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pH monitoring and artificial neural networks
Resources, Conservation and Recycling 52 (2008) 1015–1021
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Resources, Conservation and Recycling journal homepage: www.elsevier.com/locate/resconrec
Dynamic control of disinfection for wastewater reuse applying ORP/pH monitoring and artificial neural networks Ruey-Fang Yu a,∗ , Ho-Wen Chen b , Wen-Po Cheng a , Yu-Chiu Shen a a b
Department of Safety, Health and Environmental Engineering, National United University, Miao-Li 360, Taiwan, ROC Department of Environmental Engineering and Management, Chaoyang University of Technology, Taichung 413, Taiwan, ROC
a r t i c l e
i n f o
Article history: Received 21 February 2007 Received in revised form 26 January 2008 Accepted 26 March 2008 Available online 14 May 2008 Keywords: Artificial neural network (ANN) Chlorination and dechlorination Dynamic dosage control Oxidation-reduction potential (ORP) pH Wastewater reuse
a b s t r a c t This study applies on-line pH and oxidation-reduction potential (ORP) monitoring and artificial neural network models to dynamically control the wastewater chlorination and dechlorination dosage for reuse purposes. A series of wastewater chlorination and dechlorination experiments were conducted in a continuous laboratory-scale reactor. The ORP and pH variations in raw wastewater, and chlorination and dechlorination reactors were monitored on-line. Artificial neural networks (ANNs) were used to build control models using the monitored ORP and pH data. Another series of continuous experiments were conducted to evaluate the proposed control strategy for meeting different requirements for total coliform counts and residual chlorine concentrations for different wastewater reclamation purposes. The dynamical controlled experimental results show that chlorination and dechlorination were effectively controlled, and that appropriate disinfection efficiencies were achieved and remaining chlorine residuals in effluent were controlled simultaneously for different treatment targets. This ANN control method is simple and has potential benefits in reducing chemical costs for wastewater chlorination and dechlorination. © 2008 Elsevier B.V. All rights reserved.
1. Introduction Disinfection is one of the most important processes for wastewater reuse. Chlorination/dechlorination is the most common process used for wastewater disinfection (White, 1999). Total coliform count in wastewater effluent is a primary factor for wastewater reuse, and is affected by chlorination dose; however, concentrations of ammonia and organic-N in influent can react with chlorine to form chloramines and organochloramines (Wolfe et al., 1985; Kim and Hensley, 1997; Devokta et al., 2000). Additionally, total coliform counts cannot be monitored on-line, thereby increasing the difficulty in controlling chlorination dosage. Conversely, chlorinated effluent always contains significant chlorine residuals that are disadvantageous for the receiving water body. Therefore, dechlorination is always required. In addition, some chlorine residuals remaining in chlorinated wastewater are appropriate for some wastewater reuses. Controlling chlorine residuals in effluent is complex and dynamic, and varies depending on the influent ammonia concentration, chlorine and dechlorination dosages (Yu, 2004). Conventional batch-wise chlorination and dechlorination tests were conducted to determine the required dosage (White, 1999;
∗ Corresponding author. Tel.: +886 37 354164; fax: +886 37 381765. E-mail addresses: [email protected], [email protected] (R.-F. Yu). 0921-3449/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.resconrec.2008.03.007
Kim et al., 2006). These tests are time-consuming and cannot be applied for on-line control. Direct monitoring of residual chlorines can be utilized to control chlorination and dechlorination dosages. However, the instruments need for such monitoring are relatively expensive, difficult to maintain and easy to interfere with other substances (Finger et al., 1985; Dieu et al., 1995; White, 1999; Harp, 2000; Kim et al., 2006), and ineffective for presenting germicidal efficiency (Kim and Hensley, 1997). On-line monitoring of oxidation-reduction potential (ORP) and pH has been successfully used to control numerous biological/chemical wastewater treatment processes (Eilbeck, 1984; Zhao et al., 1999; Yu et al., 2000; Ficara and Rozzi, 2001; Feitkenhauer et al., 2001). The ORP set-point control has been employed to regulate chlorination and dechlorination dosages at a municipal wastewater treatment plant in California, USA (Kim and Hensley, 1997). However, these ORP control set points vary depending on ammonia and dissolved oxygen concentrations (Devokta et al., 2000; Yu and Cheng, 2003; Yu, 2004). A pH/ORP titration device was recently developed to determine concentrations of chlorine-consuming materials by identifying specific points on pH/ORP profiles in a titration reactor, an effective feed-forward dose control for wastewater chlorination was proposed (Yu and Cheng, 2003; Yu, 2004). However, significant chlorine residuals were found in the chlorinated effluent. Effective dosage control for dechlorination remains a problem that has not been discussed in literature. In addition, simultaneously controlling doses for wastewater chlori-
