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Cairone S, Hasan SW, Choo KH, Li CW, Zarra T, Belgiorno V, Naddeo V. Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 944:173999. [PMID: 38879019 DOI: 10.1016/j.scitotenv.2024.173999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/28/2024] [Accepted: 06/12/2024] [Indexed: 06/20/2024]
Abstract
Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification. This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.
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Affiliation(s)
- Stefano Cairone
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, 84084 Fisciano, SA, Italy
| | - Shadi W Hasan
- Center for Membranes and Advanced Water Technology (CMAT), Department of Chemical and Petroleum Engineering, Khalifa University of Science and Technology, PO, Box 127788, Abu Dhabi, United Arab Emirates
| | - Kwang-Ho Choo
- Department of Environmental Engineering, Kyungpook National University (KNU), 80 Daehak-ro, Bukgu, Daegu 41566, Republic of Korea
| | - Chi-Wang Li
- Department of Water Resources and Environmental Engineering, Tamkang University, 151 Yingzhuan Road Tamsui District, New Taipei City 25137, Taiwan
| | - Tiziano Zarra
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, 84084 Fisciano, SA, Italy
| | - Vincenzo Belgiorno
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, 84084 Fisciano, SA, Italy
| | - Vincenzo Naddeo
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, 84084 Fisciano, SA, Italy.
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Kim J, Bae E, Park H, Park HJ, Shah SSA, Lee K, Lee J, Oh HS, Park PK, Shin YC, Moon H, Naddeo V, Choo KH. Membrane reciprocation and quorum quenching: An innovative combination for fouling control and energy saving in membrane bioreactors. WATER RESEARCH 2024; 250:121035. [PMID: 38154339 DOI: 10.1016/j.watres.2023.121035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/27/2023] [Accepted: 12/18/2023] [Indexed: 12/30/2023]
Abstract
Membrane bioreactors (MBRs) play a crucial role in wastewater treatment, but they face considerable challenges due to fouling. To tackle this issue, innovative strategies are needed. This study investigated the effectiveness of membrane reciprocation and quorum quenching (QQ) to control fouling in MBRs. The study compared MBRs using membrane reciprocation (30 rpm) and QQ (injecting media containing 100 or 200 mg/L BH4) with conventional MBRs employing different air-scouring intensities. The results demonstrated that combining membrane reciprocation (30 rpm) with QQ (200 mg/L BH4) significantly extended the service time of MBRs, making it approximately six times longer than conventional methods. Moreover, this approach reduced physically reversible resistance. The reduction in signal molecules related to biofouling due to QQ showcased its critical role in controlling biofouling, even under high shear caused by membrane reciprocation. However, the impact of QQ on microbial community structure appeared relatively insignificant when compared to factors such as operation time, aeration intensity, and membrane reciprocation. By combining membrane reciprocation and QQ, the study achieved a remarkable 81 % energy saving compared to extensive aeration (103 s-1 in velocity gradient), in addition to the extended service time. Importantly, this combined antifouling approach did not negatively affect microbial characteristics and wastewater treatment, emphasizing its effectiveness in MBRs. Overall, the findings of this study offer valuable insights for developing synergistic fouling control strategies in MBRs, significantly improving the energy efficiency of the wastewater treatment process.
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Affiliation(s)
- Jinwoo Kim
- School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
| | - Eunjin Bae
- School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea; Daegu Metropolitan City Waterworks Headquarters (Water Quality Research Institute), 176 Dangsan-ro, Dalseo-gu, Daegu 42650, Republic of Korea
| | - Hyeona Park
- Advanced Institute of Water Industry, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
| | - Hyung-June Park
- School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
| | - Syed Salman Ali Shah
- School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea; Center for Water Research, Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, Guangdong 519087, China
| | - Kibaek Lee
- Department of Biotechnology and Bioengineering, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Jaewoo Lee
- Department of Polymer-Nano Science and Technology, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea
| | - Hyun-Suk Oh
- Department of Environmental Engineering, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea
| | - Pyung-Kyu Park
- Department of Environmental and Energy Engineering, Yonsei University, Wonju, Gangwon-do 26439, Republic of Korea
| | - Yong Cheol Shin
- HifilM, 24 Deokseongsandan 2-ro, Idong-eup, Cheoin-gu, Yongin, Gyeonggi-do 17130, Republic of Korea
| | - HeeWan Moon
- HifilM, 24 Deokseongsandan 2-ro, Idong-eup, Cheoin-gu, Yongin, Gyeonggi-do 17130, Republic of Korea
| | - Vincenzo Naddeo
- Sanitary Environmental Engineering Division, Department of Civil Engineering, University of Salerno, via Giovanni Paolo II, Fisciano, SA 84084, Italy
| | - Kwang-Ho Choo
- School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea; Advanced Institute of Water Industry, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea; Department of Environmental Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea.
