An intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring multivariable ...

Multivariate statistical process monitoring (MSPM), contribution plots, and parity space fault diagnosis (FD) techniques are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. ...

A morphologically structured model is proposed to describe penicillin production in fed-batch cultivations. The model accounts for the effects of dissolved oxygen on cell growth and penicillin production and variations in ...

Supervision of batch bioprocess operations in real-time during the progress of a batch run offers many advantages over end-of-batch quality control. Multivariate statistical techniques such as multiway partial least squares ...

Real-time supervision of batch operations during the progress of a batch run offers many advantages over end-of-batch quality control. Process monitoring, quality estimation, and fault diagnosis activities are automated ...

Slight changes in raw material properties or operating conditions during critical periods of operation of batch and semi-batch polymerization reactors may have a strong influence on reaction mechanism and impact final ...

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 ...

This study focuses on performance assessment and monitoring of model predictive control systems. A methodology is proposed to determine a benchmark and monitor model predictive control performance on-line. A performance ...

Two types of nonlinear feedback control schemes are introduced and analyzed for their capability of recovering the original state of an isothermal continuous-flow stirred tank reactor with one robust cubic autocatalytic ...

The monitoring of intermediate phases of production is as important as monitoring and control of the final stage. Here a framework is proposed for monitoring overall process performance at the end of each batch.