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nation and dechlorination is complex and dynamic, especially for different wastewater reuse purposes that have different requirements for water quality, total coliform counts and residual chlorine concentrations. Therefore, traditional mathematical models have difficulty in building such control models. The artificial neural network (ANN) model is very effective in representing the relationships between input and output variables in nonlinear and complex systems. Moreover, ANN models have been widely applied to address problems in process forecasting and control in water and wastewater treatment (Lee and Park, 1999; Zhang and Stanley, 1999; Choi and Park, 2001; Yu et al., 2005). This study utilized back-propagation neural (BPN) network to establish dosage control models for both wastewater chlorination and dechlorination. On-line monitored ORP/pH in different tanks was used as the main parameters of these control models. Different total coliform counts and concentrations of chlorine residuals in effluent for different wastewater reuse purposes were setup for control targets. 2. Materials and method 2.1. Reactors and wastewater samples Fig. 1 shows a schematic diagram of the laboratory-scale chlorination/dechlorination reactor with an on-line pH/ORP monitoring system. A 2.0-liter pre-mixed tank was designed to regulate influents containing different ammonia concentrations. A plug-flow chlorination tank with a volume of 2.6 L and contact time of 30 min was arranged to follow the pre-mixed tank. Finally, a complete mixed dechlorination tank with a volume of 1.3 L and contact time of 15 min was also located after the chlorination tank. Another two tanks were used to store NaOCl and Na2 S2 O3 solutions. One peristaltic pump (Master Flex, USA) was used to control influent flow and two peristaltic pumps were applied to control chlorination and dechlorination doses according to predictions generated by ANN models. The ORP probes with Ag/AgCl electrodes (Mettler/Toledo, Switzerland) and pH probes (Mettler/Toledo, Switzerland) were installed in the pre-mixing, chlorination and dechlorination tanks. All probes/meters and pumps were connected to a PC computer for
on-line monitoring and process control. The software utilized for acquiring data and controlling processes was programmed using LabVIEW 7.1 (National Instruments, USA). Typically, chlorine and sulfur dioxide gas can be used for wastewater chlorination and dechlorination in real wastewater treatment plants. However, for easy storage and maintenance in the laboratory, a NaOCl (Panreac, Spain) solution was employed as the disinfectant and sodium thiosulfate (Na2 S2 O3 ) (Panreac, Spain) was utilized for dechlorination; NaOCl and Na2 S2 O3 were stored in tanks at concentrations of 100 and 200 mg/L, respectively. The real wastewater samples procured from the Miao-Li City sewer system was used in this study, have the following characteristics: DO, 4.5–8.1 mg/L; BOD5 , 2.7–9.9 mg/L; suspended solids, 1.4–26.4 mg/L; ammonia nitrogen, 0.5–2.5 mg/L; nitrate nitrogen, 2.8–7.8 mg/L; and, total coliform counts, 104 –106 CFU/100 mL. The prepared NH4 Cl solution was added to samples to regulate influent ammonia concentrations at 1.0–5.0 mg/L, which is a typical range for ammonia in domestic nitrified biologically treated effluent in Taiwan. 2.2. ANN control model The BPN models used in this study consist of three layers (input, hidden and output layers), which were developed using PCN4 software (Yeh, 2000). Input vector (Xi ), hidden vector (Zj ) and output vector (Yk ) are defined by the following equations. Hidden layer :
I
Zj = f (
wij × Xi )
(1)
i=1
Output layer :
J
Yk = f (
wjk × Zj )
(2)
j=1
where Xi , Zk and Yj are input, hidden and output vectors, respectively; Wij and Wjk are connecting weights from Xi to Zj and Zj to Yk ; f () is the activation/transfer function: f (x) =
1 1 + e−x
Fig. 1. Schematic diagram of the laboratory-scale chlorination/dechlorination reactor and the monitoring and control channels used in this study.
