The control strategy is based on process understanding, process control through compaction force weight control on the tablet press, periodic checks of mean and individual tablet weight combined with at-line testing of tablet content by near infrared (NIR).
Several approaches based on process design modifications or control strategies are being developed to overcome this problem, but none has as yet been entirely successful.
In this paper, the threshold values would be fixed based on statistical process control (SPC) and statistical process control charts which are used to describe the output characteristics of the process in a coordinate graph.
For optimization-based dynamic control of simulated moving bed (SMB) process, a novel control strategy based on process identification, which is an extension of the earlier work (Song et al., 2006a. Identification and predictive control of a simulated moving bed process: purity control. Chemical Engineering Science 61, 1973 1986), is proposed.
The idea developed and presented in this paper concerns the prediction of the performance of adaptive control policies, based on process modeling.
Our approach uses a common repository for process planning to ensure reusability, specification, instantiation, monitoring, and execution of processes, which are defined using a relational database and rigorous control flow specifications based on process algebra (ACP).
This methodology is based on Statistical Process Control (SPC) and it is encapsulated in a Leakage Location System (LLS) that automatically processes the night-time data recorded, with a one minute frequency, by the DMA's pressure loggers.
An experimental case study is presented to validate the use of model based on-line control of the RTM process and to demonstrate how it lends itself to automation in the manufacturing process.
The analysis, focused on process control purposes, is based on a generic framework, and the extra work needed to generate a dynamic model from a static one is discussed.
