Wavelet analysis is an emerging field of applied mathematics that has provided new tools and algorithms for solving such problems as are encountered in fault diagnosis, modelling, identification, and control and optimization of chemical systems, where raw sensor data have to be processed into meaningful information. The theory has acquired the status of a unifying theory underlying many of the methods used in physics and signal processing. Wavelet Applications in Chemical Engineering gives an insight into the many possible applications of time-frequency decomposition to the discipline of chemical engineering. It also includes tutorial chapters introducing wavelets and providing computational examples which will be especially helpful to readers new to the field. For researchers and students investigating new tools to solve problems in process operation and control, process modeling and simulation, process optimization and sensor data interpretation, Wavelet Applications in Chemical Engineering is both a practical resource and a valuable reference.
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2 of 2 people found the following review helpful:
4.0 out of 5 stars
Edited papers on wavelets and applications in chemical engg.,
By A Customer
This review is from: Wavelet Applications in Chemical Engineering (The Springer International Series in Engineering and Computer Science) (Hardcover)
The authors of the 8 chapters in this book provide an excellent overview of wavelets, wavelet transforms, and their applications in chemical engineering.
The first two chapters deal with wavelets, wavelet transforms, and an easy-to-read tutorial on computing wavelet transforms and constructing wavelets. Chapters 3 and 4 discuss applications in trend analysis and feature extraction. The authors of Chapter 4 present applications of wavelets to the field of process and sensor data fingerprinting. Chapter 5 describes how wavelets can be used along with neural networks to extract information from process data via induction. Chapter 6 presents a wavelet domain formulation and solution of model predictive control. Chapter 7 addresses numerical solution of differential equations, solutions for which exhibit abrupt changes, using orthogonal and semi-orthogonal wavelets. Several numerical examples are given. The last chapters discusses implentation of two-dimensional wavelet packet transform and its application to analyze two-dimensional turbulent flow fields.
The book is valuable as a reference source and should prove useful to introduce researchers to the exciting facets of wavelets.
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