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Treatment and hybrid modeling of domestic reverse osmosis concentrate using biological activated carbon
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Treatment and hybrid modeling of domestic reverse osmosis concentrate using biological activated carbon Ching Kwek Pooi, Vincent Loka, How Yong Ng⁎ Centre for Water Research, Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
GRAPHICAL ABSTRACT
ARTICLE INFO
ABSTRACT
Keywords: Hybrid modeling Reverse osmosis concentrate Machine learning Biological activated carbon
The simulation and removal of organics from municipal reverse osmosis concentrate (ROC) using biological activated carbon (BAC) was investigated. BAC treatment of ROC achieved a TOC removal of 25.5% and 28.1% for empty bed contact time (EBCT) of 40 min and 60 min, respectively. Batch studies revealed that the TOC removal followed a first order reaction with a rate constant of 2.735 at R2 > 0.98. Most of the organics were removed within 80 min of treatment, with an average non-biodegradable fraction of 0.6453. The results obtained from the batch studies were then used for simulation of the continuous lab-scale BAC reactors, yielding average relative deviations of 7%–8% and normalized root mean square errors (RMSE) of 0.09–0.1. Results from a third reactor with EBCT of 80 min were used to train and verify a machine learning model. The model was then used for calculating the non-biodegradable organics fraction of ROC, which will be fed into the first order reaction model. The resulting serial hybrid modeling further reduced the average relative deviations to 5%–6% and a normalized RMSE of 0.08.
1. Introduction Wastewater reclamation is an attractive approach to address the increasing global water supply need. Membrane processes such as reverse osmosis (RO) process, have been deployed worldwide for water reclamation and recovery. The main removal mechanism of membrane
⁎
process is size exclusion. The membrane provides a physical barrier to contaminants larger than its pore size, retaining them in the retentate [1]. Due to its high rejection capability, RO process is able to produce high quality water, suitable for both potable and industrial usage. In a municipal secondary effluent recovery, 75–85% of the feed is reclaimed, with the remaining portion discharged as reverse osmosis
Corresponding author. E-mail address: [email protected] (H.Y. Ng).
https://doi.org/10.1016/j.desal.2019.06.013 Received 22 February 2019; Received in revised form 8 June 2019; Accepted 17 June 2019 0011-9164/ © 2019 Elsevier B.V. All rights reserved.
Please cite this article as: Ching Kwek Pooi, Vincent Loka and How Yong Ng, Desalination, https://doi.org/10.1016/j.desal.2019.06.013
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Nomenclature ARD BAC BOD EBCT EEM GAC
LC-OCD RMSE ROC SUVA TN TIC TOC
Average relative deviation Biological activated carbon Biochemical oxygen demand Empty bed contact time Excitation emission matrix Granular activated carbon
concentrate (ROC). The ROC generated contains elevated concentration of organic and inorganic contaminants. The organics present are mainly end products of biological processes, thus, are recalcitrant and difficult to breakdown [2]. Surface disposal and sewer disposal are widely used for disposal of ROC [3]. With proper treatment, high quality water can still be reclaimed from ROC. Several approaches had shown success in ROC treatment; ion exchange, electrocoagulation and nanofiltration processes were able to reduce the inorganic content while chemical coagulation, electrocoagulation, nanofiltration and adsorption process were able to reduce the organic content in ROC [4]. However, these physical/chemical processes are costly for brine treatment. Numerous studies had been done on the biological treatment of ROC. ROC had been used for microalgae cultivation while removing nitrogen, phosphorous and hardness [5,6]. Treatment of ROC using biological activated carbon (BAC) has also been explored to remove micropollutants [7] and organic contaminants [2,8,9]. BAC has been widely used for drinking water treatment [10]. It consists of carbon adsorption and biodegradation whereby organics are adsorbed onto the activated carbon and degraded by the microorganism attached. This allows slowly biodegradable organics to be adsorbed first and then consumed by the microorganisms. Adsorption also increases the biological utilization of the microorganism because substrate is available in both the adsorbed phase and liquid phase [11]. Adsorption also allows for adsorption-induced acclimation of microorganisms, allowing toxic compounds degradation [11,12]. Many studies had been done to model BAC treatment, with first order kinetics being the most commonly employed kinetic model [10,13]. Numerous methods had also been done to improve model simulation. In the past decade, with sufficiently large data, machine learning had been used for the simulation and prediction of wastewater treatment. Studies had shown that machine learning is a reliable method for water quality prediction [14–16]. Hybrid model had also been proposed, where machine learning is used in conjunction with mechanistic model. The incorporation of machine learning had shown to improve the prediction of the mechanistic model significantly [17–19]. In this study, three BAC column reactors were used for the long-term study on treatment of ROC. BAC had been chosen as a cost effective process to treat ROC [4,13]. Batch studies was conducted to determine the kinetics of the biodegradation of organics in the ROC by BAC. Results from the batch studies were then used for the modeling and simulation of treatment of ROC using BAC. Lastly, machine learning will be incorporated into the model to improve the prediction and performance of the model.