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Kim J, Hua C, Kim K, Lin S, Oh G, Park MH, Kang S. Optimizing coagulant dosage using deep learning models with large-scale data. CHEMOSPHERE 2024; 350:140989. [PMID: 38135126 DOI: 10.1016/j.chemosphere.2023.140989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/15/2023] [Accepted: 12/17/2023] [Indexed: 12/24/2023]
Abstract
Water treatment plants are facing challenges that necessitate transition to automated processes using advanced technologies. This study introduces a novel approach to optimize coagulant dosage in water treatment processes by employing a deep learning model. The study utilized minute-by-minute data monitored in real time over a span of five years, marking the first attempt in drinking water process modeling to leverage such a comprehensive dataset. The deep learning model integrates a one-dimensional convolutional neural network (Conv1D) and gated recurrent unit (GRU) to effectively extract features and model complex time-series data. Initially, the model predicted coagulant dosage and sedimentation basin turbidity, validated against a physicochemical model. Subsequently, the model optimized coagulant dosage in two ways: 1) maintaining sedimentation basin turbidity below the 1.0 NTU guideline, and 2) analyzing changes in sedimentation basin turbidity resulting from reduced coagulant dosage (5-20%). The findings of the study highlight the effectiveness of the deep learning model in optimizing coagulant dosage with substantial reductions in coagulant dosage (approximately 22% reduction and 21 million KRW/year). The results demonstrate the potential of deep learning models in enhancing the efficiency and cost-effectiveness of water treatment processes, ultimately facilitating process automation.
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Affiliation(s)
- Jiwoong Kim
- Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea; Korea Water Resources Corporation (k-water), 200 Sintanjin-ro, Daedeok-gu, Deajeon, 34350, South Korea
| | - Chuanbo Hua
- Department of Industrial and System Engineering, Korea Advanced Institute of Science and Technology, South Korea
| | - Kyoungpil Kim
- Korea Water Resources Corporation (k-water), 200 Sintanjin-ro, Daedeok-gu, Deajeon, 34350, South Korea
| | - Subin Lin
- Department of the Built Environment, College of Design and Engineering, National University of Singapore, 4 Architecture Drive, 117566, Singapore
| | - Gunhak Oh
- Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea
| | - Mi-Hyun Park
- Department of Civil and Environmental Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul, 04620, South Korea.
| | - Seoktae Kang
- Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.
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Niu X, Bi X, Gu R, Yin Z, Yang B, Liu C. Modification of a plant-scale semi-centralized wastewater treatment system to enhance nitrogen and phosphorus removal from black water. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2023; 87:2005-2019. [PMID: 37119169 DOI: 10.2166/wst.2023.100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Owing to the low ratio of chemical oxygen demand to total nitrogen (SCOD/TN), effective removal of nutrient pollutants from black water is difficult. In this study, to enhance nitrogen and phosphorus removal from such wastewater, a series of operational modification strategies was investigated and applied to a plant-scale semi-centralized system used for black water treatment. The results showed that 21 mg Fe3+/L was the optimal dosage for the chemical-enhanced pretreatment process, achieving average removal efficiencies of 51.1 and 74.1% for organics and phosphorus, respectively, with a slight enhancement in nitrogen removal by 2.3%. However, nitrogen and phosphorus removal could be further enhanced to 88 and 96%, by the addition of carbon sources in the post-anoxic zone of the reversed anaerobic-anoxic-aerobic process. Contrastingly, neither the addition of carbon sources in the pre-anoxic zone nor the prolongation of the time for pre-denitrification could significantly improve the nitrogen and phosphorus removal efficiencies. Furthermore, reducing the aeration intensity promoted simultaneous nitrification and denitrification in aerobic reactors, thereby making it a potential energy-saving method for system operation.
