Design, analysis and validation of a simple dynamic model of a submerged membrane bioreactor

Integrated data-driven strategy to optimize the processes configuration for full-scale wastewater treatment plant predesign

Wastewater treatment plants (WWTPs) play an irreplaceable role in eliminating pollutants from domestic and industrial wastewater and contribute to water recycling. Nowadays, the selection of processes configuration of WWTPs mainly depends on the local wastewater treatment standards and the experience of wastewater engineers rather than an intelligent data-driven strategy. In this study, an integrated data-driven strategy consisting of t-distributed stochastic neighbor embedding (t-SNE) and deep neural networks (DNNs) is proposed for optimizing the processes configuration of full-scale WWTP predesign. A large dataset with 14,647 samples collected from 10 full-scale WWTPs with distinct treatment processes is clustered by the t-SNE method based on the influent characteristics, and four meaningful clusters (Clusters I-IV) are identified for the subsequent development of DNN classification models. All four DNN models achieve acceptable classification accuracy (>0.8975) and the maximal testing accuracy is 0.9505. The DNN models are capable of finding the optimized processes configuration of WWTPs under target scenarios. Our results highlight the strength of combining the t-SNE and the DNN models to utilize the relationships between key parameters and processes configuration of WWTPs, and help engineers predesign WWTPs with the optimal processes configuration.

Numerical simulation of filtration performance in submerged membrane bioreactors: effect of particle packed structure

It is widely known that the accumulation of solid matter forming a cake layer on the membrane surface is one of the major limitations of the filtration performance in submerged membrane bioreactors (SMBR). This study is focused on the influence of the cake porosity of different particle microscopic packed structures on the filtration performance of hollow fiber systems. An integrated model based on the finite element method to simulate numerically the flow in an SMBR is presented. The model coupled the Navier-Stokes and Darcy Brinkman equations to simulate a complete filtration run. The cake growth took into consideration not only the deposition with local filtration velocity but also the effect of aeration scouring. A novel solution of mesh deformation was adopted to investigate transient cake growth along the fiber. Comparisons between simulations and experiments are in good agreement. The results show that a higher porosity particle packed structure causes non-uniform filtration and cake thickness but also higher permeate flux. Meanwhile, the proportion of cake resistance to total resistance increases with the decrease of porosity.

Development of a dynamic mathematical model for membrane bioelectrochemical reactors with different configurations

Membrane bioelectrochemical reactors (MBERs) integrate membrane filtration into bioelectrochemical systems for sustainable wastewater treatment and recovery of bioenergy and other resource. Mathematical models for MBERs will advance the understanding of this technology towards further development. In the present study, a mathematical model was implemented for predicting current generation, membrane fouling, and organic removal within MBERs. The relative root-mean-square error was used to examine the model fit to the experimental data. It was found that a constant to determine how fast the internal resistance responds to the change of the anodophillic microorganism concentration could have a dominant impact on current generation. Hydraulic cross-flow exhibited a minor effect on membrane fouling unless it was reduced below 0.5 m s(-1). This MBER model encourages further optimization and eventually can be used to guide MBER development.