Liquid chromatography- organic carbon detection Root mean square error Reverse osmosis concentrate Specific UV254 absorbance Total nitrogen Total inorganic carbon Total organic carbon
prior to usage. 2.2. Lab-scale BAC column reactor Three identical lab-scale BAC column reactors were setup to treat the ROC collected. The BAC column had a diameter of 50 mm and an effective packing height of 400 mm. Packing material used was granular activated carbon (Norit 1240 W, Cabotcorp, USA). It has an effective diameter of 0.6–0.7 mm and density of 470 kg/m3. The ROC entered from the bottom of the column as influent and exit the reactor from the top via an overflow outlet. The empty bed contact time (EBCT) and daily flow rate of each reactor are shown in Table 1. In this study, reactor R3 was operated for 75 d. The EBCT of reactor R3 was 80 min as it was deduced from the kinetic study (Section 3.1) that most of the biodegradable TOC was removed by 80 min. The results obtained from R3 were used for machine learning for training and verification. Prior to this study, all three reactors had been in operation for more than a year and had reached breakthrough with ROC as feed. 2.3. Water quality analysis Water quality of the influent ROC and BAC treated ROC were characterized for TOC, TIC, UV254, pH, conductivity, turbidity and BOD5. TOC, TIC and TN were measured using a total organic carbon analyzer (TOC-VCSH, Shimadzu, Japan) and UV absorbance (UV254) was measured using a spectrophotometer (DR5000, Hach, USA). pH and conductivity were quantified using a portable pH/conductivity meter (Ultrameter II, Myron L, USA). Turbidity was determined using a turbidimeter (2100N, Hach, USA). Five-day biochemical oxygen demand (BOD5) was done in accordance with the Standard methods for the Examination of Water and Wastewater [20]. Organics in influent ROC and BAC treated ROC were characterized using EEM and LC-OCD. EEM was done using the Cary Eclipse fluorescence spectrophotometer (Agilent Technologies, U.S.A.) and LC-OCD was quantified using the LCOCD analyzer (DOC-Labor Dr. Huber, Germany). 2.4. Kinetic determination, modeling and simulation Kinetic study was conducted to evaluate the removal of TOC of the ROC. The TOC results were fitted using the exponential growth function with iteration algorithm of Levenberg Marquardt for the non-linear least square curve fitting. The equation of this exponential growth function is shown in Eq. (1) as below: (1)
[TOC ]t = X + Y *e k * t
2. Materials and methods
where t is time (h), [TOC]t represents the normalized TOC concentration at time t, k is the rate constant for TOC removal (h−1). X represents the non-biodegradable TOC (normalized) while Y represents the
2.1. ROC samples ROC samples were collected weekly from the second-stage of a RO process in a water reclamation plant reclaiming treated secondary effluent in Singapore. The secondary effluent was produced from a membrane bioreactor treating municipal wastewater. The characteristic of the ROC collected is summarized in Table 3 and its corresponding TOC is shown in Fig. 4. Once collected from the plant, the ROC was stored in the cold room at 4 °C. ROC was brought to room temperature
Table 1 EBCT and daily flow rate of R1, R2 & R3.
EBCT (min) Daily flow rate (L)
2
R1
R2
R3
40 28.28
60 18.85
80 14.14
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Fig. 1. Serial hybrid modeling for prediction of non-biodegradable TOC.
Fig. 2. Kinetic study of TOC removal of (a) Run 1, (b) Run 2, (c) Run 3 and (d) 2nd order fit. Table 2 Kinetic parameter derived for TOC removal using BAC.
Table 3 Water quality of influent ROC and BAC treated ROC.
Parameter
Run 1
Run 2
Run 3
Average
Parameter
ROC
R1 effluent
R2 effluent
Influent TOC (mg/L) X (normalized) Y (normalized) k (h−1) R2
21.16 0.63482 0.35389 −2.95705 0.98429
17.73 0.63991 0.36280 −2.27859 0.99694
19.84 0.66119 0.34061 −2.97006 0.99456
19.58 0.6453 0.35243 −2.7352 –
pH Conductivity (μS/cm) Turbidity (NTU) Total Organic Carbon (mg/ L) Total Nitrogen (mg/L) UV254 SUVA BOD (mg/L)
7.35 ± 0.33 2170 ± 260 0.751 ± 0.339 23.1 ± 4.0
7.27 ± 0.27 2209 ± 471 0.448 ± 0.065 17.2 ± 3.0
7.27 ± 0.24 2170 ± 423 0.460 ± 0.058 16.6 ± 2.9
29.3 ± 11.6 0.413 ± 0.090 1.788 ± 0.447 <2.0
26.9 ± 11.1 0.333 ± 0.066 1.936 ± 0.189 <2.0
26.9 ± 11.2 0.312 ± 0.061 1.880 ± 0.180 <2.0
biodegradable TOC (normalized). Modeling and simulation of the treatment of ROC using BAC were done using the results obtained from the kinetic study. Modeling were done using the python language with the Pandas package. Euler method was used to find the numerical approximation to the differential equation. The assumptions used in this simulation are as follows:
series to mimic the performance of a plug flow reactor. Serial hybrid modeling was used in the second phase of the experiment. The schematic diagram of the serial hybrid model is summarized in Fig. 1. Data obtained from reactor R3 was used for machine
1. The BAC reactor was modeled as a plug flow reactor. 2. The model used 4 continuous stirred tank reactors connected in 3
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Fig. 3. (a) EEM graph of ROC, with each regions labelled; (b) EEM graph of R1 effluent; and (c) EEM graph of R2 effluent.
conductivity was used as input for the machine learning. The machine learning will then calculate its corresponding non-biodegradable TOC fraction, which will be used for the first order reaction model. Machine learning was done using Microsoft Azure machine learning studio. Boosted decision tree regression was used with maximum of 30 leaves per tree and a learning rate of 0.44.
