Applied Parameter Estimation for Chemical Engineers (Chemical Industries) [Hardcover]

Peter Englezos (Author), Nicolas Kalogerakis (Author)

Book Description

ISBN-10: 082479561X | ISBN-13: 978-0824795610 | Publication Date: December 15, 2000 | Edition: 1st

This book determines adjustable parameters in mathematical models that describe steady state or dynamic systems, presenting the most important optimization methods used for parameter estimation. It focuses on the Gauss-Newton method and its modifications for systems and processes represented by algebraic or differential equation models.

Editorial Reviews

Review

Prepublication praise. . . . . .fills an enormous gap in chemical engineering and engineering in general....puts everything into proper perspective in a very organized way. The presentation is superb....the authors have used outstanding examples throughout the text in chemical reactor design, thermodynamics, biochemical engineering, and even petroleum reservoir simulation. This is one terrific book!
---Leo A. Behie Professor of Chemical Engineering and Director of PPRF, University of Calgary, Alberta, Canada

Product Details

• Hardcover: 462 pages
• Publisher: CRC Press; 1st edition (December 15, 2000)
• Language: English
• ISBN-10: 082479561X
• ISBN-13: 978-0824795610
• Product Dimensions: 9.1 x 6.2 x 1.3 inches
• Shipping Weight: 1.7 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #1,381,918 in Books (See Top 100 in Books)

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3 of 3 people found the following review helpful:

5.0 out of 5 stars Good source of practical knowledge, January 18, 2005

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C. Gelmi - See all my reviews
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This review is from: Applied Parameter Estimation for Chemical Engineers (Chemical Industries) (Hardcover)

For a long time I have been looking for a book like this one. Finally, I had found it. Just from the Table of Contents, you will see that includes a broad range of applications in parameter estimation such as: linear, non-linear, ODEs, PDEs and also algorithm's implementation. Special topics, such as sensitivity analysis in ODE models, Information index (where to do the measurements in order to maximize the content of information of the experimental data), and Statistical inference (i.e. parameters' inference, model adequacy test, etc.) are exposed with clarity.

I particularly like the section of Solved Problems, were you can compare your own algorithms with the ones exposed in the book. I work with MATLAB (including my own algorithms), and I have done a couple of comparisons that match perfect the numerical results shown in the book.

Many of the typical problems that I have faced in the area of parameter estimation were mentioned in the book; good recommendations or practices on how to avoid them are explained. In addition, there is a short section on shortcuts in the "bio" area.

In summary, a good source of practical knowledge for those ones interested in the topic.