Input analysis, process data example

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. 

Those based on strictly empirical descriptions Mathematical models based on physical and cnemical laws (e.g., mass and energy balances, thermodynamics, chemical reaction kinetics) are frequently employed in optimization applications. These models are conceptually attractive because a general 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 these models, optimization is often used to fit a model to process data, using a procedure called parameter estimation. The well-known least squares curve-fitting procedure is based on optimization theory, assuming that the model parameters are contained linearly in the model. One example is the yield matrix, where the percentage yield of each product in a unit operation is estimated for each feed component... 

Data quality must be ensured for all of the modules investigated. On the basis of the input materials for a process and the specific process type, an analysis of data completeness and plausibility can be carried out, for example by means of stoichiometric calculations or by comparing the data with published statistics. If substitute values or estimates are used, they must be labeled as such and their use explained. Assumptions made must be explained. 

Examining the contribution of technology to the safety of the entire system is usually done by applying safety-, reliability- and risk analysis techniques developed in past decades. Technical system components can be broken down into subcomponents, the structure of which can be unambiguously defined (i.e. what kind of input data they receive, what functions they use to process data and what kind of output data they provide). For example, a primary radar data processing unit may get the time of transmission and time of reception... 

Example 2 (Univariate time series analysis for chemical production data). An application of univariate time series analysis is the modelling of univariate input flows of chemical production processes (as observed for SISO or SIMO processes). To apply prewhitening procedures, the auto-correlation structure of the (exogenous) time series of the input flow has to be modelled. In this example, the input flow rate of a Naphtha cracker is analysed. Figure 2.12 shows the average hourly input flow rate for one week (T = 168). 

Figure 9.4 Risk assessment for an aquatic environment based on a probabilistic procedure into which the concept of varying sensitivity in multispecies communities is incorporated (Nendza, Volmer and Klein, 1990). Exposure and effects are determined separately from experimental or, if not available, QSAR data. Physico-chemical data and information on bioaccumulation and biotransformation are the input for computer simulations of transport and distribution processes that estimate the concentrations of a potential contaminant in a selected river scenario, using, for example, the EXAMS model (Bums, Cline and Lassiter, 1982). For the effects assessment, the log-normal sensitivity distribution is calculated from ecotoxicological data and the effective concentrations for the most sensitive species are determined. The exposure concentrations and toxicity data are then compared by analysis of variance to give a measure of risk for the environment. Modified from Nendza, Volmer and Klein (1990) with kind permission from Kluwer Academic Publishers, Dordrecht.

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