Table 4 Percentage distribution of each region in the EEM of ROC, R1 effluent and R2 effluent. Region I II III IV V
Organic nature Aromatic Protein I Aromatic Protein II Fulvic acid-like Soluble microbial-byproduct-like Humic acid-like
ROC (%)
R1 effluent (%)
R2 effluent (%)
6.83 20.70 26.86 20.13
6.48 19.18 30.22 16.62
6.76 17.87 31.11 16.93
25.48
27.50
27.33
3. Results and discussion 3.1. Kinetic study Three batch studies, using different batch of ROC, were done to determine the kinetic of TOC removal of ROC. The BAC used had achieved breakthrough prior to the batch studies, thus, results from the batch studies reflect the BAC's biological treatment of ROC. Results were then fitted using zero-order, first-order and second-order reaction. Results for zero-order reaction (R2 < 0.8) and second-order reaction
learning to predict the non-biodegradable TOC. The machine learning was then used for predicting the non-biodegradable TOC for Day 75–Day 230 using the influent's parameters. Wastewater characteristic such as TOC, TIC, TN, UV254 and 4
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Table 5 LC-OCD results of ROC, R1 effluent and R2 effluent. Sample
ROC R1 effluent R2 effluent
TOC
22.4 ± 5.1 16.7 ± 1.9 15.9 ± 1.5
Hydrophobic organic carbon
21.69% ± 2.53% 22.25% ± 5.82% 18.52% ± 2.76%
Hydrophilic organic carbon
78.30% ± 2.53% 77.75% ± 5.82% 81.48% ± 2.76%
Molecular weight >20,000
~1000
Bio-polymer
Humic substances
1.60% ± 0.69% 1.26% ± 0.65% 1.42% ± 0.77%
41.53% ± 5.28% 44.22% ± 5.00% 50.96% ± 4.83%
(R2 < 0.92) were deemed unsatisfactory while results for first-order reaction yielded the best fit, with R2 > 0.98. In addition, organics removal by BAC had often been reported to follow the first order reaction [10,13]. Hence, first-order reaction was proposed to be used in the model. Fig. 2 shows the results for each run and their corresponding fit. Table 2 summarizes the kinetic results. Though the organics in ROC are recalcitrant and difficult to degrade, the kinetic constant, k, is relatively high, having an average value of 2.73 h−1. More than 90 % of the biodegradable organics in ROC are removed within the first hour. These findings coincide with the study by Ying and Weber [11]. The non-biodegradable TOC portion, X, has an average value of 0.6453, indicating that although the BOD of ROC is <2.0 mg/L, some of the organics in ROC can still be degraded. The high non-biodegradable TOC portion also indicated that pretreatment, such as advanced oxidation process, may be needed in order to increase the biodegradability of ROC. Various AOP such as ozone and UV/H2O2, had shown great potential in the pretreatment of ROC before feeding into the BAC column [8,21]. From the kinetic results, it can also be deduced that most of the biodegradable organics were removed within 80 min of treatment.
300–500
<350
Aromaticity
Building blocks
Low molecular weight neutrals
1.46 ± 0.28 1.31 ± 0.26 1.45 ± 0.30
15.90% ± 3.17% 15.02% ± 3.66% 12.79% ± 3.41%
19.28% ± 2.50% 17.07% ± 2.86% 16.07% ± 2.52%
Low molecular weight acids 0% 0% 0%
wastewater treatment. After BAC treatment, the percentage of aromatic protein II and soluble microbial-by-product-like organics decreased, with an increase in percentage of humic acid-like and fulvic acid-like organics, indicating that BAC mainly consumed aromatic protein II and soluble microbial-by-products. Organic characterization of ROC, R1 effluent and R2 effluent using LC-OCD are tabulated in Table 5. Similar to the EEM results, LC-OCD analysis revealed that the percentage of humic substances increased after BAC treatment. Bio-polymer, building blocks and low molecular weight (neutral) percentage decreased after BAC treatment. Another observation from the LC-OCD result was R1 effluent's hydrophobic organic carbon percentage increased, while R2 effluent's hydrophobic organic carbon percentage decreased. This observation suggested that the microorganisms in BAC consumed the hydrophilic organic carbon first, before consuming the hydrophobic organic carbon. 3.3. Simulation of TOC removal of ROC using BAC Results from the kinetic study were applied into the model. The time series simulation results of R1 and R2 are summarized in Fig. 4(b) and (d), while the simulated results against the measured results of R1 and R2 are shown in Fig. 4(c) and (e), respectively. The statistical results of the simulation (Day 75–Day 230) are shown in Table 6. The statistical result revealed that the model was a good fit, having a low ARD of 7.84% and 7.02% for R1 and R2, respectively. The model also has a low normalized RMSE value (<0.1). Fig. 4(b) and (d) shows that the simulated TOC follows closely to that of measured TOC. The model was able to simulate a surge in the organic concentration in the effluent due to a surge in the organics in the influent on Day 96. This also shows that even though there was a sudden increase in TOC in the influent, the inert TOC fraction of the influent and decay rate constant remained the same. From Fig. 4(c) and (e), it can be deduced that the simulated TOC in both the effluents of R1 and R2 were slightly higher than those of the measured TOC. This could be due to an overestimation of inert TOC, resulting in a lower fraction of biodegradable TOC.
