Process inferential analysis

While this may seem to be an esoteric concern, it has an important consequence. As discussed earlier, the purpose of a clinical trial is to forecast outcomes for the population, not to focus on the results of the sample itself. Inferential statistics is the discipline used to make this forecast. Virtually all the commonly used statistical inferential analysis techniques for clinical trials require that the sample be drawn from the population using a random process. The p-value itself only has meaning in this framework, where it reflects the probability that two samples (subjects treated with the new drug vs. those treated with control) could have achieved the results seen in the trial if the two treatments in fact had the same effectiveness. This is often rephrased as the probability that the apparent benefit... 

The field of Process Analysis, and inferential analysis in particular, has received a huge boost in importance by the emphasis that the Center for Drug Evaluation and Research (CDER) within the US Food and Drug Agency (FDA) has given to the use of PAT in the pharmaceutical and biotechnology industries... 

Inferential analysis [20, 21] is not a spectroscopy but could have a bearing upon the use of all process analysis techniques. It is a term being used to describe measurements that are not made but are inferred from other properties of the process under scrutiny. These methods rely upon process models being available for the process concerned. The value of this approach, quite apart from the fact that no expensive equipment is needed, is that it can give an indication of a measurement when it is impossible to extract a sample without it undergoing change or where inserting a probe is impractical. Inferential methods can also be useful to provide values between the frequency of the installed measurement devices or indeed when the measurement devices are off-line for maintenance purposes. The quality of an intermediate or a product, can in some instances be inferred from the values of temperature, pressure and flow rates in the area of the process under consideration. 

The cost per analysis point and the projected payback are only two metrics used to evaluate the feasibility of installing a process analyzer. If a spectrometer replaces an existing service intensive analyzer then that is a positive situation. Also, because of the flexibility of infrared analyses, it is feasible that a mid-IR analyzer might replace several existing analyzers it is a case where direct chemical information from IR can be more important than certain inferential methods that only provide indirect information. Also, if the only other... 

Joseph, B. and Brosilow, C.B., "Inferential Control of Processes Part 1. Steady-Stated Analysis and Design, Part III. Construction of Optimal and Suboptimal Dynamic Estimations," AlChE J. Vol. 24, No. 3, pp 485-492, pp 500-509, 1978a,b. 

Part V (Chapters 19 through 22) deals with the description, analysis, and design of more complex control systems, with one controlled output. In particular, Chapter 19 introduces the concept of feedback compensation with Smith s predictor, to cope with systems possessing large dead times or inverse response. Chapter 20 describes and analyzes a variety of multiloop control systems (with one controlled output) often encountered in chemical processes, such as cascade, selective, and split-range. Chapter 21 is devoted exclusively to the analysis and design of feedforward and ratio control systems, while Chapter 22 makes a rather descriptive presentation of adaptive and inferential control schemes why they are needed and how they can be used. 

An example of an inferential task-analysis approach that is relatively new is nonlinear causal resource analysis (NCRA) [Kondraske, 1998,1999 Kondraske et al., 1997]. This method was motivated by human performance analysis situations where direct analysis is not possible (e.g., determination of the amount of visual information-processing speed required to drive safely on a highway). Quantitative task demands, in terms of performance variables that characterize the involved subsystems, are inferred from a population data set that includes measures of subsystem performance, resource availabilities (e.g., speed, accuracy, etc.), and overall performance on the task in question. This method is based on the simple observation that the individual with the least amount of the given resource (i.e., the lowest performance capacity) who is still able to accomplish a given goal (i.e., achieve a given level of performance in the specified high-level task) provides the key clue. That amount of availability is used to infer the amount of demand imposed by the task. 

Porges, S. W., Bohrer, R. E. (1990). The analysis of periodic processes in psychophysiological research. In J. T Cacioppo L. G. Tassinary (Eds.), Principles of psychophysiology Physical, social, and inferential elements (pp. 708-753). New York Cambridge University Press. 

Statistics is a collection of methods of enquiry used to gather, process, or interpret quantitative data. The two main functions of Statistics are to describe and summarize data and to make inferences about a larger population of which the data are representative. These two areas are referred to as Descriptive and Inferential Statistics, respectively both areas have an important part to play in Data Mining. Descriptive Statistics provides a toolkit of methods for data summarization while Inferential Statistics is more concerned with data analysis. 

Quality control laboratories may be equipped with instruments which indicate MW or MWD directly. The most common technique used for this purpose is gel permeation chromatography (GPC). Infrequent analysis is the rule. Such off-line measurements are most often used to update inferential models, or to effect open loop control of the polymerization by manual process adjustments based on GPC results. On-line GPC is available its application is not yet common in industrial practice, but applications of on-line measurement of MWD by GPC have been reported [26]. The difficulties encountered with on-line GPC are the maintenance, sampling and calibration problems associated with any process chromatography application. In addition, a compromise must be made between resolution of the MWD and time of analysis. As a rule of thumb, it is possible to determine an accurate average molecular weight in under 10 minutes. Determination of the MWD can take considerably longer. 

Typical automatic batch-dryer control systems use the exhaust-air temperature as the controlled variable to determine when to end the drying process. Shinskey [37] and Fadum and Shinskey [38] have described an alternative control scheme employing inferential control. The system has proven to be an effective substitute for online moisture analysis in terminating drying. This method is based on the following equation ... 

Dynamic process models are usefulfor detailed cfynamic process analysis and in process control applications. They are also increasingly usedfor predicting process variables, such as quality. Especially when the quality variable is difficult to measure or can be measured infrequently, dynamic models can provide an inferential measurement, based on easily and frequently measurable variables. 

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