Modeling the performance of Single-stage Nitrogen removal using Anammox and Partial nitritation (SNAP) process with backpropagation neural network and response surface methodology
Item
Title
Modeling the performance of Single-stage Nitrogen removal using Anammox and Partial nitritation (SNAP) process with backpropagation neural network and response surface methodology
Date
2019
Language
English
Abstract
Two novel feedforward backpropagation Artificial Neural Networks (ANN)-based-models (8:NH:1 and 7:NH:1) combined with Box-Behnken design of experiments methodology was proposed and developed to model NH4+ and Total Nitrogen (TN) removal within an upflow-sludge-bed (USB) reactor treating nitrogen-rich wastewater via Single-stage Nitrogen removal using Anammox and Partial nitritation (SNAP) process. ANN were developed by optimizing network architecture parameters via response surface methodology. Based on the goodness-of-fit standards, the proposed three-layered NH4+ and TN removal ANN-based-models trained with Levenberg-Marquardt-algorithm demonstrated high-performance as computations exhibited smaller deviations-(±2.1%) as well as satisfactory coefficient of determination (R2), fractional variance-(FV), and index of agreement-(IA) ranging 0.989–0.997, 0.003–0.031 and 0.993–0.998, respectively. The computational results affirmed that the ANN architecture which was optimized with response surface methodology enhanced the efficiency of the ANN-based-models. Furthermore, the overall performance of the developed ANN-based models revealed that modeling intricate biological systems (such as SNAP) using ANN-based models with the view to improve removal efficiencies, establish process control strategies and optimize performance is highly feasible. Microbial community analysis conducted with 16S rRNA high-throughput approach revealed that Candidatus Kuenenia was the most pronounced genera which accounted for 13.11% followed by Nitrosomonas-(6.23%) and Proteocatella-(3.1%), an indication that nitrogen removal pathway within the USB was mainly via partial-nitritation / anammox process.
Collection
Citation
“Modeling the performance of Single-stage Nitrogen removal using Anammox and Partial nitritation (SNAP) process with backpropagation neural network and response surface methodology,” CSIRSpace, accessed December 23, 2024, http://cspace.csirgh.com/items/show/899.