3.2. Treatment of ROC using BAC The performance of BAC over the 230 d of operation is summarized in Table 3. The pH, BOD and conductivity of the effluents of both R1 and R2 did not differ much as those of the ROC. This was within expectation as the BAC reactors were not designed to remove inorganic dissolved contaminants. Approximately 10% TN removal was observed, similar to observation by Ng et al. [22]. TN removal may be attributed by mineralization, reduction of inorganic N and utilization for biomass growth [9]. TOC removal was approximately 25.4% and 27.9% for R1 and R2, respectively. The long-term results coincided with the findings by the batch study, where an increase in EBCT increased the TOC removal. This result is similar to the finding by Ng et al. [22] (25%) and slightly higher than the finding by Lu et al. [9] (15%).The TOC concentration of the effluents of R1 and R2 are summarized in Fig. 4(b) and (d), respectively. The EEM figures of ROC, R1 effluent and R2 effluent are presented in Fig. 3. EEMs can be delineated into 5 regions [23]. The percentage distribution of each region of EEM of ROC, R1 effluent and R2 effluent are summarized in Table 4. From Fig. 3, ROC had an overall higher peak intensity for all 5 regions in the EEM figures as compared to the BAC-treated effluents, indicating that BAC treatment brought about an overall decrease in in peak intensity for all 5 regions in the EEM figures of BAC treated effluents as compared to ROC. The intensity of Region III and Region V of ROC, R1 effluent and R2 effluent were higher than the other regions indicating high amount of humic acid-like and fulvic acidlike organics. Using the method by Chen [23], the percentage distribution of each region can be quantified. As seen from Table 4, quantification of the EEM figures revealed that majority of the organics in ROC were indeed fulvic acid-like and humic acid-like, followed by soluble microbial-by-product-like and aromatic protein II. This was within expectation as ROC were mainly waste product from domestic
3.4. Hybrid modeling of ROC treatment Serial hybrid modeling was used to further improve the model and reduce the ARD and normalized RMSE. Kinetic study revealed that most of the organics were removed within 80 min of treatment. Thus, results from reactor R3 were used for machine learning to relate the inert TOC to the influent characteristic. 80% of the data set was sampled randomly to train the model while the remaining 20% were used for verification. The machine learning model was then used for prediction of inert TOC fraction of ROC from Day 75–Day 230. The data obtained from the machine learning model was then used in the first order reaction model. Results from the hybrid model are shown in Fig. 4(f), (g), (h) and (i) while the statistical results are summarized in Table 6. The prediction performance for TOC concentration improved for the hybrid model as compared to the first order reaction model only. Hybrid model was also 5
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Fig. 4. (a) TOC concentration of influent ROC; (b) Simulation and treatment of ROC of R1; (c) Simulated effluent TOC against measured effluent TOC of R1; (d) Simulation and treatment of ROC of R2; (e) Simulated effluent TOC against measured effluent TOC of R2; (f) Serial hybrid modeling of treatment of ROC of R1; (g) Simulated effluent TOC (hybrid model) against measured effluent TOC of R1; (h) Serial hybrid modeling of treatment of ROC of R2; and (i) Simulated effluent TOC (hybrid model) against measured effluent TOC of R2.
6
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Fig. 4. (continued)
Declaration of Competing Interest
Table 6 Statistical results (Day 75–230) for Reactor 1 and Reactor 2. Modeling First order reaction model only
Serial Hybrid Modeling
Average relative deviation (%) Root mean square error (RMSE) Normalized RMSE Correlation Average relative deviation (%) RMSE Normalized RMSE Correlation
Reactor 1
Reactor 2
7.84
7.02
1.756
1.599
0.099 0.9292 5.93
0.093 0.9106 6.54
1.407 0.079 0.9259
1.449 0.085 0.8973
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capable of simulating the surge in TOC concentration in Day 96. Hybrid model resulted in lower RMSE and ARD, indicating a more accurate simulation. ARD was reduced from 7.84 to 5.93 and 7.02 to 6.54 for reactor R1 and R2, respectively. RMSE was reduced from 0.099 to 0.079 for R1 and 0.093 to 0.085 for R2. However, the correlation between measured effluent TOC and simulated effluent TOC dropped slightly for the hybrid model. 4. Conclusion Hybrid modeling of treatment of ROC using BAC had shown promising results. Average TOC removal efficiencies of 25.4% and 27.9% was achieved for EBCT of 40 min and 60 min, respectively. Approximately 10% TN removal was achieved. EEM and LC-OCD results revealed that BAC mainly consumed the low molecular weight organics and biopolymers, resulting in an increased in fraction of humic substances in the organics. Batch study revealed that treatment of ROC using BAC followed a first order reaction, with R2 value of >0.98. The average decay rate constant was −2.735 h−1 and the average inert TOC fraction was 0.6453. Modeling and simulation of the BAC treatment using the results from the kinetic study yielded good fit, with an ARD of <8% and normalized RMSE of <0.1. Hybrid modeling further reduced the ARD to <7% and normalized RMSE of <0.085. However, hybrid modeling resulted in a slight reduction in the correlation between the simulated results and measured results. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 7
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C.K. Pooi, et al. full-scale cokes wastewater treatment plant, J. Biotechnol. 115 (2005) 317–328, https://doi.org/10.1016/j.jbiotec.2004.09.001. [18] D.S. Lee, C.O. Jeon, J.M. Park, K.S. Chang, Hybrid neural network modeling of a full-scale industrial wastewater treatment process, Biotechnol. Bioeng. 78 (2002) 670–682, https://doi.org/10.1002/bit.10247. [19] M. Cote, B. Grandjean, P. Lessard, J. Thibault, Dynamic modelling of the activated sludge process: improving prediction using neural networks, Water Res. 29 (1995) 995–1004 http://www.sciencedirect.com/science/article/pii/ 004313549593250W. [20] APHA, Standard Methods for the Examination of Water & Wastewater, American Public Health Association, 2005, https://books.google.com.sg/books?id=
buTn1rmfSI4C. [21] S. Pradhan, L. Fan, F.A. Roddick, Removing organic and nitrogen content from a highly saline municipal wastewater reverse osmosis concentrate by UV/H2O2-BAC treatment, Chemosphere 136 (2015) 198–203, https://doi.org/10.1016/j. chemosphere.2015.05.028. [22] H.Y. Ng, L.Y. Lee, S.L. Ong, G. Tao, B. Viawanath, K. Kekre, W. Lay, H. Seah, Treatment of RO brine-towards sustainable water reclamation practice, Water Sci. Technol. 58 (2008) 931–936, https://doi.org/10.2166/wst.2008.713. [23] C. Wen, W. Paul, J.A. Leenheer, B. Karl, Fluorescence excitation-emission matrix regional integration to quantify spectra for dissolved organic matter, Environ. Sci. Technol. 37 (2015) 5701–5710.
