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[1] Jetten, M.S., Strous, M., van de Pas-Schoonen, K.T., Schalk, J., van Dongen, U.G., van de Graaf, A.A., Logemann, S., Muyzer, G., van Loosdrecht, M.C. & Kuenen, J.G., The anaerobic oxidation of ammonium. FEMS Microbiology Reviews, 22(5), pp. 421–437, 1998.
[2] Mulder, A., van de Graaf, A.A., Robertson, L.A. & Kuenen, J.G., Anaerobic ammonium oxidation discovered in a denitrifying fluidized bed reactor. FEMS Microbiology Ecology, 16(3), pp. 177–183, 1995.
[3] Thamdrup, B. & Dalsgaard, T., Production of N2 through Anaerobic Ammonium Oxidation Coupled to Nitrate Reduction in Marine Sediments. Applied and Environmental Microbiology, 68(3), pp. 1312–1318, 2002.
[4] Dalsgaard, T., Canfield, D.E., Petersen, J., Thamdrup, B. & Acuna-Gonzalez, J., N2 production by the anammox reaction in the anoxic water column of Golfo Dulce, Costa Rica. Nature, 422(6932), pp. 606–608, 2003.
[5] Van de Graaf, A.A., de Bruijn, P., Robertson, L.A., Jetten, M.S. & Kuenen, J.G., Autotrophic growth of anaerobic ammonium-oxidizing micro-organisms in a fluidized bed reactor. Microbiology, 142(8), pp. 2187–2196, 1996.
[6] Third, K.A., Paxman, J., Schmid, M., Strous, M., Jetten, M.S.M. & Cord-Ruwisch, R., Enrichment of anammox from activated sludge and its application in the CANON process. Microbial Ecology, 49(2), pp. 236–244, 2005.
[7] Holenda, B., Domokos, E., Rédey, Á. & Fazakas, J., Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control. Computers & Chemical Engineering, 32(6), pp. 1270–1278, 2008.
[8] Li, G., Carvajal-Arroyo, J.M., Sierra-Alvarez, R. & Field, J.A., Mechanisms and Control of NO2—Inhibition of Anaerobic Ammonium Oxidation (Anammox). Water Environment Research, 89(4), pp. 330–336, 2017.
[9] Lu, X., Yin, Z., Sobotka, D., Wisniewski, K., Czerwionka, K., Xie, L., Zhou, Q. & Makinia, J., Modeling the pH effects on nitrogen removal in the anammox-enriched granular sludge. Water Science and Technology, 75(2), pp. 378–386, 2017.
[10] Hu, Z., Lotti, T., van Loosdrecht, M. & Kartal, B., Nitrogen removal with the anaerobic ammonium oxidation process. Biotechnology Letters, 35(8), pp. 1145–1154, 2013.
[11] Gonzalez-Martinez, A., Rodriguez-Sanchez, A., Garcia-Ruiz, M.J., Muñoz-Palazon, B., Cortes-Lorenzo, C., Osorio, F. & Vahala, R., Performance and bacterial community dynamics of a CANON bioreactor acclimated from high to low operational temperatures. Chemical Engineering Journal, 287, pp. 557–567, 2016.
[12] Strous, M., Kuenen, J.G. & Jetten, M.S.M., Key Physiology of Anaerobic Ammonium Oxidation. Applied and Environmental Microbiology, 65(7), pp. 3248–3250, 1999.
[13] Vives, J.C.M.T., López, H., Ganigué, R., Ruscalleda, M., Sànchez*, A., Vila, X., López, R., mjesús llorens, Salamero, M., González, E., Jiménez, E., Balaguer, M.D. & Elorduy, M., Closing the Nitrogen Cycle from Urban Landfill Leachate by Biological Nitrogen Removal Over Nitrite and Thermal Treatment, 2003.
[14] Henze, M., Gujer, W., Mino, T. & van Loosedrecht, M., Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Publishing, 2006.
[15] Rustum, R., Modelling Activated Sludge Wastewater Treatment Plants Using Artificial Intelligence Techniques (Fuzzy Logic and Neural Networks). In Heriot-Watt University, 2009.
[16] Xie, B., Ma, Y.W., Wan, J.Q., Wang, Y., Yan, Z.C., Liu, L. & Guan, Z.Y., Modeling and multi-objective optimization for ANAMMOX process under COD disturbance using hybrid intelligent algorithm. Environmental Science and Pollution Research, 25(21), pp. 20956–20967, 2018.
[17] Dochain, D. & Pauss, A., On-line estimation of microbial specific growth-rates: An illustrative case study. The Canadian Journal of Chemical Engineering, 66(4), pp. 626–631, 1988.
[18] Hong, S.H., Lee, M.W., Lee, D.S. & Park, J.M., Monitoring of sequencing batch reactor for nitrogen and phosphorus removal using neural networks. Biochemical Engineering Journal, 35(3), pp. 365–370, 2007.
[19] W.H. Organization, Sustanable Development Goal 6, In United Nation, 2018.
[20] W.H. Organization, Sustainable Development Goal 7, In 2018.
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Open Access
Research article