R.-F. Yu et al. / Resources, Conservation and Recycling 52 (2008) 1015–1021
Fig. 2. Typical three layers architecture of the BPN model in this study.
These BPN models use the generalized delta-learning rule as a training algorithm, the gradient descent method to minimize error, sigmoid function as the activation function, and Root Mean Square (RMS) to evaluate the performance of training and test procedures. Fig. 2 presents the BPN architecture.
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nation doses in the laboratory-scale reactor. Experimental results were used to generate control models and strategy. The stored wastewater samples were pumped into the pre-mixing tank at a constant flow rate, and the stored NH4 Cl (Panreac, Spain) solution was added simultaneously to the pre-mixing tank at various flow rates to dynamically regulate the influents containing different ammonia concentrations of 1.0–5.0 mg/L. This pre-mixed wastewater sample was pumped into the chlorination tank and then into the dechlorination tank. The computer-controlled pumps added the required doses of NaOCl and Na2 S2 O3 into the chlorination and dechlorination reactors, respectively. The influent of the chlorination reactor and effluent in the dechlorination reactor were sampled at 30-min intervals for water quality analysis. Total coliform counts and ammonia concentrations in the influent and chlorine residuals and total coliform counts in the effluent were analyzed according to Standard Methods (APHA et al., 2005). The ORP/pH values in the different tanks were monitored simultaneously on-line. Finally, another series of continuous wastewater chlorination/dechlorination experiments was conducted to verify the proposed ANN control models. Five wastewater reuse qualities of total coliform counts and residual chlorine concentrations in Taiwan were set as control targets. 3. Results and discussion
2.3. Experiments
3.1. Continuous chlorination/dechlorination experiments A series of 411 runs of continuous wastewater chlorination/dechlorination experiments was conducted under different influent ammonia concentrations, and chlorination and dechlori-
A series of 411 runs of continuous chlorination/dechlorination experiments was conducted under different influent ammonia
Table 1 Experimental conditions and results of the series of 411 runs of continuous chlorination/dechlorination Run number
Influent ammonia concentration (mg/L)
Influent total coliform counts (CFU/100 mL)
Chlorine dose (mg/L)
Na2 S2 O3 dose (mg/L)
Effluent total coliform counts (CFU/100 mL)
Effluent residual chlorine (mg/L)
411
1.12–4.80 (2.68)
1.7 × 104 –1.1 × 106 (8.2 × 104 )
1.7–10.8 (5.3)
2.1–16.3 (8.7)
ND-9500 (798)
ND-2.07 (0.82)
Monitored ORP (mV) and pH in the pre-mixing tanks
Monitored ORP (mV) and pH in the chlorination tanks
Monitored ORP (mV) and pH in the dechlorination tanks
ORP (mV)
pH
ORP (mV)
pH
ORP (mV)
pH
39.9–528.9 (298.8)
6.99–9.00 (8.40)
259.0–562.0 (400.3)
6.04–8.92 (8.45)
132.2–409.4 (262.2)
8.00–8.83 (8.45)
() means the average; ND means undetectable. Table 2 Details of the BPN control models used in this study BPN model
Input parameter
Output parameter
R2
Network architecture
Optimization algorithm Train cycles
BPN1
BPN2
BPN3
BPN4
BPN5
pH/ORP values in the pre-mixing tank and chlorination reactor, control target of effluent total coliform counts pH/ORP values in the pre-mixing tank and chlorination reactor, chlorine dose pH/ORP values in the pre-mixing tank, chlorination reactor and dechlorination reactor, chlorine dose, control target of effluent chlorine residuals pH/ORP values in the pre-mixing tank, chlorination reactor and dechlorination reactor, chlorine dose, dechlorination dose pH/ORP values in the pre-mixing tank and chlorination reactor, chlorine dose
Random seed
Learn rate
Chlorine dose
0.80
5-9-1
5,300
0.3
2.0
Effluent total coliform counts
0.92
5-20-1
4,800
0.1
2.0
Dechlorination dose
0.92
8-14-1
1,500
0.2
3.0
Effluent chlorine residuals
0.86
8-20-1