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Treatment and hybrid modeling of domestic reverse osmosis concentrate using biological activated carbon Ching Kwek Pooi, Vincent Loka, How Yong Ng⁎ Centre for Water Research, Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
GRAPHICAL ABSTRACT
ARTICLE INFO
ABSTRACT
Keywords: Hybrid modeling Reverse osmosis concentrate Machine learning Biological activated carbon
The simulation and removal of organics from municipal reverse osmosis concentrate (ROC) using biological activated carbon (BAC) was investigated. BAC treatment of ROC achieved a TOC removal of 25.5% and 28.1% for empty bed contact time (EBCT) of 40 min and 60 min, respectively. Batch studies revealed that the TOC removal followed a first order reaction with a rate constant of 2.735 at R2 > 0.98. Most of the organics were removed within 80 min of treatment, with an average non-biodegradable fraction of 0.6453. The results obtained from the batch studies were then used for simulation of the continuous lab-scale BAC reactors, yielding average relative deviations of 7%–8% and normalized root mean square errors (RMSE) of 0.09–0.1. Results from a third reactor with EBCT of 80 min were used to train and verify a machine learning model. The model was then used for calculating the non-biodegradable organics fraction of ROC, which will be fed into the first order reaction model. The resulting serial hybrid modeling further reduced the average relative deviations to 5%–6% and a normalized RMSE of 0.08.
1. Introduction Wastewater reclamation is an attractive approach to address the increasing global water supply need. Membrane processes such as reverse osmosis (RO) process, have been deployed worldwide for water reclamation and recovery. The main removal mechanism of membrane
⁎
process is size exclusion. The membrane provides a physical barrier to contaminants larger than its pore size, retaining them in the retentate [1]. Due to its high rejection capability, RO process is able to produce high quality water, suitable for both potable and industrial usage. In a municipal secondary effluent recovery, 75–85% of the feed is reclaimed, with the remaining portion discharged as reverse osmosis
Corresponding author. E-mail address: [email protected] (H.Y. Ng).
https://doi.org/10.1016/j.desal.2019.06.013 Received 22 February 2019; Received in revised form 8 June 2019; Accepted 17 June 2019 0011-9164/ © 2019 Elsevier B.V. All rights reserved.
Please cite this article as: Ching Kwek Pooi, Vincent Loka and How Yong Ng, Desalination, https://doi.org/10.1016/j.desal.2019.06.013
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Nomenclature ARD BAC BOD EBCT EEM GAC
LC-OCD RMSE ROC SUVA TN TIC TOC
Average relative deviation Biological activated carbon Biochemical oxygen demand Empty bed contact time Excitation emission matrix Granular activated carbon
concentrate (ROC). The ROC generated contains elevated concentration of organic and inorganic contaminants. The organics present are mainly end products of biological processes, thus, are recalcitrant and difficult to breakdown [2]. Surface disposal and sewer disposal are widely used for disposal of ROC [3]. With proper treatment, high quality water can still be reclaimed from ROC. Several approaches had shown success in ROC treatment; ion exchange, electrocoagulation and nanofiltration processes were able to reduce the inorganic content while chemical coagulation, electrocoagulation, nanofiltration and adsorption process were able to reduce the organic content in ROC [4]. However, these physical/chemical processes are costly for brine treatment. Numerous studies had been done on the biological treatment of ROC. ROC had been used for microalgae cultivation while removing nitrogen, phosphorous and hardness [5,6]. Treatment of ROC using biological activated carbon (BAC) has also been explored to remove micropollutants [7] and organic contaminants [2,8,9]. BAC has been widely used for drinking water treatment [10]. It consists of carbon adsorption and biodegradation whereby organics are adsorbed onto the activated carbon and degraded by the microorganism attached. This allows slowly biodegradable organics to be adsorbed first and then consumed by the microorganisms. Adsorption also increases the biological utilization of the microorganism because substrate is available in both the adsorbed phase and liquid phase [11]. Adsorption also allows for adsorption-induced acclimation of microorganisms, allowing toxic compounds degradation [11,12]. Many studies had been done to model BAC treatment, with first order kinetics being the most commonly employed kinetic model [10,13]. Numerous methods had also been done to improve model simulation. In the past decade, with sufficiently large data, machine learning had been used for the simulation and prediction of wastewater treatment. Studies had shown that machine learning is a reliable method for water quality prediction [14–16]. Hybrid model had also been proposed, where machine learning is used in conjunction with mechanistic model. The incorporation of machine learning had shown to improve the prediction of the mechanistic model significantly [17–19]. In this study, three BAC column reactors were used for the long-term study on treatment of ROC. BAC had been chosen as a cost effective process to treat ROC [4,13]. Batch studies was conducted to determine the kinetics of the biodegradation of organics in the ROC by BAC. Results from the batch studies were then used for the modeling and simulation of treatment of ROC using BAC. Lastly, machine learning will be incorporated into the model to improve the prediction and performance of the model.
