Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process
Aeration control is a way to have a wastewater treatment plant (WWTP) that uses less energy and produces higher effluent quality to meet state and federal regulations. The goal of this research is to develop a neural network (NN) ammonia-based aeration control (ABAC) that focuses on reducing tot...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Exeley Inc.
2021
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/36430/1/nitrogen1.pdf http://ir.unimas.my/id/eprint/36430/ https://www.exeley.com/in_jour_smart_sensing_and_intelligent_systems/doi/10.21307/ijssis-2021-016 https://doi.org/10.21307/ijssis-2021-016 |
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| Summary: | Aeration control is a way to have a wastewater treatment plant
(WWTP) that uses less energy and produces higher effluent quality
to meet state and federal regulations. The goal of this research is
to develop a neural network (NN) ammonia-based aeration control
(ABAC) that focuses on reducing total nitrogen and ammonia
concentration violations by regulating dissolved oxygen (DO)
concentration based on the ammonia concentration in the final
tank, rather than maintaining the DO concentration at a set elevated
value, as most studies do. Simulation platform used in this study is
Benchmark Simulation Model No. 1, and the NN ABAC is compared
to the Proportional-Integral (PI) ABAC and PI controller. In comparison
to the PI controller, the simulation results showed that the proposed
controller has a significant improvement in reducing the AECI up to
23.86%, improving the EQCI up to 1.94%, and reducing the overall
OCI up to 4.61%. The results of the study show that the NN ABAC
can be utilized to improve the performance of a WWTP’s activated
sludge system. |
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