Cost Effective Nitrogen Removal – Novel Control Strategies

Alam Nawaz1,
Amarpreet Singh Arora1,
Choa Mun Yun2,
Hwanchul Cho3,
Moonyong Lee1*
1
School of Chemical Engineering, Yeungnam University, Gyeongsan, South Korea
2
Sherpa Space Inc., Daejeon 34051, Republic of Korea
3
Doosan Heavy Industries & Construction, Yongin 16858, Republic of Korea
International Journal of Computational Methods and Experimental Measurements
|
Volume 7, Issue 4, 2019
|
Pages 376-384
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: N/A
View Full Article|Download PDF

Abstract:

The anammox process, used to remove nitrogen from wastewaters is conside red a promising approach due to its advantages over traditional processes. The current study emphasizes on the cost effective nitrogen removal from the sidestream effluent of anaerobic digester with partial nitration (PN) and anaerobic ammonium oxidation (anammox) process for the municipal wastewater treatment plant. The main objective of this study was to model a cost effective strategy for setting up a lab-scale sequencing batch reactor (SBR) by using activated sludge model (ASM) process equations with applying novel control strategies (NCS) for improving nitrogen-removal efficiency (NRE). An average rate of removal 80% was obtained during the period of its operation. NCS (intermittent aeration, alteration in the cycle length, etc) were introduced to optimize the operating cost. The overall system contributes to lower- ing in the greenhouse gas emissions by minimizing the use of energy (60–65%) and hence supporting the WHO mission of achieving sustainable development goals. Results further indicate the future possibility of escalating the lab-scale to full-scale applications.

