coli), which is controlled by a decoupled input–output‐linearizing controller (DIOLC). The performance of the DIOLC is compared to a proportional integral derivative (PID) controller subjected to the same conditions. The disruptions are introduced manually and the effect of controller action on process recovery and LTNF synthesis is measured in terms of peak purity and concentration. It is observed that DIOLC performs better after reinstating operating conditions and results in a meaningful improvement in performance.