Neural Network ABAC with Dropout Layer for Activated Sludge System
Due to the expensive operation of the activated sludge process and more stringent effluent requirements of wastewater treatment plant (WWTP), the wastewater treatment operator has been forced to find an alternative to improve the current control strategy, especially for those operating using an ac...
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my.unimas.ir.361382021-09-20T05:41:33Z http://ir.unimas.my/id/eprint/36138/ Neural Network ABAC with Dropout Layer for Activated Sludge System Maimun, Binti Huja Husin Mohd Fua’ad, Rahmat Norhaliza, Abdul Wahab Mohamad Faizrizwan, Mohd Sabri TK Electrical engineering. Electronics Nuclear engineering Due to the expensive operation of the activated sludge process and more stringent effluent requirements of wastewater treatment plant (WWTP), the wastewater treatment operator has been forced to find an alternative to improve the current control strategy, especially for those operating using an activated sludge system. The study aims to reduce the energy usage of a WWTP and to increase the effluent quality to meet the requirements of state and national laws by using the aeration control technique. The goals are achieved by varying the dissolved oxygen concentration in the benchmark plant's fifth tank according to the real ammonium measurement, a technique known as Ammonium-based aeration control (ABAC), which produced less nitrogen, resulting in better effluent and lower energy consumption. The simulation model Benchmark Simulation Model No. 1 (BSM1) was used to analyze ABAC in this study. The neural network (NN) model is used to design the ABAC controller, and simulation results were compared to the Proportional Integral (PI) controller of the BSM1 and PI ABAC control configurations. A dropout layer was added during the training process to improve neural network generalization. The dropout layer in the NN ABAC has improved the performances in terms of total nitrogen effluent violations by 4 percent less than the PI-ABAC and by 36 percent less than the PI. The NN ABAC LM dropout has been proven to be more effective in terms of energy efficiency by significantly reduced by 25 percent, effluent quality by successfully improved by 1 percent, and successfully reduced the total overall cost index by 5 percent when compared to PI-ABAC control. The study has illustrated that the NN ABAC could be used to improve the performance of the activated sludge system. Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press) 2021-09-15 Article PeerReviewed text en http://ir.unimas.my/id/eprint/36138/1/neural1.pdf Maimun, Binti Huja Husin and Mohd Fua’ad, Rahmat and Norhaliza, Abdul Wahab and Mohamad Faizrizwan, Mohd Sabri (2021) Neural Network ABAC with Dropout Layer for Activated Sludge System. Journal of Electrical Engineering, 20 (2-2). pp. 82-86. ISSN 0128-4428 https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/view/297 |
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TK Electrical engineering. Electronics Nuclear engineering Maimun, Binti Huja Husin Mohd Fua’ad, Rahmat Norhaliza, Abdul Wahab Mohamad Faizrizwan, Mohd Sabri Neural Network ABAC with Dropout Layer for Activated Sludge System |
| description |
Due to the expensive operation of the activated sludge process and more stringent effluent requirements of
wastewater treatment plant (WWTP), the wastewater treatment operator has been forced to find an alternative to improve the
current control strategy, especially for those operating using an activated sludge system. The study aims to reduce the energy
usage of a WWTP and to increase the effluent quality to meet the requirements of state and national laws by using the aeration
control technique. The goals are achieved by varying the dissolved oxygen concentration in the benchmark plant's fifth tank
according to the real ammonium measurement, a technique known as Ammonium-based aeration control (ABAC), which
produced less nitrogen, resulting in better effluent and lower energy consumption. The simulation model Benchmark
Simulation Model No. 1 (BSM1) was used to analyze ABAC in this study. The neural network (NN) model is used to design
the ABAC controller, and simulation results were compared to the Proportional Integral (PI) controller of the BSM1 and PI
ABAC control configurations. A dropout layer was added during the training process to improve neural network generalization.
The dropout layer in the NN ABAC has improved the performances in terms of total nitrogen effluent violations by 4 percent
less than the PI-ABAC and by 36 percent less than the PI. The NN ABAC LM dropout has been proven to be more effective
in terms of energy efficiency by significantly reduced by 25 percent, effluent quality by successfully improved by 1 percent,
and successfully reduced the total overall cost index by 5 percent when compared to PI-ABAC control. The study has illustrated
that the NN ABAC could be used to improve the performance of the activated sludge system. |
| format |
Article |
| author |
Maimun, Binti Huja Husin Mohd Fua’ad, Rahmat Norhaliza, Abdul Wahab Mohamad Faizrizwan, Mohd Sabri |
| author_facet |
Maimun, Binti Huja Husin Mohd Fua’ad, Rahmat Norhaliza, Abdul Wahab Mohamad Faizrizwan, Mohd Sabri |
| author_sort |
Maimun, Binti Huja Husin |
| title |
Neural Network ABAC with Dropout Layer for
Activated Sludge System |
| title_short |
Neural Network ABAC with Dropout Layer for
Activated Sludge System |
| title_full |
Neural Network ABAC with Dropout Layer for
Activated Sludge System |
| title_fullStr |
Neural Network ABAC with Dropout Layer for
Activated Sludge System |
| title_full_unstemmed |
Neural Network ABAC with Dropout Layer for
Activated Sludge System |
| title_sort |
neural network abac with dropout layer for
activated sludge system |
| publisher |
Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press) |
| publishDate |
2021 |
| url |
http://ir.unimas.my/id/eprint/36138/1/neural1.pdf http://ir.unimas.my/id/eprint/36138/ https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/view/297 |
| _version_ |
1712289131750490112 |
| score |
13.211869 |