Univariate sensors

The overall objective of the system is to map from three types of numeric input process data into, generally, one to three root causes out of the possible 300. The data available include numeric information from sensors, product-specific numeric information such as molecular weight and area under peak from gel permeation chromatography (GPC) analysis of the product, and additional information from the GPC in the form of variances in expected shapes of traces. The plant also uses univariate statistical methods for data analysis of numeric product information. 

The growing nse of more complex PAT (versus the historically used simple univariate sensors such as pressure, temperature, pH, etc.) within manufacturing industries is driven by the increased capabilities of these systems to provide scientihc and engineering controls. Increasingly complex chemical and physical analyses can be performed in, on, or immediately at, the process stream. Drivers to implement process analytics include the opportunity for live feedback and process control, cycle time reduction, laboratory test replacement as well as safety mitigation. All of these drivers can potentially have a very inunediate impact on the economic bottom line, since product quality and yield may be increased and labor cost reduced. 

Granulation of urea [13] is a complex process that has to be controlled by experienced process operators in order to avoid critical shutdown situations. The parameters most often used for monitoring granulation processes are measured by classical univariate sensors, such as temperature, pressure and flow. However, these standard process measurements carry only little or no relevant information, or are only indirectly related to, for example particle size, clogging of the reactor, or the accumulation of a solids layer on the bottom plate. The response from these sensors often comes with quite a substantial delay time. 

GC has been used for process analysis for many decades, along with many spectroscopic tools and univariate sensors. In recent years, developments in HPLC have made it now also available for on-line monitoring It has the advantage over spectroscopic methods in being able to detect trace levels of compounds, such as... 

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. 

The main advantage of multivariate calibration based on CLS with respect to univariate calibration is that CLS does not require selective measurements. Selectivity is obtained mathematically by solving a system of equations, without the requirement for chemical or instrumental separations that are so often needed in univariate calibration. In addition, the model can use a large number of sensors to obtain a signal-averaging effect [4], which is beneficial for the precision of the predicted concentration, making it less susceptible to the noise in the data. Finally, for the case of spectroscopic data, the Lambert Bouguer Beer s law provides a sound foundation for the predictive model. 

In the era of single-loop control systems in chemical processing plants, there was little infrastructure for monitoring multivariable processes by using multivariate statistical techniques. A limited number of process and quality variables were measured in most plants, and use of univariate SPM tools for monitoring critical process and quality variables seemed appropriate. The installation of computerized data acquisition and storage systems, the availability of inexpensive sensors for typical process variables such as temperature, flow rate, and pressure, and the development of advanced chemical analysis systems that can provide reliable information on quality variables at high frequencies increased the number of variables measured at... 

A bias is added to the reactor conversion measurement with a magnitude of about 5 % of the current reactor conversion (sensor 2). Immediately after the bias change is introduced to the sensor, both the univariate chart for reactor conversion and the multivariate and SPE charts (Figure 8.7) indicate an abnormality. A CVSS model is developed to generate the residuals for sensor audit. The KBS automatically begins the sensor validation routine to determine the source cause of the inflated and SPE statistics. Figure 8.8 shows that the residuals mean for sensors 1 (initiator concentration), 2 (reactor conversion), and 4 (polydispersity) have exceeded... 

Univariate sensor correction gave the best results in our case (table 8.1). 

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