Chapter 10 - Modeling Wastewater Treatment Process: A Genetic Programming Approach
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- @InCollection{SIVAPRAGASAM:2021:SCTSWWM,
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author = "C. Sivapragasam and Naresh K. Sharma and S. Vanitha",
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title = "Chapter 10 - Modeling Wastewater Treatment Process: A
Genetic Programming Approach",
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editor = "Rama Rao Karri and Gobinath Ravindran and
Mohammad Hadi Dehghani",
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booktitle = "Soft Computing Techniques in Solid Waste and
Wastewater Management",
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publisher = "Elsevier",
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pages = "187--201",
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year = "2021",
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isbn13 = "978-0-12-824463-0",
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DOI = "doi:10.1016/B978-0-12-824463-0.00026-4",
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URL = "https://www.sciencedirect.com/science/article/pii/B9780128244630000264",
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keywords = "genetic algorithms, genetic programming, biological
treatment, wastewater, soft computing, artificial
neural networks, biodegradation",
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abstract = "Wastewater treatment, recycle, and reuse cannot be
underestimated in the present context. Microbial units
containing bacterial and/or algal biomass are often
overrated as treatment techniques providing cheap and
effective alternatives. Nonetheless, they come with a
setback of limitedly understood systems especially with
respect to mechanisms involving pollutant degradation
and relationships among diverse environmental factors.
Although different modeling techniques have been in use
for predicting biodegradation, data mining based soft
computing tools such as artificial neural networks
(ANNs) and genetic programming (GP) offer substantial
control over process operation in terms of specific
understanding between experimental inputs and output.
ANN and GP are also used in performance prediction over
wider influent fluctuations with respect to variations
in pH, single- or multipollutants, biomass, and/or
e-donor/acceptor concentrations. Although these tools
have found applications in the domain of wastewater
treatment, the current practice is mostly on treatment
plant performance while limited or no emphasis on
relationships between diverse environmental factors and
pollutant removal. When compared to models based on
kinetic equations and the following cumbersome solving
of these equations, soft computing methods decipher the
information concealed in the data obtained from many
experimental and real-time biological experiments. This
chapter provides its readers an insight into biological
system aimed at pollutant degradation and use of soft
computing tools to precisely identify influential
parameters among several factors involved in
biodegradation of a certain pollutant. A comprehensive
approach of ascertaining and optimizing influential
parameters in the removal of specific pollutants from
aerobic, anaerobic bioreactors and constructed wetlands
(CWs) has been given herewith. It is shown that
biochemical oxygen demand removal from the biological
systems depended on crucial parameters such as the
differential temperature (Ta-Tw; the difference between
apparent temperature and wastewater temperature) in CWs
and detention times in bioreactors. Root mean square
error (RMSE) was used to compute the model performance,
for cyanide and phenol removal using GP, and the RMSEs
of 1.69 and 2.71 were obtained, respectively, while an
RMSE of 5.03 was obtained for modeling CW using ANN.
The case studies and modeling approaches discussed here
shall direct its readers in selecting and programming
data-driven models to better predict and understand the
roles of influencing parameters in the removal of
complex contaminants treated in biological units",
- }
Genetic Programming entries for
C Sivapragasam
Naresh K Sharma
Sankararajan Vanitha
Citations