Process analytical technology (PAT), viewed broadly, includes chemical, physical, microbiological, statisti-cal, and risk analysis conducted in an integrated manner used broadly

in batch manufacturing. In particular, multivari-ate statistical modeling (data analytics) serves as an excellent tool designed for batch process analysis. The online implementation of analyt-ic technology comprises fault detection and end of batch quality prediction. The approach offers significant economic benefits; however, imple-mentations face many challenges and so far few have documented online applications.

For batch analytics online to be successful, the follow key areas should be addressed:

n Process holdups: Operator- and event-ini-tiated processing halts and restarts. Sometimes halts and restarts are part of the batch pro-cess design, such as adding a special ingredi-ent. Other times progression of a batch may be delayed by limitations imposed by the need to wait for common equipment to become avail-

able. Holdups data must be accounted for dur-ing analytic model development and in the online application of analytics.

n Access to lab data: Due to the nature of batch processing, online measurement of qual-ity parameters may not be technically feasible or economically justified. Thus, it is common that at various points in the batch a grab sam-ple is taken and analyzed in the lab. To imple-ment online analytics, it is necessary that lab results be available for model development and validation.

n Variations in feedstock: The charge to a batch may come from storage tanks that are periodically refilled by upstream processes, or by truck or rail shipment from outside suppli-ers. Changes in incoming raw material proper-ties directly impact batch operation and quality parameters and should be available for online analytic tools.

Online Go to www.controleng.com/process

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Robert Wojewodka The Lubrizol Corporation

Terry Blevins, Willy Wojsznis Emerson Process Management

Batch Operations Benefit from Process Analytical TechnologyA successful application of batch analytics for online operation, resulting in a solution validated for a specialty chemicals batch process. The discussion illustrates basics of batch analytics operation, including access via a Web interface. (Part 1)

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OnlineFor more information, visit:

www.lubrizol.com

www.emersonprocess.com

Covering control, instrumentation, and automation systems worldwide

MAY 2011

P2 ● MAY 2011 CONTROL ENGINEERING ● www.controleng.com

n Varying operating conditions: The pro-cessing conditions may vary significantly with each batch operation. The batch process should be split on operations performed in similar con-ditions (stages), and an analytic model should be developed for every stage.

n Concurrent batches: Multiple batches of the same product may be executing at various stages of completion.

n Assembly and organization of the data: One of the limiting items which often pre-vents detailed analysis of batch processes is the inability to access, correctly sequence, and organize a data set of all necessary data. This requirement must be fulfilled to analyze the pro-cess and to move the results of that analysis online.

n Data alignment from different batches: Batch durations are not equal. For develop-ing an analytics model, data from the various batches should be aligned, forming data with an equal number of data samples for every batch.

An effective solution to meet batch challeng-es can be found by applying current develop-ments and research in batch modeling, process control systems, and Web technology, and then by considering an implementation strategy based on close cooperation of analytics system developers and end users.

Basics of analytics Analytics may be effectively built on mul-

tivariate statistical methods which have been known from the beginning of the previous cen-tury. Personal computers made practical use of those methods possible in many applica-tions, and in particular, batch analysis. Sever-al accompanied techniques, primary for data unfolding and data alignment, have also been developed for batch analysis implementation. A summary of batch analytics techniques is presented below.

data alignmentTime required to complete one or more oper-

ations associated with a batch may vary because of process holdups or processing conditions. (See Figure 1.) However, the batch data used in model development must be somehow aligned in order to facilitate data analysis.

To achieve uniform batch length, the data at a certain time in the batch could be simply chopped off, compressed, or expanded in some fashion to achieve the same number of time increments. Better results may be achieved by applying a newer technique known as dynamic time warping (DTW). DTW aligns batch data

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Fig.1 - The nature of batch data

Fig. 2 - Application of dynamic time warping

Fig. 3 - Hybrid data unfolding and PCA modeling matrices

with the reference trajectories by minimizing total distance between them. The batch with median time duration can be used as an ini-tial DTW reference batch. The DTW principle for aligning one trajectory is illustrated in Fig-ure 2. To satisfy the minimal distance between trajectories, two or more points on the red tra-jectory (A) can be transformed into one point on the green aligned trajectory, or one point is stretched on many points (B).

data unfoldingThe aligned model data file is a three-dimen-

sional array: I batches, J variables, and K scan periods. (See Figure 3.) Prior to the model development, the data file is unfolded into two dimensions, IKxJ. Hybrid unfolding is a recent improvement over commonly used batch-wise and variable-wise unfolding. With hybrid unfolding, mean values and variances are cal-culated for every time period for batch-wise unfolded data. (See Figure 3, arrow A.) The data is then rearranged as variable-wise unfold-ed (See Figure 3, arrow B.); therefore, there is no need to assume arbitrary trajectories from the current time until the end of the stage, as with the original batch-wise unfolding.

Multivariate statistical methodsProcesses with correlated relationships

require use of multivariate statistical tech-niques. Figure 4 shows that while trends for both correlated parameters are within their respective process control limits, the process operation is still deemed faulty (i.e., the rela-tionship between parameters is broken) for the identified observation, and the fault is detected only by the multivariate statistics.

A primary multivariate statistical meth-od often used is principal component analysis (PCA). At the heart of PCA technology is the concept that a time-based profile for measure-ment values may be established using a variety of batches that produced good quality product and had no abnormal processing upsets. The model may be used to develop a better under-standing of how multivariate parameters relate to one another and how all factors can impact batch-to-batch costs.

The model structure takes into account that many of the measurements used in the batch operation are related to each other and respond in a similar manner to a process input change. In other words, they are collinear. For such con-ditions, all process variations can be modeled by the primary principal components. (Matrix T in Figure 3.) The PCA model may be used

to identify process and measurement faults that may impact product quality.

The modeled part of the process variation is also captured by the Hoteling’s T2 statistic. Uncorrelated variations not included into the principal components are not modeled. They are presented by what is known as the Q sta-tistic. In this way all process variations can be reflected by two indicators. (See Figure 5.) Two bar plots at the bottom display show how particular process parameters contribute to the process variations. For diagnosing a select-ed parameter, operators may look at a param-eter trend plotted along with a reference trend and the acceptable parameter band of variation. (See Figure 6.)

Fig. 4 - Basic concept of fault detection

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Fig. 5 - PCA captures both modeled andunmodeled variations

Through the use of these two statistics, it is possible to determine fault conditions in the batch sooner and thus allow investigations and corrections to be made to counter the impact of the fault.

Projection to latent structures (PLS, also known as partial least squares), is applied to ana-lyze the impact of processing conditions on final-product quality, and can provide operators with

continuous predictions of end-of-batch quality parameters. In some batches, where the objective is to classify the operation results into discrete cat-egories (e.g., fault category, grades, etc.), combin-ing another multivariate statistical method known as discriminate analysis (PLS-DA) would be used in conjunction with PCA and PLS. ce

Coming in Part 2 (July): Steps to a success-ful project implementation.

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