Liquid chromatography- organic carbon detection Root mean square error Reverse osmosis concentrate Specific UV254 absorbance Total nitrogen Total inorganic carbon Total organic carbon
prior to usage. 2.2. Lab-scale BAC column reactor Three identical lab-scale BAC column reactors were setup to treat the ROC collected. The BAC column had a diameter of 50 mm and an effective packing height of 400 mm. Packing material used was granular activated carbon (Norit 1240 W, Cabotcorp, USA). It has an effective diameter of 0.6–0.7 mm and density of 470 kg/m3. The ROC entered from the bottom of the column as influent and exit the reactor from the top via an overflow outlet. The empty bed contact time (EBCT) and daily flow rate of each reactor are shown in Table 1. In this study, reactor R3 was operated for 75 d. The EBCT of reactor R3 was 80 min as it was deduced from the kinetic study (Section 3.1) that most of the biodegradable TOC was removed by 80 min. The results obtained from R3 were used for machine learning for training and verification. Prior to this study, all three reactors had been in operation for more than a year and had reached breakthrough with ROC as feed. 2.3. Water quality analysis Water quality of the influent ROC and BAC treated ROC were characterized for TOC, TIC, UV254, pH, conductivity, turbidity and BOD5. TOC, TIC and TN were measured using a total organic carbon analyzer (TOC-VCSH, Shimadzu, Japan) and UV absorbance (UV254) was measured using a spectrophotometer (DR5000, Hach, USA). pH and conductivity were quantified using a portable pH/conductivity meter (Ultrameter II, Myron L, USA). Turbidity was determined using a turbidimeter (2100N, Hach, USA). Five-day biochemical oxygen demand (BOD5) was done in accordance with the Standard methods for the Examination of Water and Wastewater [20]. Organics in influent ROC and BAC treated ROC were characterized using EEM and LC-OCD. EEM was done using the Cary Eclipse fluorescence spectrophotometer (Agilent Technologies, U.S.A.) and LC-OCD was quantified using the LCOCD analyzer (DOC-Labor Dr. Huber, Germany). 2.4. Kinetic determination, modeling and simulation Kinetic study was conducted to evaluate the removal of TOC of the ROC. The TOC results were fitted using the exponential growth function with iteration algorithm of Levenberg Marquardt for the non-linear least square curve fitting. The equation of this exponential growth function is shown in Eq. (1) as below: (1)
[TOC ]t = X + Y *e k * t
2. Materials and methods
where t is time (h), [TOC]t represents the normalized TOC concentration at time t, k is the rate constant for TOC removal (h−1). X represents the non-biodegradable TOC (normalized) while Y represents the
2.1. ROC samples ROC samples were collected weekly from the second-stage of a RO process in a water reclamation plant reclaiming treated secondary effluent in Singapore. The secondary effluent was produced from a membrane bioreactor treating municipal wastewater. The characteristic of the ROC collected is summarized in Table 3 and its corresponding TOC is shown in Fig. 4. Once collected from the plant, the ROC was stored in the cold room at 4 °C. ROC was brought to room temperature
Table 1 EBCT and daily flow rate of R1, R2 & R3.
EBCT (min) Daily flow rate (L)
2
R1
R2
R3
40 28.28
60 18.85
80 14.14
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Fig. 1. Serial hybrid modeling for prediction of non-biodegradable TOC.
Fig. 2. Kinetic study of TOC removal of (a) Run 1, (b) Run 2, (c) Run 3 and (d) 2nd order fit. Table 2 Kinetic parameter derived for TOC removal using BAC.
Table 3 Water quality of influent ROC and BAC treated ROC.
Parameter
Run 1
Run 2
Run 3
Average
Parameter
ROC
R1 effluent
R2 effluent
Influent TOC (mg/L) X (normalized) Y (normalized) k (h−1) R2
21.16 0.63482 0.35389 −2.95705 0.98429
17.73 0.63991 0.36280 −2.27859 0.99694
19.84 0.66119 0.34061 −2.97006 0.99456
19.58 0.6453 0.35243 −2.7352 –
pH Conductivity (μS/cm) Turbidity (NTU) Total Organic Carbon (mg/ L) Total Nitrogen (mg/L) UV254 SUVA BOD (mg/L)
7.35 ± 0.33 2170 ± 260 0.751 ± 0.339 23.1 ± 4.0
7.27 ± 0.27 2209 ± 471 0.448 ± 0.065 17.2 ± 3.0
7.27 ± 0.24 2170 ± 423 0.460 ± 0.058 16.6 ± 2.9
29.3 ± 11.6 0.413 ± 0.090 1.788 ± 0.447 <2.0
26.9 ± 11.1 0.333 ± 0.066 1.936 ± 0.189 <2.0
26.9 ± 11.2 0.312 ± 0.061 1.880 ± 0.180 <2.0
biodegradable TOC (normalized). Modeling and simulation of the treatment of ROC using BAC were done using the results obtained from the kinetic study. Modeling were done using the python language with the Pandas package. Euler method was used to find the numerical approximation to the differential equation. The assumptions used in this simulation are as follows:
series to mimic the performance of a plug flow reactor. Serial hybrid modeling was used in the second phase of the experiment. The schematic diagram of the serial hybrid model is summarized in Fig. 1. Data obtained from reactor R3 was used for machine
1. The BAC reactor was modeled as a plug flow reactor. 2. The model used 4 continuous stirred tank reactors connected in 3
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Fig. 3. (a) EEM graph of ROC, with each regions labelled; (b) EEM graph of R1 effluent; and (c) EEM graph of R2 effluent.
conductivity was used as input for the machine learning. The machine learning will then calculate its corresponding non-biodegradable TOC fraction, which will be used for the first order reaction model. Machine learning was done using Microsoft Azure machine learning studio. Boosted decision tree regression was used with maximum of 30 leaves per tree and a learning rate of 0.44.
