Process chemometrics

There are two basic steps in the above mentioned process monitoring procedure. In phase 1, data from the process under normal operating conditions are obtained. From these data control charts are calculated. In phase 2, the daily operation of the process, measurements are taken and plotted in the control charts. It is then decided whether the process is still working under normal operating conditions or not. The first phase is called the training phase and the second phase the operational or test phase. 

A short description of monitoring continuous processes for deviating behavior will be given, because some parts of batch process monitoring are taken from that area. Monitoring batch processes is discussed in detail as three-way analysis is heavily involved in that area. 

In statistical process control a single quality characteristic is monitored. Multiple quality characteristics are mostly measured simultaneously and these characteristics are typically correlated. Multivariate process monitoring takes this correlation into account. 

Multi-way Analysis With Applications in the Chemical Sciences 

From product quality measurements to process variable measurements 

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Dessy, R. A. "Scientific Word Processing". Chemometrics and Intelligent Laboratory Systems 1987,1, 309-319. 

BM Wise and NB Gallagher. The process chemometrics approach to process monitoring and fault detection. J. Process Control, 6(6) 329-348, 1996. 

ANN is a kind of information processing chemometrical technique. It simu- properties of human brain, and is often applied in the field of regres-... 

Process chemometrics is a field where multi-way methods were introduced more recently. A batch process gives rise to an array of batches followed over time and measured by multiple process sensors such as temperature and pressure sensors or perhaps spectroscopy, as in Figure 10.3. Multi-way component models are used for the construction of control charts and multi-way regression is used for constructing predictive calibration models. 

Dahl KS, Piovoso MJ, Kosanovich KA, Translating third-order data analysis methods to chemical batch processes, Chemometrics and Intelligent Laboratory Systems, 1999, 46, 161-180. 

The tools of chemometrics encompass not only the familiar (univariant) methods of statistics, but especially the various multivariant methods, together with a package of pattern-recognition methods for time-series analyses and all the known models for signal detection and signal processing. Chemometric methods of evaluation have now become an essential part of environmental analysis, medicine, process analysis, criminology, and a host of other fields. 

Sahni NS, Isaksson T, Tormod Naes. The use of experimental design methodology and multivariate analysis to determine critical control points in a process. Chemometrics Intelligent Lab Syst 2001 56 105-121. 

B. M. Wise and B. R. Kowalski, Process Chemometrics, in Process Analytical Chemistry (F. McLen-naan and B. R. Kowalski, eds.), Blackie Academic, London, 1995. 

Proper data processing (chemometrics) requires more consideration. For instance, currently the Chinese Pharmacopoeia uses only relative retention times and sometimes relative ratios (i.e., qualitative information) to evaluate LC profiles. However, the absolute concentrations of components are clearly also of importance for the quality of TCMs. Thus,... 

P. Nomikos, j. F. MacGregor, Multi-way partial least squares in monitoring batch processes , Chemometrics and Intelligent Laboratory Systems, 1995, 30, 97. 

Mortensen PP, Bro R. Real-time monitoring and chemical profiling of a cultivation process. Chemometr Intell Lab Syst 2006 84 106-13. 

The quahty of an analytical result also depends on the vaUdity of the sample utilized and the method chosen for data analysis. There are articles describiag Sampling and automated sample preparation (see Automated instrumentation) as well as articles emphasizing data treatment (see Chemometrics Computer technology), data iaterpretation (see Databases Imaging technology), and the communication of data within the laboratory or process system (see Expert systems Laboratory information managet nt systems). 

In the context of chemometrics, optimization refers to the use of estimated parameters to control and optimize the outcome of experiments. Given a model that relates input variables to the output of a system, it is possible to find the set of inputs that optimizes the output. The system to be optimized may pertain to any type of analytical process, such as increasing resolution in hplc separations, increasing sensitivity in atomic emission spectrometry by controlling fuel and oxidant flow rates (14), or even in industrial processes, to optimize yield of a reaction as a function of input variables, temperature, pressure, and reactant concentration. The outputs ate the dependent variables, usually quantities such as instmment response, yield of a reaction, and resolution, and the input, or independent, variables are typically quantities like instmment settings, reaction conditions, or experimental media. 

The aim of this work is the optimization of distillation process using H SO for fluoride separation and potentiometric determination in anhydrite samples by means of chemometric tools. 

The fluoride amount recovery by Willard-Winter distillation process using a mixture of mineral acids (50 % v/v) was 3.2 0.1 % F. The new analytical conditions using H SO obtained from the chemometric study gave the result of3.3 0.2%F. 

Chemometrics, in the most general sense, is the art of processing data with various numerical techniques in order to extract useful information. It has evolved rapidly over the past 10 years, largely driven by the widespread availability of powerful, inexpensive computers and an increasing selection of software available off-the-shelf, or from the manufacturers of analytical instruments. 

Multiway and particularly three-way analysis of data has become an important subject in chemometrics. This is the result of the development of hyphenated detection methods (such as in combined chromatography-spectrometry) and yields three-way data structures the ways of which are defined by samples, retention times and wavelengths. In multivariate process analysis, three-way data are obtained from various batches, quality measures and times of observation [55]. In image analysis, the three modes are formed by the horizontal and vertical coordinates of the pixels within a frame and the successive frames that have been recorded. In this rapidly developing field one already finds an extensive body of literature and only a brief outline can be given here. For a more comprehensive reading and a discussion of practical applications we refer to the reviews by Geladi [56], Smilde [57] and Henrion [58]. 

A more recently introduced technique, at least in the field of chemometrics, is the use of neural networks. The methodology will be described in detail in Chapter 44. In this chapter, we will only give a short and very introductory description to be able to contrast the technique with the others described earlier. A typical artificial neuron is shown in Fig. 33.19. The isolated neuron of this figure performs a two-stage process to transform a set of inputs in a response or output. In a pattern recognition context, these inputs would be the values for the variables (in this example, limited to only 2, X and x- and the response would be a class variable, for instance y = 1 for class K and y = 0 for class L. 

A homogeneity index or significance coefficienf has been proposed to describe area or spatial homogeneity characteristics of solids based on data evaluation using chemometrical tools, such as analysis of variance, regression models, statistics of stochastic processes (time series analysis) and multivariate data analysis (Singer and... 

The amount of information, which can be extracted from a spectrum, depends essentially on the attainable spectral or time resolution and on the detection sensitivity that can be achieved. Derivative spectra can be used to enhance differences among spectra, to resolve overlapping bands in qualitative analysis and, most importantly, to reduce the effects of interference from scattering, matrix, or other absorbing compounds in quantitative analysis. Chemometric techniques make powerful tools for processing the vast amounts of information produced by spectroscopic techniques, as a result of which the performance is significantly... 

Until fairly recently, IR spectroscopy was scarcely used in quantitative analysis owing to its many inherent shortcomings (e.g. extensive band overlap, failure to fulfil Beer s law over wide enough concentration ranges, irreproducible baselines, elevated instrumental noise, low sensitivity). The advent of FTIR spectroscopy, which overcomes some of these drawbacks, in addition to the development of powerful chemometric techniques for data processing, provides an effective means for tackling the analysis of complex mixtures without the need for any prior separation of their components. 

Sharaf MA, liman DL, Kowalski BR (1986) Chemometrics. Wiley, New York Stanley WD (1975) Digital signal processing. Reston Publishing, Reston, VA... 

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