2,400
0.35
6.0
Chlorine residuals in chlorination reactor
0.95
5-10-1
9,300
0.2
4.0
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concentrations and chlorination/dechlorination doses. Table 1 presents a summary of experimental conditions and results for these 411 experiments. The influent ammonia concentrations were 1.12–4.80 mg/L and total coliform counts were 104 –106 CFU/ 100 mL. Both ammonia and organic-N in influent reacted with chlorine and formed chloramines and organochloramines that affect disinfection efficiency during chlorination (Wolfe et al., 1985; Devokta et al., 2000). The most reasonable pathways by which chlorine reacts with ammonia are as follows (White, 1999): NH4 + + HOCl → NH2 Cl + H2 O + H+
(3)
NH2 Cl + 0.5HOCl → 0.5N2 + 0.5H2 O + 1.5H+ + 1.5Cl−
(4)
The reaction of organic-N, particularly organic amino compounds, and free chlorine is as follows (Donnermair and Blatchley, 2003): RNH2 + HOCl ↔ RNHCl + H2 O
(5)
White (1999) proposed a Cl/N ratio (a weight ratio of chlorine to ammonia nitrogen) of 6 for chlorine dose control to achieve the best disinfection efficiency. However, this Cl/N ratio of 6 causes overdosing in high influent ammonia conditions (Yu, 2004). According to Yu’s study, lower Cl/N weight ratios were sufficient for high influent ammonia conditions, thereby reducing chlorination and
Fig. 3. Predict results of the ANN control models of BPN1 to BPN5 .
R.-F. Yu et al. / Resources, Conservation and Recycling 52 (2008) 1015–1021
dechlorination doses. Therefore, chlorine doses in this study were controlled at Cl/N ratios of 2.5–5.5. Monochloramine (NH2 Cl) is the major disinfectant in wastewater chlorination as the Cl/N ratio is less than 5. The reaction of monochloramine and thiosulfate is as follows (White, 1999): NH2 Cl + S2 O3 2− + 5H2 O → 2SO4 2− + Cl− + NH3 + 9H+
(6)
In addition, thiosulfate also reacts with dissolved oxygen in solutions as follows (Bedner et al., 2004; Schreiber and Pavlostathis, 1998): 2O2 + S2 O3 2− + H2 O → 2SO4 2− + 2H+
(7)
Therefore, an increased dechlorination dose may be required to remove chlorine residuals. Since the average DO concentration is 6.54 mg/L, Na2 S2 O3 doses were also controlled at 1.0–1.8 times the chlorine doses. Under these operational conditions, effluent total coliform counts were undetectable (ND)-9500 CFU/100 mL and residual chlorines concentrations were ND-2.07 mg/L. Table 1 also presents a summary of monitored ORP and pH values in the pre-mixing tank, chlorination reactor and dechlorination reactor. 3.2. ANN control models In this study, BPN models were used to represent nonlinear relationships between chlorination and dechlorination doses, monitored ORP and pH values in different tanks/reactors, and effluent total coliform counts and chlorine residuals. Five BPN models, BPN1–5 , were used as control models to dynamically regulate chlorination and dechlorination doses. BPN1 and BPN2 models were utilized for chlorination dosage control. In the BPN1 model, pH/ORP
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values in the pre-mixing tank and chlorination reactor, and control targets for effluent total coliform counts were used as input parameters to estimate the required chlorine dose for feed-forward control. In the BPN2 model, the pH/ORP values in the pre-mixing tank and chlorination reactor, and chlorine doses were used as input parameters to predict effluent total coliform counts. The BPN2 was activated at 5 min after the BPN1 model, which was used as a feedback control rule. On the other hand, BPN3–5 models were used to control dechlorination doses; BPN3 and BPN5 provide feed-forward control rules and BPN4 provides a feedback control rule. Table 2 lists detailed information for these five BPN models. Experimental results of the 411 continuous chlorination/dechlorination experiments were used to generate control rules for the BPN models. Experimental data sets of 270 runs were randomly selected as training samples, and data sets for the other 141 runs were used as test samples. Fig. 3 shows prediction results for BPN1–5 . All BPN models achieved precise prediction results, indicating that these five BPN models have high potential for controlling chlorination and dechlorination doses. 