Control systems analytical examples

A prominent feature of applied geochemical analysis is the regular use of control methods to ensure that a satisfactory level of accuracy and precision is maintained from batch to batch of samples. The methods are based on the planned use of standard samples and/or duplication, as much as 5—10% of the analyses being devoted to the control system. For example, in a batch of 300 samples, it would be usual to insert 5 each of 2 standard samples (representing low and medium levels of the analyte), 10 samples (selected at random) in duplicate, and 10 reagent blanks. The control samples should be inserted at random positions in the analytical sequence. While the statistical principles behind the control methods are elementary, their application needs a considerable amount of close attention to ensure that realistic (rather than optimistically biassed) results are obtained. 

Detecting known substances, and determining their quantity, is also important. In synthetic research, it is essential to know the relative proportions of various reaction products. In manufacturing, it is important to detect any impurities in the product and to determine whether they are present in a significant amount. Analytical characterization is critical in pharmaceutical products, for instance. Products for practical uses—paint or adhesives, for example—will typically consist of several components. For proper and reliable performance it is important to measure the amounts of each of the components as part of a manufacturing quality control system. Manufacturers also commonly need to analyze the raw materials they receive, measuring the amounts of various substances in them to be sure that the material meets their requirements. Before it can be correctly processed into steel, iron ore must be analyzed to determine how much of other components need to be added to produce a metal alloy of the desired composition and properties. 

The analytical approach and quality control system has been successfully applied in geochemical mapping projects. During the past 15 years, approximately 50,000 samples have been analysed for Pt and Pd. Some examples of applications are as follows ... 

The analytical solution of the Smoluchowski equation for a Coulomb potential has been found by Hong and Noolandi [13]. Their results of the pair survival probability, obtained for the boundary condition (11a) with R = 0, are presented in Fig. 2. The solid lines show W t) calculated for two different values of Yq. The horizontal axis has a unit of r /D, which characterizes the timescale of the kinetics of geminate recombination in a particular system For example, in nonpolar liquids at room temperature r /Z) 10 sec. Unfortunately, the analytical treatment presented by Hong and Noolandi [13] is rather complicated and inconvenient for practical use. Tabulated values of W t) can be found in Ref. 14. The pair survival probability of geminate ion pairs can also be calculated numerically [15]. In some cases, numerical methods may be a more convenient approach to calculate W f), especially when the reaction cannot be assumed as totally diffusion-controlled. 

The use of chemical analysis to monitor the quality of the raw materials or finished products of industrial processes goes back a long way.313,314 Indeed, some techniques owe their development to the need of industry for rapid analytical techniques. However, analytical methods are now often intimately bound up with the production itself, and supply much of the information required for the control and regulation of the process.315 A good example of a continuous monitoring technique that can be used in process control is that of electrodeless conductivity measurement its history has been described.316 A history of early industrial pH measurement and control systems has been given.317... 

It is clear that the manual preparation and continual updating of the charts shown in Fig. 2 for a multilevel, multi-analyte quality control system involves a great deal of work. However, it is possible in a multilevel control system to represent all individual values at different levels on one chart which is a variant of the Shewhart mean plot. The difference of an individual value (e.g. from the target mean (x ) is divided by the target standard deviation (sQ and thus the position of the individual value is represented relative to the target mean in standard deviation intervals 1), see Fig. 1. The bias of each value, irrespective of its analyte concentration, is therefore represented on the same standard deviation scale. This is very convenient for manual and computer plotting as complex scaling is avoided. Fig. 4 shows an example of this... 

The computer systems used within each of these levels tend to have common characteristics that will influence their validation. For instance, measurement and control systems are generally configurable Commercial Off-The-Shelf (COTS) instruments. Examples include control instrumentation, analytical instrumentation, and medical devices including blood processing systems. 

The type and degree of validation of a computerized analytical system depends on its complexity. For example, the functions of a simple, computer-controlled system, with little or no flexibility regarding data input or evaluation, can be verified by execnting holistic tests and by comparing the test results with anticipated results. On the other hand, a more complicated compnterized system with on-line databases and extensive flexible data evalnation requires complex validation. 