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Affiliation(s)
- Xinyuan Niu
- Qingdao University of Technology, 777 Jialingjiang East Road, Huangdao District, Qingdao 266520, China E-mail:
| | - Xuejun Bi
- Qingdao University of Technology, 777 Jialingjiang East Road, Huangdao District, Qingdao 266520, China E-mail:
| | - Ruihuan Gu
- Qingdao Water Group Co., Ltd, 22 Ningde Road, Laoshan District, Qingdao 266071, China
| | - Zhixuan Yin
- Qingdao University of Technology, 777 Jialingjiang East Road, Huangdao District, Qingdao 266520, China E-mail:
| | - Benliang Yang
- Qingdao University of Technology, 777 Jialingjiang East Road, Huangdao District, Qingdao 266520, China E-mail:
| | - Changqing Liu
- Qingdao University of Technology, 777 Jialingjiang East Road, Huangdao District, Qingdao 266520, China E-mail:
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Materials discovery of ion-selective membranes using artificial intelligence. Commun Chem 2022; 5:132. [PMID: 36697945 PMCID: PMC9814132 DOI: 10.1038/s42004-022-00744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 09/29/2022] [Indexed: 01/28/2023] Open
Abstract
Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.
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Woo T, Nam K, Heo S, Lim JY, Kim S, Yoo C. Predictive maintenance system for membrane replacement time detection using AI-based functional profile monitoring: Application to a full-scale MBR plant. J Memb Sci 2022. [DOI: 10.1016/j.memsci.2022.120400] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Heo S, Nam K, Woo T, Yoo C. Digitally-transformed early-warning protocol for membrane cleaning based on a fouling-cumulative sum chart: Application to a full-scale MBR plant. J Memb Sci 2022. [DOI: 10.1016/j.memsci.2021.120080] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Multivariable identification of membrane fouling based on compacted cascade neural network. Chin J Chem Eng 2022. [DOI: 10.1016/j.cjche.2022.01.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Odriozola M, van Lier JB, Spanjers H. Optimising the Flux Enhancer Dosing Strategy in a Pilot-Scale Anaerobic Membrane Bioreactor by Mathematical Modelling. MEMBRANES 2022; 12:membranes12020151. [PMID: 35207073 PMCID: PMC8877340 DOI: 10.3390/membranes12020151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/20/2022] [Accepted: 01/22/2022] [Indexed: 11/30/2022]
Abstract
Flux enhancers (FEs) have been successfully applied for fouling mitigation in membrane bioreactors. However, more research is needed to compare and optimise different dosing strategies to improve the filtration performance, while minimising the use of FEs and preventing overdosing. Therefore, the goal of this research is to develop an optimised control strategy for FE dosing into an AnMBR by developing a comprehensive integrated mathematical model. The integrated model includes filtration, flocculation, and biochemical processes to predict the effect of FE dosing on sludge filterability and membrane fouling rate in an AnMBR. The biochemical model was based on an ADM1, modified to include FEs and colloidal material. We developed an empirical model for the FE-induced flocculation of colloidal material. Various alternate filtration models from the literature and our own empirical models were implemented, calibrated, and validated; the best alternatives were selected based on model accuracy and capacity of the model to predict the effect of varying sludge characteristics on the corresponding output, that is fouling rate or sludge filterability. The results showed that fouling rate and sludge filterability were satisfactorily predicted by the selected filtration models. The best integrated model was successfully applied in the simulation environment to compare three feedback and two feedforward control tools to manipulate FE dosing to an AnMBR. The modelling results revealed that the most appropriate control tool was a feedback sludge filterability controller that dosed FEs continuously, referred to as ∆R20_10. Compared to the other control tools, application of the ∆R20_10 controller resulted in a more stable sludge filterability and steady fouling rate, when the AnMBR was subject to specific disturbances. The simulation environment developed in this research was shown to be a useful tool to test strategies for dosing flux enhancer into AnMBRs.
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Review of New Approaches for Fouling Mitigation in Membrane Separation Processes in Water Treatment Applications. SEPARATIONS 2021. [DOI: 10.3390/separations9010001] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
This review investigates antifouling agents used in the process of membrane separation (MS), in reverse osmosis (RO), ultrafiltration (UF), nanofiltration (NF), microfiltration (MF), membrane distillation (MD), and membrane bioreactors (MBR), and clarifies the fouling mechanism. Membrane fouling is an incomplete substance formed on the membrane surface, which will quickly reduce the permeation flux and damage the membrane. Foulant is colloidal matter: organic matter (humic acid, protein, carbohydrate, nano/microplastics), inorganic matter (clay such as potassium montmorillonite, silica salt, metal oxide, etc.), and biological matter (viruses, bacteria and microorganisms adhering to the surface of the membrane in the case of nutrients) The stability and performance of the tested nanometric membranes, as well as the mitigation of pollution assisted by electricity and the cleaning and repair of membranes, are reported. Physical, chemical, physico-chemical, and biological methods for cleaning membranes. Biologically induced biofilm dispersion effectively controls fouling. Dynamic changes in membrane foulants during long-term operation are critical to the development and implementation of fouling control methods. Membrane fouling control strategies show that improving membrane performance is not only the end goal, but new ideas and new technologies for membrane cleaning and repair need to be explored and developed in order to develop future applications.
