Big Chemical Encyclopedia

Chemical substances, components, reactions, process design ...

Articles Figures Tables About

Input-output analysis, process data

Data about the plans and routines used by workers in controlling a process can be obtained by means of an "activity analysis," a type of input-output analysis. A chart can be made showing how certain process indicators change over time in response to changes of the control settings. From this chart it is possible to determine the type of process mformation that workers use to carry... [Pg.157]

In this chapter, we focus on recent and emerging technologies that either are or soon will be applied commercially. Older technologies are discussed to provide historic perspective. Brief discussions of potential future technologies are provided to indicate current development directions. The chapter substantially extends an earlier publication (Davis et al., 1996a) and is divided into seven main sections beyond the introduction Data Analysis, Input Analysis, Input-Output Analysis, Data Interpretation, Symbolic-Symbolic Interpretation, Managing Scale and Scope of Large-Scale Process Operations, and Comprehensive Examples. [Pg.9]

Those based on strictly empirical descriptions Mathematical models based on physical and chemical laws (e.g., mass and energy balances, thermodynamics, chemical reaction kinefics) are frequently employed in optimization apphcations. These models are conceptually attractive because a gener model for any system size can be developed before the system is constructed. On the other hand, an empirical model can be devised that simply correlates input-output data without any physiochemical analysis of the process. For... [Pg.742]

As discussed and illustrated in the introduction, data analysis can be conveniently viewed in terms of two categories of numeric-numeric manipulation, input and input-output, both of which transform numeric data into more valuable forms of numeric data. Input manipulations map from input data without knowledge of the output variables, generally to transform the input data to a more convenient representation that has unnecessary information removed while retaining the essential information. As presented in Section IV, input-output manipulations relate input variables to numeric output variables for the purpose of predictive modeling and may include an implicit or explicit input transformation step for reducing input dimensionality. When applied to data interpretation, the primary emphasis of input and input-output manipulation is on feature extraction, driving extracted features from the process data toward useful numeric information on plant behaviors. [Pg.43]

The process of field validation and testing of models was presented at the Pellston conference as a systematic analysis of errors (6. In any model calibration, verification or validation effort, the model user is continually faced with the need to analyze and explain differences (i.e., errors, in this discussion) between observed data and model predictions. This requires assessments of the accuracy and validity of observed model input data, parameter values, system representation, and observed output data. Figure 2 schematically compares the model and the natural system with regard to inputs, outputs, and sources of error. Clearly there are possible errors associated with each of the categories noted above, i.e., input, parameters, system representation, output. Differences in each of these categories can have dramatic impacts on the conclusions of the model validation process. [Pg.157]

Failure to meet the requirements of the validation protocol with respect to process inputs and output control should be subjected to requalification following a thorough analysis of process data and formal review by the CMC Coordination Committee. [Pg.36]

Control based on neural network. Similar to fuzzy logic modeling, neural network analysis uses a series of previous data to execute simulations of the process, with a high degree of success, without however using formal mathematical models (Chen and Rollins, 2000). To this goal, it is necessary to define inputs, outputs, and how many layers of neurons will be used, which depends on the number of variables and the available data. [Pg.270]


See other pages where Input-output analysis, process data is mentioned: [Pg.7]    [Pg.32]    [Pg.32]    [Pg.285]    [Pg.216]    [Pg.27]    [Pg.10]    [Pg.111]    [Pg.24]    [Pg.15]    [Pg.72]    [Pg.10]    [Pg.1]    [Pg.72]    [Pg.17]    [Pg.232]    [Pg.916]    [Pg.9]    [Pg.24]    [Pg.201]   


SEARCH



Data processing

Input analysis, process data

Input data

Input processing

Input-output analysis

Input-output analysis, process data regression

Input/output

Output Analysis

Output data

Process analysis

Process analysis processes

Process data

Process data analysis

Process inputs/outputs

Processing analysis

© 2024 chempedia.info