Process Monitoring, Modeling and Control

Processing intelligent What is needed is to cut inefficiency, such as the variables, and in turn cut the costs associated with them. One approach that can overcome these difficulties is called intelligent processing (IP) of materials. This technology utilizes new sensors, expert systems, and process models that control processing conditions as materials are produced and processed without the need for human control or monitoring. Sensors and expert systems are not new in themselves. 

As was pointed out, the most important drawback in the operation of AD processes is related to the instability of the process. However, this drawback can be overcome by associating monitoring procedures with decision support systems that allow the on-line stable operation of the process via a feedback control loop [27, 33]. Nowadays, most of the monitoring and control techniques available in the literature belong to those called model-based . Such... 

In this work, the AD model proposed and validated by Bernard et ah, 2001 is used in the development of the robust nonlinear scheme. The use of this model is justified by two facts i) this model has demonstrated to be useful in the monitoring and control of AD processes and, ii) the semi-industrial fixed-bed anaerobic digester located in the LBEhlNRA used in the validation and identification of the model will be also used in the experimental implementation of the robust nonlinear approach here proposed. Therefore, in what follows the model is briefly described. 

Inferential sensors, also known as soft sensors, are models that nse readily measurable variables to determine product properties critical to prediction of prodnct/process qnafity. Ideally the soft sensors are continuously monitored and controlled, or moiutored on a relevant time scale. They need to make predictions quickly enough to be used for feedback control to keep process variability to a minimum. 

An example of CQV of the batch cultivation of a vaccine has been demonstrated, where univariate (temperature, dissolved oxygen, pH) as well as spectroscopic tools were used to develop process models. The measurements were used for a consistency analysis of the batch process, providing better process understanding which includes the understanding of the variations in the data. MSPC analysis of four batches of data was performed to monitor the batch trajectories, and indicated that one batch had a deviation in the pH. From the MSPC information, combined with calibration models for the composition of the process based on NIR spectral data, improved monitoring and control systems can be developed for the process, consistent with concept of CQV. The data from the univariate sensors and NIR were also fused for a global analysis of the process with a model comprised of all the measurements. 

High cell densities are not only a prerequisite for high productivity additionally an effective on-line control and modeling of the bioprocesses is necessary. For industrial applications, optical measurement methods are more attractive because they are non-invasive and more robust. The potential of the BioView sensor for on-line bioprocess monitoring and control was tested. For high-cell-density cultivation of Escherichia coli, maintaining aerobic conditions and removal of inhibitory by-products are essential. Acetic acid is known to be one of the critical metabolites. Information about changes in the cell metabolism and the time of important process operations is accessible on-line for optimization... 

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