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Gkotsis P, Banti D, Pritsa A, Mitrakas M, Samaras P, Peleka E, Zouboulis A. Effect of Operating Conditions on Membrane Fouling in Pilot-Scale MBRs; Filaments Growth, Diminishing Dissolved Oxygen and Recirculation Rate of the Activated Sludge. MEMBRANES 2021; 11:membranes11070490. [PMID: 34210095 PMCID: PMC8303956 DOI: 10.3390/membranes11070490] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/24/2021] [Accepted: 06/28/2021] [Indexed: 11/25/2022]
Abstract
This is the first study that examines the effect of operating conditions on fouling of Membrane Bio-Reactors (MBRs), which treat municipal wastewater in field conditions, with specific regard to the controlled development of filamentous microorganisms (or filaments). The novelty of the present work is extended to minimize the dissolved oxygen (DO) in recirculated activated sludge for improving the process of denitrification. For this purpose, two pilot-scale MBRs were constructed and operated in parallel: (i) Filament-MBR, where an attempt was made to regulate the growth of filaments by adjustment of DO, the Food-to-Microorganisms (F/M) ratio and temperature, and (ii) Control-MBR, where a gentle stirring tank was employed for the purpose of zeroing the DO in the recycled sludge. Results showed that low temperature (<15 °C) slightly increased the number of filaments in the Filament-MBR which, in turn, decreased the Trans-Membrane Pressure (TMP). As the Soluble Microbial Products (SMP) and the colloids are considered to be the basic foulants of membranes in MBR systems, specific attention was directed to keep their concentration at low values in the mixed liquor. The low F/M ratio in the aeration tanks which preceded the membrane tank was achieved to keep the SMP proteins and carbohydrates at very low values in the mixed liquor, i.e., less than 6 mg/L. Moreover, as a result of the low recirculation rate (2.6∙Qin), good aggregation of the produced excess sludge was achieved, and low concentration of colloids with a size ≤50 nm (nearly the membranes’ pore size used for filtration/separation) was measured, accounted for maximum 15% of the total colloids. Additionally, the increase in filamentous population at the Filament-MBR contributed to the further reduction of colloids in the mixed liquor at 7.9%, contributing beneficially to the reduction of TMP and of membrane fouling. The diminishing of DO in the recirculated sludge improved denitrification, and resulted in lower concentrations of Ν-NO3− and TN in the effluent of the Control-MBR. Furthermore, the recirculation rate of Qr = 2.6∙Qin, in comparison with Qr = 4.3∙Qin, resulted in improved performance regarding the removal of N-NH4+. Finally, high organics removal and ammonium nitrification was observed in the effluent of both pilots, since COD and Ν-ΝH4+ concentrations were generally in the range of 10–25 mg/L and <0.1 mg/L, respectively.
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Affiliation(s)
- Petros Gkotsis
- Laboratory of Chemical and Environmental Technology, Department of Chemistry, Faculty of Sciences, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (P.G.); (E.P.)
| | - Dimitra Banti
- Laboratory of Technologies of Environmental Protection and Utilization of Food By-Products, Department of Food Science and Technology, International Hellenic University, GR-57400 Thessaloniki, Greece; (D.B.); (P.S.)
| | - Anastasia Pritsa
- Analytic Chemistry Laboratory, Department of Chemical Engineering, School of Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (A.P.); (M.M.)
| | - Manassis Mitrakas
- Analytic Chemistry Laboratory, Department of Chemical Engineering, School of Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (A.P.); (M.M.)
| | - Petros Samaras
- Laboratory of Technologies of Environmental Protection and Utilization of Food By-Products, Department of Food Science and Technology, International Hellenic University, GR-57400 Thessaloniki, Greece; (D.B.); (P.S.)
| | - Efrosini Peleka
- Laboratory of Chemical and Environmental Technology, Department of Chemistry, Faculty of Sciences, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (P.G.); (E.P.)
| | - Anastasios Zouboulis
- Laboratory of Chemical and Environmental Technology, Department of Chemistry, Faculty of Sciences, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (P.G.); (E.P.)
- Correspondence: ; Tel.: +30-2310-997-794
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