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Membrane Bioreactor Process Modeling and Optimization by Artificial Neural Network and Integrated Bioprocess Models

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Abstract:

The energy efficiency of Ulu Pandan MBR plant is optimized by Artificial Neural Network (ANN) and bioprocess models. The ANN model predicts the dependence of the energy consumption per unit permeate product on operating parameters. The input variables for the ANN model are the volume of membrane scouring aeration, the volume of bioprocess aeration, the volume of mixed liquor transferred into the MBR system, and the volume of treated water produced. The input variables are used by the ANN model to predict the dependent output variable, energy consumption per unit permeate product water (kW-hr/m3). The ANN model results correlate well with operating data. An integrated bioprocess model based on the Activated Sludge Model is developed that includes the effects of sludge retention time (SRT), bound extracellular polymeric substances (EPS), and soluble microbial products (SMP). The bioprocess model investigates the impact of SRT on biological parameters in the bioreactor. The bioprocess model predictions of the key performance indicator, concentrations of volatile suspended solids (VSS) in the bioreactor, agree well with experimental results.

Keywords: Activated Sludge Model; Artificial Neural Networks; Membrane bioreactor; Process optimization

Document Type: Research Article

DOI: https://doi.org/10.2175/193864711802837075

Publication date: 2011-01-01

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