3.3. ANN dynamic dose control strategy According to prediction results by the BPN models, this study proposes a novel, dynamic dose control strategy for continuous wastewater chlorination/dechlorination (Fig. 4). An initial chlorine dose of 5.0 mg/L was added to the chlorination reactor. After 5 min of complete mixing, the monitored ORP/pH values in the premixing and chlorination tanks and the control target for effluent total coliform counts were used to predict a new chlorine dose using BPN1 . The initial dose was then changed to the new dose, and BPN2 was then activated to predict effluent total coliform counts for
Fig. 4. Proposed dynamic ANN control strategy for the continuous wastewater chlorination and dechlorination.
(0.01) 0.90–1.69 (1.27) 1.2 (11.7) 10.9–14.2 (12.2) (152) ND-12 (2) () means the average. ND means undetectable. a
b
(5.5) 6.4–9.0 (7.4) (2.7 × 105 ) 7.6 × 104 –3.0 × 105 (1.6 × 105 ) Decorative fountain
(2.96) 2.24–4.04 (3.11)
20
(1.26) ND-0.34 ND (12.7) 9.9–14.7 27–500 (9.8) 3.2–7.0 (3.4 × 104 ) 1.6 × 105 –3.9 × 105 Natural and Man-made wetlands
Sprinkle water and air conditioner cooling water
Toilet flushing
(2.90) 2.11–3.76
NDb
1000
500
1.1
0.5
1.0
9.7–13.7 (12.8) 5.3–13.0 (9.9) 10.8–15.0 ND-48 (9) ND-950 (187) ND 4.6–8.4 (7.4) 2.4–5.8 (4.3) 7.4–10.0 7.6 × 104 –3.0 × 105 (1.6 × 105 ) 1.5 × 104 –5.5 × 104 (3.6 × 104 ) 1.5 × 104 –5.5 × 104 Recreational purpose
2.20–3.50 (2.78)a 2.06–3.93 (2.89) 1.81–3.94
Target for total coliform counts (CFU/100 mL) Chlorine dose (mg/L) Influent total coliform counts (CFU/100 mL) Influent ammonia concentration (mg/L)
Table 3 Control results of continuous chlorination/dechlorination using the BPN models for different wastewater reuse purposes
Another series of continuous wastewater chlorination and dechlorination experiments were conducted to evaluate the proposed dynamic dose control strategy. Same real wastewater samples as the previous experiments were used in this continuous experiment. Five runs of continuous chlorination and dechlorination experiments were conducted and controlled using the proposed ANN control strategy. Each run was operated for >10 h. Control targets were set to achieve different water qualities for the five wastewater reuse purposes in Taiwan (Table 3). Table 3 shows the detailed influent water qualities and operational conditions of these five runs. Influent ammonia concentrations were around 1.81–4.04 mg/L, and total coliform counts were 104 –105 CFU/100 mL. Table 3 also shows ANN control results for continuous chlorination/dechlorination. For wastewater reuse for recreational purposes, total coliform counts of 50 CFU/100 mL and residual total chlorine >1.0 mg/L are recommended. Under this ANN control, chlorine doses were 4.60–8.43 mg/L and Na2 S2 O3 doses were 9.74–13.65 mg/L, resulting in effluent total coliform counts of ND-48 CFU/100 mL (average, 9 CFU/100 mL), and residual total chlorine concentrations of 0.64–1.42 mg/L (average, 1.15 mg/L). Both total coliform counts and residual total chlorine concentrations were effectively controlled. For reused wastewater for flushing toilets, total coliform counts must be <1000 CFU/100 mL and the remaining residual chlorine around 0.5 mg/L are recommended. For these control targets, chlorine doses were controlled at 2.40–5.81 mg/L, and the Na2 S2 O3 dose was 5.30–12.99 mg/L. The controlled result for total coliform counts was ND-950 CFU/100 mL (average, 186 CFU/100 mL), and that for residual total chlorine was 0.18–0.78 mg/L (average, 0.52 mg/L); both are within acceptable ranges. These required chlorine and Na2 S2 O3 doses were much lower than doses for recreational purposes, resulting in a savings of 40% for chlorine and 23% for Na2 S2 O3 . For