The need to protect the natural environment from pollution from
point sources has fuelled the development of mathematical models
for predicting the performance of such systems. Previous attempts
at mathematical modeling of wastewater treatment have focused on
the use of mechanistic models which rely on the detailed
specification of the physics of the various biochemical reactions
and their numerous parameters. This is a problem because the huge
amounts of data needed for calibrating such models are often
unavailable, which together with imprecision in model structure
identification, results in uncertainties. This book has catalogued
a new approach to the problem using artificial intelligence methods
that are essentially data driven. In particular, the book through
practical case studies has demonstrated the feasibility of AI
techniques such as ANN, Fuzzy logic and hybrids of these in
wastewater management. The book will be suitable for students and
researchers in environmental engineering, practitioners and
consultants on wastewater treatment projects, and indeed anyone
faced with the task of analyzing, and understanding embedded
knowledge in large arrays of data.
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