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Applied Parameter Estimation for Chemical Engineers by Peter Englezos

By Peter Englezos

A reference and textbook offering readers with an important optimization tools for parameter estimation in chemical engineering. The CD-ROM positive aspects Fortran machine courses for fixing equation platforms and different issues of Microsoft Excel and Sigma Plot. includes over 900 equations in all.

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Extra resources for Applied Parameter Estimation for Chemical Engineers

Sample text

H m ] 1 is a m-dimensional vector function of known form that relates in a nonlinear fashion the state vector to the output vector. The state variables are the minimal set of dependent variables that are needed in order to describe fully the state of the system. The output vector represents normally a subset of the state variables or combinations of them that are measured. For example, if we consider the dynamics of a distillation column, in order to describe the condition of the column at any point in time we need to know the prevailing temperature and concentrations at each tray (the state variables).

1987) among others. 3 PARAMETER ESTIMATION SUBJECT TO CONSTRAINTS In parameter estimation we are occasionally faced with an additional complication. Besides the minimization of the objective function (a weighted sum of errors) the mathematical model of the physical process includes a set of constrains that must also be satisfied. In general these are either equality or inequality constraints. In order to avoid unnecessary complications in the presentation of the material, constrained parameter estimation is presented exclusively in Chapter 9.

In certain circumstances, the model equations may not have an explicit expression for the measured variables. Namely, the model can only be represented implicitly. In such cases, the distinction between dependent and independent variables becomes rather fuzzy, particularly when all the variables are subject to experimental error. As a result, it is preferable to consider an augmented vector of measured variables, y, that contains both regressor and response variables (Box, 1970; Britt and Luecke, 1973).

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