wastewater reuse as air conditioner cooling water, an undetectable total coliform count and residual total chlorine concentration of >1.1 mg/L are required. Under ANN control, chlorine doses were 7.37–10.04 mg/L, and Na2 S2 O3 doses were 10.79–15.00 mg/L. Control results show that total coliform counts in all effluents were undetectable, and the effluent residual total chlorine concentration was 0.88–1.66 mg/L (average, 1.26 mg/L). These control results are acceptable for the reuse of air conditioner cooling water. Table 3 shows control results for other wastewater reclamation purposes. Generally, acceptable effluent water qualities were achieved. These experimental findings indicate that continuous chlorination and dechlorination were effectively controlled using the proposed ANN dynamic dose control strategy. Additionally, sig-
Control effluent total coliform counts (CFU/100 mL)
3.4. Dynamic dose control of wastewater chlorination/dechlorination for reuse
50
Na2 S2 O3 dose (mg/L)
Target for residual chlorine (mg/L)
Control effluent residual chlorine (mg/L)
roughly the next 5 min. When the predicted effluent total coliform count was located within 10% of the control target, this chlorine dose was confirmed; otherwise, the predicted chlorine dose was altered according to the difference between predicted and targeted total coliform counts. Control of the Na2 S2 O3 dose utilized a similar process as that for controlling the chlorine dose. The monitored ORP/pH values in the pre-mixing, chlorination and dechlorination tanks, and chlorine dose were used in BPN3 to predict the required Na2 S2 O3 dose. Then, the residual chlorine concentration predicted by BPN4 was used to compare the control target of residual chlorine concentration, and was used to tune the required Na2 S2 O3 dose. Then BPN5 was used to predict the forming residual chlorine concentrations in the chlorination tank, which can be employed as a reference for the required Na2 S2 O3 dose, thereby increasing control stability.
0.64–1.42 (1.15) 0.18–0.78 (0.52) 0.88–1.66
R.-F. Yu et al. / Resources, Conservation and Recycling 52 (2008) 1015–1021
Wastewater reclamation purpose
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nificantly different chlorine and Na2 S2 O3 doses were required to meet regulations for wastewater reuse proposes, indicating the potential to reduce chemical costs. 4. Conclusions This study applies ORP/pH monitoring and ANN models to dynamically control chlorination and dechlorination doses for wastewater reuse. Five BPN control models were developed to build dynamic dose control strategy for chlorination and dechlorination. A series of 411 continuous chlorination and dechlorination experiments were conducted under different influent ammonia concentrations and operational conditions. Experimental data were used to generate control rules for these ANN control models. Another series of continuous chlorination and dechlorination experiments were conducted to evaluate the proposed dynamic dose control strategy. Real wastewater samples were treated to meet different water quality requirements for wastewater reuse based on total coliform counts and residual chlorine concentrations. Experimental results show that chlorination and dechlorination doses were effectively controlled using the ANN control models. Different control targets for different wastewater reuse purpose were achieved. The potential benefits in reducing chemicals costs were also obtained. Acknowledgement The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC-93-2622-E-239-004-CC3. References APHA, AWWA, WEF. Standard methods for the examination of water and wastewater, 21st ed. Washington, DC: American Public Health Association; 2005. Bedner M, MacCrehan WA, Helz GR. Making chlorine greener: investigation of alternatives to sulfite for dechlorination. Water Res 2004;38:2505–14. Choi DJ, Park HY. A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process. Water Res 2001;35:3959–67.
