Abstract—The great potential of Waste Activated Sludge
(WAS) to produce methane as renewable bio-resource energy
has always been of engineers’ interest. The evaluation of the
rate of methane generation and its ultimate value is a crucial
step to predict the performance of anaerobic digesters
degrading wide ranges of raw and pre-treated WAS.
Biochemical methanogenic potential (BMP) test is known as the
most common assay in this context. However, it is known as a
time consuming, equipment-intensive and consequently
expensive tool. The objectives of this research are to identify
key WAS properties required to estimate biodegradability of
raw and pretreated sludge and accordingly generate a proper
model for predicting sludge biodegradability, utilizing Artificial
Neural Networks (ANN). Earlier attempts to identify such key
indicators and generating a proper model representing sludge
biodegradability using typical mathematical approaches were
unsuccessful. However, the results of this research proved ANN
effective in modeling sludge biodegradability.
Index Terms—Sludge digestion, pre-treatment, solid
residence time, artificial neural networks.
Peiman Kianmehr, Wathiq Mansoor, and Fadi A. Kfoury are with School
of Engineering, American University in Dubai, United Arab Emirates (e-mail:
pkianmehr@aud.edu).
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Cite:Peiman Kianmehr, Wathiq Mansoor, and Fadi A. Kfoury, "Prediction of Biogas Generation Profiles in Wastewater Treatment Plants Using Neural Networks," Journal of Clean Energy Technologies vol. 2, no. 3, pp. 201-205, 2014.