Table 4 Percentage distribution of each region in the EEM of ROC, R1 effluent and R2 effluent. Region I II III IV V
Organic nature Aromatic Protein I Aromatic Protein II Fulvic acid-like Soluble microbial-byproduct-like Humic acid-like
ROC (%)
R1 effluent (%)
R2 effluent (%)
6.83 20.70 26.86 20.13
6.48 19.18 30.22 16.62
6.76 17.87 31.11 16.93
25.48
27.50
27.33
3. Results and discussion 3.1. Kinetic study Three batch studies, using different batch of ROC, were done to determine the kinetic of TOC removal of ROC. The BAC used had achieved breakthrough prior to the batch studies, thus, results from the batch studies reflect the BAC's biological treatment of ROC. Results were then fitted using zero-order, first-order and second-order reaction. Results for zero-order reaction (R2 < 0.8) and second-order reaction
learning to predict the non-biodegradable TOC. The machine learning was then used for predicting the non-biodegradable TOC for Day 75–Day 230 using the influent's parameters. Wastewater characteristic such as TOC, TIC, TN, UV254 and 4
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Table 5 LC-OCD results of ROC, R1 effluent and R2 effluent. Sample
ROC R1 effluent R2 effluent
TOC
22.4 ± 5.1 16.7 ± 1.9 15.9 ± 1.5
Hydrophobic organic carbon
21.69% ± 2.53% 22.25% ± 5.82% 18.52% ± 2.76%
Hydrophilic organic carbon
78.30% ± 2.53% 77.75% ± 5.82% 81.48% ± 2.76%
Molecular weight >20,000
~1000
Bio-polymer
Humic substances
1.60% ± 0.69% 1.26% ± 0.65% 1.42% ± 0.77%
41.53% ± 5.28% 44.22% ± 5.00% 50.96% ± 4.83%
(R2 < 0.92) were deemed unsatisfactory while results for first-order reaction yielded the best fit, with R2 > 0.98. In addition, organics removal by BAC had often been reported to follow the first order reaction [10,13]. Hence, first-order reaction was proposed to be used in the model. Fig. 2 shows the results for each run and their corresponding fit. Table 2 summarizes the kinetic results. Though the organics in ROC are recalcitrant and difficult to degrade, the kinetic constant, k, is relatively high, having an average value of 2.73 h−1. More than 90 % of the biodegradable organics in ROC are removed within the first hour. These findings coincide with the study by Ying and Weber [11]. The non-biodegradable TOC portion, X, has an average value of 0.6453, indicating that although the BOD of ROC is <2.0 mg/L, some of the organics in ROC can still be degraded. The high non-biodegradable TOC portion also indicated that pretreatment, such as advanced oxidation process, may be needed in order to increase the biodegradability of ROC. Various AOP such as ozone and UV/H2O2, had shown great potential in the pretreatment of ROC before feeding into the BAC column [8,21]. From the kinetic results, it can also be deduced that most of the biodegradable organics were removed within 80 min of treatment.
300–500
<350
Aromaticity
Building blocks
Low molecular weight neutrals
1.46 ± 0.28 1.31 ± 0.26 1.45 ± 0.30
15.90% ± 3.17% 15.02% ± 3.66% 12.79% ± 3.41%
19.28% ± 2.50% 17.07% ± 2.86% 16.07% ± 2.52%
Low molecular weight acids 0% 0% 0%
wastewater treatment. After BAC treatment, the percentage of aromatic protein II and soluble microbial-by-product-like organics decreased, with an increase in percentage of humic acid-like and fulvic acid-like organics, indicating that BAC mainly consumed aromatic protein II and soluble microbial-by-products. Organic characterization of ROC, R1 effluent and R2 effluent using LC-OCD are tabulated in Table 5. Similar to the EEM results, LC-OCD analysis revealed that the percentage of humic substances increased after BAC treatment. Bio-polymer, building blocks and low molecular weight (neutral) percentage decreased after BAC treatment. Another observation from the LC-OCD result was R1 effluent's hydrophobic organic carbon percentage increased, while R2 effluent's hydrophobic organic carbon percentage decreased. This observation suggested that the microorganisms in BAC consumed the hydrophilic organic carbon first, before consuming the hydrophobic organic carbon. 3.3. Simulation of TOC removal of ROC using BAC Results from the kinetic study were applied into the model. The time series simulation results of R1 and R2 are summarized in Fig. 4(b) and (d), while the simulated results against the measured results of R1 and R2 are shown in Fig. 4(c) and (e), respectively. The statistical results of the simulation (Day 75–Day 230) are shown in Table 6. The statistical result revealed that the model was a good fit, having a low ARD of 7.84% and 7.02% for R1 and R2, respectively. The model also has a low normalized RMSE value (<0.1). Fig. 4(b) and (d) shows that the simulated TOC follows closely to that of measured TOC. The model was able to simulate a surge in the organic concentration in the effluent due to a surge in the organics in the influent on Day 96. This also shows that even though there was a sudden increase in TOC in the influent, the inert TOC fraction of the influent and decay rate constant remained the same. From Fig. 4(c) and (e), it can be deduced that the simulated TOC in both the effluents of R1 and R2 were slightly higher than those of the measured TOC. This could be due to an overestimation of inert TOC, resulting in a lower fraction of biodegradable TOC.
