Quality records analysis

The use of various statistical techniques has been discussed (46) for two situations. For standard air quality networks with an extensive period of record, analysis of residuals, visual inspection of scatter diagrams, and comparison of cumulative frequency distributions are quite useful techniques for assessing model performance. For tracer studies the spatial coverage is better, so that identification of meiximum measured concentrations during each test is more feasible. However, temporal coverage is more limited with a specific number of tests not continuous in time. 

Regarding quality records, you will need to be selective and choose for analysis those which will yield some useful data. Don t embark upon a progressive analysis without good reason as it can be a fruitless exercise. 

The oil and gas samples are taken from the appropriate flowlines of the same separator, whose pressure, temperature and flowrate must be carefully recorded to allow the recombination ratios to be calculated. In addition the pressure and temperature of the stock tank must be recorded to be able to later calculate the shrinkage of oil from the point at which it is sampled and the stock tank. The oil and gas samples are sent separately to the laboratory where they are recombined before PVT analysis is performed. A quality check on the sampling technique is that the bubble point of the recombined sample at the temperature of the separator from which the samples were taken should be equal to the separator pressure. 

The principal tool for performance-based quality assessment is the control chart. In a control chart the results from the analysis of quality assessment samples are plotted in the order in which they are collected, providing a continuous record of the statistical state of the analytical system. Quality assessment data collected over time can be summarized by a mean value and a standard deviation. The fundamental assumption behind the use of a control chart is that quality assessment data will show only random variations around the mean value when the analytical system is in statistical control. When an analytical system moves out of statistical control, the quality assessment data is influenced by additional sources of error, increasing the standard deviation or changing the mean value. 

Names and training records of staff involved in sampling and analysis, or details of contract analysts and any quality standards/accreditation schemes with which they comply. 

More sensitive detection methods and more objective recording methods (e g the employment of scanners) are constantly been striven for m order to overcome this illusion It IS for this reason too that fluorescent methods have been introduced to an increasing extent on account of their higher detection sensitivity This allows an appreciable reduction in the amount of sample applied, so that possible interfering substances are also present m smaller quantibes This increases the quality of the chromatographic separation and the subsequent m situ analysis... 

Customer complaints Warranty claims Failure analysis reports Process capability studies Service reports Concessions Change requests Subcontractor assessments Performance analysis Deviations and waivers Contract change records Quality cost data External Quality Audit records... 

In order to demonstrate the achievement of quality and the effectiveness of the quality system, records will need to be gathered in from the locations where they were produced. This is more than a convenience because you will be unable to analyze all the data efficiently unless you need it in front of you. If you are lucky enough to operate a computer network and all the data is available on the network, data collection is a simple affair. However, many organizations still rely on paper records and therefore you will need a means of enabling such records to be either submitted to the analysis points or collected from source. To facilitate the collection of records you will need to insert submission or collection instructions in the relevant procedures which specify the records. 

For a major incident investigation using a comprehensive root cause analysis system, teams will be formed to acquire information relevant to determine the structure and analyze the causes in depth. In addition to evaluations of the immediate causes, imderlying causes are likely to be evaluated by investigations in areas such as safety and quality management. Both paper- and computer-based systems will be used to acquire and record information for subsequent detailed analyses. 

The quality of combustion can be measured with suitable instrumentation, on either a periodic or a continual basis. If continuous analysis is practiced then there may be feedback to continuously adjust the air/gas ratio and/or ratio and/or record the data derived. 

Several statistical, quality management, and optimization data analysis tools, aimed at exploring records of measurements and uncover useful information from them, have been available for some time. However, all of them require from the user a signifieant number of assumptions and a priori decisions, which determine in a very strict manner the validity of the final results obtained. Furthermore, these classical tools are guided... 

Given a set of existing (x, y) data records, where x is a vector of operating or decision variables, which are believed to influence the values taken on by y, y is a performance metric, usually assumed to be a quality characteristic of the product or process under analysis ... 

In this chapter we revisited an old problem, namely, exploring the information provided by a set of (x, y) operation data records and learn from it how to improve the behavior of the performance variable, y. Although some of the ideas and methodologies presented can be applied to other types of situations, we defined as our primary target an analysis at the supervisory control level of (x, y) data, generated by systems that cannot be described effectively through first-principles models, and whose performance depends to a large extent on quality-related issues and measurements. 

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]. 

Audits of each phase of the study should include personnel training, preparation of collection forms, application calibration, each sample collection procedure, sample transport, each type of chemical analysis, data recording, data entry, data verification and data storage. Data collection in the field is often tedious if automated logging devices are not in place. To ensure data integrity, the paper and ink used for field studies should be waterproof. Each data collection form should contain appropriate locations for information detailing the time and location of sample collection, sample transport and sample analysis. Data collection forms should be stored in an orderly fashion in a secure location immediately upon return of field teams from the field at the end of each day. It is also important for data quality for studies to collect necessary field data seven days per week when required. In our experience, poor study quality is likely when field sample and data collection do not proceed on weekends. 

Notebooks/worksheets or other records show the date of analysis, analyst, analyte, sample details, experimental observations, quality control, all rough calculations, any relevant instrument traces and relevant calibration data. 

The management of an analytical chemistry laboratory involves a number of different but related operations. Analysts will be concerned with the development and routine application of analytical methods under optimum conditions. Instruments have to be set up to operate efficiently, reproducibly and reliably, sometimes over long periods and for a variety of analyses. Results will need to be recorded and presented so that the maximum information may be extracted from them. Repetitive analysis under identical conditions is often required, for instance, in quality assurance programmes. Hence a large number of results will need to be collated and interpreted so that conclusions may be drawn from their overall pattern. The progress of samples through a laboratory needs to be logged and results presented, stored, transmitted and retrieved in an ordered manner. Computers and microprocessors can contribute to these operations in a variety of ways. 

---