Integrated computerized analytical systems, for example chromatographic systems with computer software for instrument control, data acquisition and data evaluation. Data are printed and electronically stored. Sometimes these computer systems employ spreadsheet programs or user-contributed Macros for customized data evaluation. 

The sample workup necessary for pesticide residue analysis will vary with each combination of analyte and antibody, each of which may have a different tolerance for the matrix and other factors. The effects of these factors must be considered as with the development of any other analytical technique. Matrix effects for one ELISA system are summarized in Figure 4. While the effect of the matrix on the antibodies in Figure 4 is different for each antibody-solvent-matrix combination, the competitive ELISA standard curves for most of these combinations are similar when expressed as percent of the appropriate control. Some systems may not require extensive adjustment, but this must be tested with each individual system. For example, our molinate assay performs equally well in a variety of water types at high concentrations of molinate (Figure 5). The small difference seen between the buffer and water standard curves in Figure 5 was eliminated by the addition of small amounts of concentrated buffer to water samples to equalize them to the buffer composition. 

In many cases, the identity of the analyte will be known nonetheless, it is highly desirable that this be confirmed to avoid the possibility that an interfering compound fortuitously has, for example, the same GC or HPLC retention time as that of the desired analyte. Indeed, many protocols that are now advocated use mass spectrometric systems so that this control is automatically incorporated. Samples may be spiked with internal standards to simplify calculation and eliminate small errors in pipetting and injection, or surrogate standards may be employed where, for example, incomplete extraction of the analyte is unavoidable. When MS is used as the detection system, analytes labeled with suitable isotopes have been widely used for PAHs, fully deuterated standards, and for PCBs and agrochemicals, Relabeled compounds. For partially labeled standards of analytes, care must be exercised in their choice if it is intended to analyze for metabolites of a substrate in which the label may have been lost. 

The automation of sample collection and treatment in gas chromatography has had a less extensive development than in HPLC. Some of the systems described above can be used in GC by introducing slight modifications if liquid samples are to be used. Thus, a continuous unsegmented liquid-liquid extraction system was recently developed for the determination of water pollutants [2B]. Below are discussed two commercial systems as examples of automation prior to introduction of the sample into a gas chromatograph in dealing with two analytical problems control of environmental pollution and analysis for volatile compounds in solid or semi-solid samples. 

The more analytical tools that are available and the better the understanding of critical biochemical pathways, the more rapidly fermentation processes can be developed. Besides those previously mentioned, a munber of different parameters have been monitored on-line in fermentation development [7], including exhaust gas analysis and gas fluxes [46], cell density [47], redox potential [48], IR [49], culture fluorescence [50], biological activities [45 ], and viscosity. It is important to iterate that small-scale fermentation studies should aim to develop relatively simple control systems that are easily scaled. As an example, although HPLC systems are routinely set-up on line to measure and control laboratory scale fermentations, the robustness of such a system and its utility in a manufacturing facility remains debatable. 

Tunable gas-phase lasers are expensive. Less expensive solid-state diode lasers with wavelengths in the NIR are available. Commercial instruments using multiple diode lasers are available for NIR analyses of food and fuels. Because of the narrow mission lines from a laser system, laser sources are often used in dedicated applications for specific analytes. They can be ideal for process analysis and product quality control (QC), for example, but are not as flexible in their applications as a continuous source or a tunable laser. 

On the other hand, when more than one fault can influence the system at the same time, advanced diagnostic methods are used. These methods are based on parameter estimation. Sensitivity bond graph formulation [12] allows real-time parameter estimation and thus it is possible not only to isolate multiple faults but also to quantify the fault severities. Parameter estimation in single fault [2] or multiple fault scenarios [12] are essential steps to be performed before fault accommodation. The parameter estimation scheme also gives the temporal evolution of system parameters. Thus, it is possible to identify and quantify different kinds of fault occurrences. A progressive fault shows gradual drift in estimated parameter values and intermittent fault shows spikes in the estimated parameter values. The advances made in the field of control theory have made it possible to develop state and parameter estimators for various classes of nonlinear systems. Analytical redundancy relations may also be used in optimization loop for parameter estimation because it avoids the need for state estimation. Interested readers may see Ref. [3] for further details and some solved examples. 

---