Download Applied Parameter Estimation for Chemical Engineers by Peter Englezos PDF
By Peter Englezos
A reference and textbook supplying readers with crucial optimization tools for parameter estimation in chemical engineering. The CD-ROM positive factors Fortran machine courses for fixing equation structures and different issues of Microsoft Excel and Sigma Plot. includes over 900 equations in all.
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Additional resources for Applied Parameter Estimation for Chemical Engineers
39) In all the above cases we presented confidence intervals for the mean expected response rather than a future observation (future measurement) of the response variable, yp . In this case, besides the uncertainty in the estimated parame- ters, we must include the uncertainty due to the measurement error (e0). , they have a single response variable, m=l) can be readily solved by available software. We will demonstrate such problems can be solved by using Microsoft Excel™ and SmmaPlot™. 1 Procedure for Using Microsoft Excel™ for Windows Step 1.
3 becomes where F(XJ) is an mxp dimensional matrix which depends only on Xj and it is independent of the parameters. 5) A brief review of linear regression analysis is presented in Chapter 3. ). In these models some of the parameters enter in a linear fashion, namely, the model is of the form, Copyright © 2001 by Taylor & Francis Group, LLC 10 Chapter! 7) The structure of such models can be exploited in reducing the dimensionality of the nonlinear parameter estimation problem since, the conditionally linear parameters, k], can be obtained by linear least squares in one step and without the need for initial estimates.
2 Inference on the Expected Response Variables A valuable inference that can be made to infer the quality of the model predictions is the (l-cc)100% confidence interval of the predicted mean response at x0. It should be noted that the predicted mean response of the linear regression model at x0 is y0 = F(x0)k* or simply y0 = X0k*. Although the error term s0 is not included, there is some uncertainty in the predicted mean response due to the uncertainty in k*. 30. Let us now concentrate on the expected mean response of a particular response variable.