Keywords: Anammox, Control Strategy, NRE, Operating Cost, PN, WHO Mission

1. Introduction

2. Materials and Methods

3. Results and Discussion

4. Concluding Remark and Future Aspects

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Acknowledgments

This work was supported by the 2018 Yeungnam University Research Grant. This work was also supported by Doosan Heavy Industries and Construction grant (Y16031) and by Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2014R1A6A1031189).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References
[1] Jetten, M.S., Strous, M., van de Pas-Schoonen, K.T., Schalk, J., van Dongen, U.G., van de Graaf, A.A., Logemann, S., Muyzer, G., van Loosdrecht, M.C. & Kuenen, J.G., The anaerobic oxidation of ammonium. FEMS Microbiology Reviews, 22(5), pp. 421–437, 1998.
[2] Mulder, A., van de Graaf, A.A., Robertson, L.A. & Kuenen, J.G., Anaerobic ammonium oxidation discovered in a denitrifying fluidized bed reactor. FEMS Microbiology Ecology, 16(3), pp. 177–183, 1995.
[3] Thamdrup, B. & Dalsgaard, T., Production of N2 through Anaerobic Ammonium Oxidation Coupled to Nitrate Reduction in Marine Sediments. Applied and Environmental Microbiology, 68(3), pp. 1312–1318, 2002.
[4] Dalsgaard, T., Canfield, D.E., Petersen, J., Thamdrup, B. & Acuna-Gonzalez, J., N2 production by the anammox reaction in the anoxic water column of Golfo Dulce, Costa Rica. Nature, 422(6932), pp. 606–608, 2003.
[5] Van de Graaf, A.A., de Bruijn, P., Robertson, L.A., Jetten, M.S. & Kuenen, J.G., Autotrophic growth of anaerobic ammonium-oxidizing micro-organisms in a fluidized bed reactor. Microbiology, 142(8), pp. 2187–2196, 1996.
[6] Third, K.A., Paxman, J., Schmid, M., Strous, M., Jetten, M.S.M. & Cord-Ruwisch, R., Enrichment of anammox from activated sludge and its application in the CANON process. Microbial Ecology, 49(2), pp. 236–244, 2005.
[7] Holenda, B., Domokos, E., Rédey, Á. & Fazakas, J., Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control. Computers & Chemical Engineering, 32(6), pp. 1270–1278, 2008.
[8] Li, G., Carvajal-Arroyo, J.M., Sierra-Alvarez, R. & Field, J.A., Mechanisms and Control of NO2—Inhibition of Anaerobic Ammonium Oxidation (Anammox). Water Environment Research, 89(4), pp. 330–336, 2017.
[9] Lu, X., Yin, Z., Sobotka, D., Wisniewski, K., Czerwionka, K., Xie, L., Zhou, Q. & Makinia, J., Modeling the pH effects on nitrogen removal in the anammox-enriched granular sludge. Water Science and Technology, 75(2), pp. 378–386, 2017.
[10] Hu, Z., Lotti, T., van Loosdrecht, M. & Kartal, B., Nitrogen removal with the anaerobic ammonium oxidation process. Biotechnology Letters, 35(8), pp. 1145–1154, 2013.
[11] Gonzalez-Martinez, A., Rodriguez-Sanchez, A., Garcia-Ruiz, M.J., Muñoz-Palazon, B., Cortes-Lorenzo, C., Osorio, F. & Vahala, R., Performance and bacterial community dynamics of a CANON bioreactor acclimated from high to low operational temperatures. Chemical Engineering Journal, 287, pp. 557–567, 2016.
[12] Strous, M., Kuenen, J.G. & Jetten, M.S.M., Key Physiology of Anaerobic Ammonium Oxidation. Applied and Environmental Microbiology, 65(7), pp. 3248–3250, 1999.
[13] Vives, J.C.M.T., López, H., Ganigué, R., Ruscalleda, M., Sànchez*, A., Vila, X., López, R., mjesús llorens, Salamero, M., González, E., Jiménez, E., Balaguer, M.D. & Elorduy, M., Closing the Nitrogen Cycle from Urban Landfill Leachate by Biological Nitrogen Removal Over Nitrite and Thermal Treatment, 2003.
[14] Henze, M., Gujer, W., Mino, T. & van Loosedrecht, M., Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Publishing, 2006.
[15] Rustum, R., Modelling Activated Sludge Wastewater Treatment Plants Using Artificial Intelligence Techniques (Fuzzy Logic and Neural Networks). In Heriot-Watt University, 2009.
[16] Xie, B., Ma, Y.W., Wan, J.Q., Wang, Y., Yan, Z.C., Liu, L. & Guan, Z.Y., Modeling and multi-objective optimization for ANAMMOX process under COD disturbance using hybrid intelligent algorithm. Environmental Science and Pollution Research, 25(21), pp. 20956–20967, 2018.
[17] Dochain, D. & Pauss, A., On-line estimation of microbial specific growth-rates: An illustrative case study. The Canadian Journal of Chemical Engineering, 66(4), pp. 626–631, 1988.
[18] Hong, S.H., Lee, M.W., Lee, D.S. & Park, J.M., Monitoring of sequencing batch reactor for nitrogen and phosphorus removal using neural networks. Biochemical Engineering Journal, 35(3), pp. 365–370, 2007.
[19] W.H. Organization, Sustanable Development Goal 6, In United Nation, 2018.
[20] W.H. Organization, Sustainable Development Goal 7, In 2018.
Cite this:
APA Style
IEEE Style
BibTex Style
MLA Style
Chicago Style
GB-T-7714-2015
Nawaz, A., Arora, A. S., Yun, C. M., Cho, H., & Lee, M. (2019). Cost Effective Nitrogen Removal – Novel Control Strategies. Int. J. Comput. Methods Exp. Meas., 7(4), 376-384. https://doi.org/10.2495/CMEM-V7-N4-376-384
A. Nawaz, A. S. Arora, C. M. Yun, H. Cho, and M. Lee, "Cost Effective Nitrogen Removal – Novel Control Strategies," Int. J. Comput. Methods Exp. Meas., vol. 7, no. 4, pp. 376-384, 2019. https://doi.org/10.2495/CMEM-V7-N4-376-384
@research-article{Nawaz2019CostEN,
title={Cost Effective Nitrogen Removal – Novel Control Strategies},
author={Alam Nawaz and Amarpreet Singh Arora and Choa Mun Yun and Hwanchul Cho and Moonyong Lee},
journal={International Journal of Computational Methods and Experimental Measurements},
year={2019},
page={376-384},
doi={https://doi.org/10.2495/CMEM-V7-N4-376-384}
}
Alam Nawaz, et al. "Cost Effective Nitrogen Removal – Novel Control Strategies." International Journal of Computational Methods and Experimental Measurements, v 7, pp 376-384. doi: https://doi.org/10.2495/CMEM-V7-N4-376-384
Alam Nawaz, Amarpreet Singh Arora, Choa Mun Yun, Hwanchul Cho and Moonyong Lee. "Cost Effective Nitrogen Removal – Novel Control Strategies." International Journal of Computational Methods and Experimental Measurements, 7, (2019): 376-384. doi: https://doi.org/10.2495/CMEM-V7-N4-376-384
NAWAZ A， ARORA A S， YUN C M， et al. Cost Effective Nitrogen Removal – Novel Control Strategies[J]. International Journal of Computational Methods and Experimental Measurements， 2019， 7(4)： 376-384. https://doi.org/10.2495/CMEM-V7-N4-376-384