3.2. Treatment of ROC using BAC The performance of BAC over the 230 d of operation is summarized in Table 3. The pH, BOD and conductivity of the effluents of both R1 and R2 did not differ much as those of the ROC. This was within expectation as the BAC reactors were not designed to remove inorganic dissolved contaminants. Approximately 10% TN removal was observed, similar to observation by Ng et al. [22]. TN removal may be attributed by mineralization, reduction of inorganic N and utilization for biomass growth [9]. TOC removal was approximately 25.4% and 27.9% for R1 and R2, respectively. The long-term results coincided with the findings by the batch study, where an increase in EBCT increased the TOC removal. This result is similar to the finding by Ng et al. [22] (25%) and slightly higher than the finding by Lu et al. [9] (15%).The TOC concentration of the effluents of R1 and R2 are summarized in Fig. 4(b) and (d), respectively. The EEM figures of ROC, R1 effluent and R2 effluent are presented in Fig. 3. EEMs can be delineated into 5 regions [23]. The percentage distribution of each region of EEM of ROC, R1 effluent and R2 effluent are summarized in Table 4. From Fig. 3, ROC had an overall higher peak intensity for all 5 regions in the EEM figures as compared to the BAC-treated effluents, indicating that BAC treatment brought about an overall decrease in in peak intensity for all 5 regions in the EEM figures of BAC treated effluents as compared to ROC. The intensity of Region III and Region V of ROC, R1 effluent and R2 effluent were higher than the other regions indicating high amount of humic acid-like and fulvic acidlike organics. Using the method by Chen [23], the percentage distribution of each region can be quantified. As seen from Table 4, quantification of the EEM figures revealed that majority of the organics in ROC were indeed fulvic acid-like and humic acid-like, followed by soluble microbial-by-product-like and aromatic protein II. This was within expectation as ROC were mainly waste product from domestic
3.4. Hybrid modeling of ROC treatment Serial hybrid modeling was used to further improve the model and reduce the ARD and normalized RMSE. Kinetic study revealed that most of the organics were removed within 80 min of treatment. Thus, results from reactor R3 were used for machine learning to relate the inert TOC to the influent characteristic. 80% of the data set was sampled randomly to train the model while the remaining 20% were used for verification. The machine learning model was then used for prediction of inert TOC fraction of ROC from Day 75–Day 230. The data obtained from the machine learning model was then used in the first order reaction model. Results from the hybrid model are shown in Fig. 4(f), (g), (h) and (i) while the statistical results are summarized in Table 6. The prediction performance for TOC concentration improved for the hybrid model as compared to the first order reaction model only. Hybrid model was also 5
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Fig. 4. (a) TOC concentration of influent ROC; (b) Simulation and treatment of ROC of R1; (c) Simulated effluent TOC against measured effluent TOC of R1; (d) Simulation and treatment of ROC of R2; (e) Simulated effluent TOC against measured effluent TOC of R2; (f) Serial hybrid modeling of treatment of ROC of R1; (g) Simulated effluent TOC (hybrid model) against measured effluent TOC of R1; (h) Serial hybrid modeling of treatment of ROC of R2; and (i) Simulated effluent TOC (hybrid model) against measured effluent TOC of R2.
6
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Fig. 4. (continued)
Declaration of Competing Interest
Table 6 Statistical results (Day 75–230) for Reactor 1 and Reactor 2. Modeling First order reaction model only
Serial Hybrid Modeling
Average relative deviation (%) Root mean square error (RMSE) Normalized RMSE Correlation Average relative deviation (%) RMSE Normalized RMSE Correlation
Reactor 1
Reactor 2
7.84
7.02
1.756
1.599
0.099 0.9292 5.93
0.093 0.9106 6.54
1.407 0.079 0.9259
1.449 0.085 0.8973
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capable of simulating the surge in TOC concentration in Day 96. Hybrid model resulted in lower RMSE and ARD, indicating a more accurate simulation. ARD was reduced from 7.84 to 5.93 and 7.02 to 6.54 for reactor R1 and R2, respectively. RMSE was reduced from 0.099 to 0.079 for R1 and 0.093 to 0.085 for R2. However, the correlation between measured effluent TOC and simulated effluent TOC dropped slightly for the hybrid model. 4. Conclusion Hybrid modeling of treatment of ROC using BAC had shown promising results. Average TOC removal efficiencies of 25.4% and 27.9% was achieved for EBCT of 40 min and 60 min, respectively. Approximately 10% TN removal was achieved. EEM and LC-OCD results revealed that BAC mainly consumed the low molecular weight organics and biopolymers, resulting in an increased in fraction of humic substances in the organics. Batch study revealed that treatment of ROC using BAC followed a first order reaction, with R2 value of >0.98. The average decay rate constant was −2.735 h−1 and the average inert TOC fraction was 0.6453. Modeling and simulation of the BAC treatment using the results from the kinetic study yielded good fit, with an ARD of <8% and normalized RMSE of <0.1. Hybrid modeling further reduced the ARD to <7% and normalized RMSE of <0.085. However, hybrid modeling resulted in a slight reduction in the correlation between the simulated results and measured results. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 7
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