Henk-Jan Ramaker

Critical evaluation of approaches for on-line batch process monitoring

Since the introduction of batch process monitoring using component models in 1992, different approaches for statistical batch process monitoring have been suggested in the literature. This is the first evaluation of five proposed approaches so far. The differences and similarities between the approaches are highlighted. The derivation of control charts for these approaches are discussed. A control chart should give a fast and reliable detection of disturbances in the process. These features are evaluated for each approach by means of two performance indices. First, the action signal time for various disturbed batches is tested. Secondly, the probability of a false warning in a control chart is computed. In order to evaluate the five approaches, five different data sets are studied: one simulation of a batch process, three batch processes obtained from industry and one laboratory spectral data set. The obtained results for the performance indices are summarised and discussed. Recommendations helpful for practical use are given.

Improved monitoring of batch processes by incorporating external information

Abstract In this paper an overview is given of statistical process monitoring with the emphasis on batch processes and the possible steps to take for improving this by incorporating external information. First, the general concept of statistical process monitoring of batches is explained. This concept has already been shown to be successful according to the number of references to industrial applications. The performance of statistical process monitoring of batch processes can be enhanced by incorporating external information. Two types of external information can be distinguished: batch-run specific and process specific information. Various examples of both types of external information are given. Several ideas of how to incorporate the external information in model development are discussed. The concept of incorporating process specific information is highlighted by an example of a grey model. This model is applied to a biochemical batch process that is spectroscopically monitored.

Near-infrared spectroscopic monitoring of a series of industrial batch processes using a bilinear grey model

A good process understanding is the foundation for process optimization, process monitoring, end-point detection, and estimation of the end-product quality. Performing good process measurements and the construction of process models will contribute to a better process understanding. To improve the process knowledge it is common to build process models. These models are often based on first principles such as kinetic rates or mass balances. These types of models are also known as hard or white models. White models are characterized by being generally applicable but often having only a reasonable fit to real process data. Other commonly used types of models are empirical or black-box models such as regression and neural nets. Black-box models are characterized by having a good data fit but they lack a chemically meaningful model interpretation. Alternative models are grey models, which are combinations of white models and black models. The aim of a grey model is to combine the advantages of both black-box models and white models. In a qualitative case study of monitoring industrial batches using near-infrared (NIR) spectroscopy, it is shown that grey models are a good tool for detecting batch-to-batch variations and an excellent tool for process diagnosis compared to common spectroscopic monitoring tools.

Manufacturing vaccines: An illustration of using PAT tools for controlling the cultivation of Bordetella pertussis

ABSTRACT An illustration of the operational consistency of the upstream part of a biopharmaceutical process is given. For this purpose four batch cultivations of Bordetella pertussis have been executed under identical conditions. The batches have been monitored by means of two fundamentally different process sensors. First, common single channel measurements such as temperature, pH, dissolved oxygen (DO), and flow rates are used and second, the multichannel measurements from the NIR (Near Infrared) analyzer. Because of the fundamental differences between the two types of measurements, two models have been developed to evaluate the operational consistency. The last sensor studied is a typical representative of process analyzers which are described in the PAT (Process Analytical Technology) guidance document issued in 2004 by the American Food and Drug Administration (FDA). Data from both sensors have been evaluated by a multivariate tool for data acquisition. This resulted in two different performance models. Again this approach is characteristic for the implementation of PAT for the manufacture of biopharmaceuticals. With both performance models, we were able to explore the operational consistency of the batches. In addition, the performance models were also able to detect a deviating batch. Further, it was shown that both sensor types gave partly overlapping information since a deviation in the batch profiles of the logged process variables was accompanied by a deviation in the spectral batch profiles. The performance models are valuable tools in developing advanced monitoring and control systems for biopharmaceutical processes. Using such models, advanced knowledge based systems can be developed to detect abnormal situations in an early stage and remove the cause. The procedure of data processing described in this article is relatively new in the biopharmaceutical industry. The NIR analyzer and both performance models presented in this article are clear ingredients for better process understanding and process control, as intended in the FDAs PAT Initiative. This initiative is part of the FDAs strategy of cGMP (current good manufacturing practice) for the 21st century and aims at introducing innovations in both the manufacturing of biopharmaceuticals and the development of new biopharmaceuticals. This study shows the feasibility of two typical PAT tools for controlling the manufacturing of biopharmaceuticals. To the best of our knowledge such feasibility study is not documented up to now in the scientific literature.

Pocket-Size Near-Infrared Spectrometer for Narcotic Materials Identification

While significant progress has been made towards the miniaturization of Raman, mid-infrared (IR), and near-infrared (NIR) spectrometers for homeland security and law enforcement applications, there remains continued interest in pushing the technology envelope for smaller, lower cost, and easier to use analyzers. In this paper, we report on the use of the MicroNIR Spectrometer, an ultra-compact, handheld near infrared (NIR) spectrometer, the, that weighs less than 60 grams and measures < 50mm in diameter for the classification of 140 different substances most of which are controlled substances (such as cocaine, heroin, oxycodone, diazepam), as well as synthetic cathinones (also known as bath salts), and synthetic cannabinoids. A library of the materials was created from a master MicroNIR spectrometer. A set of 25 unknown samples were then identified with three other MicroNIRs showing: 1) the ability to correctly identify the unknown with a very low rate of misidentification, and 2) the ability to use the same library with multiple instruments. In addition, we have shown that through the use of innovative chemometric algorithms, we were able to identify the individual compounds that make up an unknown mixture based on the spectral library of the individual compounds only. The small size of the spectrometer is enabled through the use of high-performance linear variable filter (LVF) technology.

Batch process monitoring using on-line MIR spectroscopy

Many high quality products are produced in a batch wise manner. One of the characteristics of a batch process is the recipe driven nature. By repeating the recipe in an identical manner a desired end-product is obtained. However, in spite of repeating the recipe in an identical manner, process differences occur. These differences can be caused by a change of feed stock supplier or impurities in the process. Because of this, differences might occur in the end-product quality or unsafe process situations arise. Therefore, the need to monitor an industrial batch process exists. An industrial process is usually monitored by process measurements such as pressures and temperatures. Nowadays, due to technical developments, spectroscopy is more and more used for process monitoring. Spectroscopic measurements have the advantage of giving a direct chemical insight in the process. Multivariate statistical process control (MSPC) is a statistical way of monitoring the behaviour of a process. Combining spectroscopic measurements with MSPC will notice process perturbations or process deviations from normal operating conditions in a very simple manner. In the following an application is given of batch process monitoring. It is shown how a calibration model is developed and used with the principles of MSPC. Statistical control charts are developed and used to detect batches with a process upset.

Single channel event (SCE) for managing sensor failures in MSPC

This paper makes use of the single channel event (SCE) index for managing sensor failures. The SCE index provides prior information how and if a sensor failure is detected in multivariate SPE and D control charts. Furthermore, the SCE index can be used as a diagnostic tool for multivariate monitoring schemes of industrial processes. These features of the SCE index attribute to improved abnormal situation management. The usage of the SCE index is demonstrated for the Tennessee Eastman continuous process.

Monitoring Batch Processes Using External Information

Abstract Batch process monitoring is usually performed based on empirical models of batch process data obtained from normal operation batch-runs. Such models may be improved by incorporating external information explicitly. Two types of external information are discussed: batch-run specific and process specific information. For both types of information, available methods are discussed. Moreover, directions are given for further research.