1021
Devokta LM, Williams DS, Matta JH, Albertson OE. Variation of oxidation-reduction potential along breakpoint curves in low-ammonia effluent. Water Environ Res 2000;72:610–7. Donnermair MM, Blatchley ER. Disinfection efficacy of organic chloramines. Water Res 2003;37:1557–70. Dieu B, Garrett MTJ, Ahmad Z, Young S. Applications of automatic control system for chlorination and dechlorination processes in wastewater treatment plants. ISA Trans 1995;34:21–8. Eilbeck WJ. Redox control in breakpoint chlorination of ammonia and metal ammine complexes. Water Res 1984;18:21–4. Feitkenhauer H, Sachs JV, Meyer U. On-line titration of volatile fatty acids for the process control of anaerobic digestion plants. Water Res 2001;36:212–8. Ficara E, Rozzi A. pH-stat titration to assess nitrification inhibition. J Environ Eng, ASCE 2001;127:698–704. Finger RE, Harrington D, Paxton LA. Development of an zero chlorine residual measurement and control system. J WPCF 1985;57:1068–73. Harp DL. Specific determination of inorganic monochloramine in chlorinated wastewaters. Water Environ Res 2000;72:706–16. Kim YH, Hensley R. Effective control of chlorination and dechlorination at wastewater treatment plants using redox potential. Water Environ Res 1997;69:1008– 14. Kim H, Kwon S, Han S, Yu M, Gong S, Colosimo MF. New process control strategy for wastewater chlorination and dechlorination using ORP/pH. Water Sci Technol 2006;53:431–8. Lee DS, Park JM. Neural network modeling for on-line estimation of nutrient dynamics in a sequentially operated batch reactor. J Biotechnol 1999;75:299–339. Schreiber DC, Pavlostathis SG. Biological oxidation of thiosulfate in mixed heterotrophic/autotrophic cultures. Water Res 1998;32:1363–72. White GC. Handbook of chlorination and alternative disinfectants, 4th ed. NY: John Wiley & Sons Inc.; 1999, 212–85, 537–74. Wolfe RL, Ward NR, Olson BH. Interference in the bactericidal properties of inorganic chloramines by organic nitrogen compounds. Environ Sci Technol 1985;19:1192–5. Yeh YC. Artificial neural network models and applications, 2nd ed. Taipei: Rue-Lin Publisher; 2000, 23–145, 214–55 (in Chinese). Yu RF, Liaw SL, Cheng WY, Chang CN. Performance enhancement of SBR applying real-time control. J Environ Eng, ASCE 2000;126:943–8. Yu RF, Cheng WP. Determination of chlorine demand in water and wastewater chlorination by oxidation-reduction potential. Water Sci Technol Water Sup 2003;3:313–20. Yu RF. Feed-forward dose control of wastewater chlorination using on-line pH and ORP titration. Chemosphere 2004;56:973–80. Yu RF, Cheng WP, Chu ML. On-line monitoring of wastewater true color using digital image analysis and ANN. J Environ Eng, ASCE 2005;131:71–9. Zhang Q, Stanley SJ. Real-time water treatment process control with artificial neural network. J Environ Eng ASCE 1999;125:153–60. Zhao HW, Mavinic DS, Oldham WK, Koch FA. Controlling factors for simultaneous nitrification and denitrification in a two-stage intermittent aeration process treating domestic sewage. Water Res 1999;33:961–70.
Contents lists available at ScienceDirect
Resources, Conservation and Recycling journal homepage: www.elsevier.com/locate/resconrec
Dynamic control of disinfection for wastewater reuse applying ORP/pH monitoring and artificial neural networks Ruey-Fang Yu a,∗ , Ho-Wen Chen b , Wen-Po Cheng a , Yu-Chiu Shen a a b
Department of Safety, Health and Environmental Engineering, National United University, Miao-Li 360, Taiwan, ROC Department of Environmental Engineering and Management, Chaoyang University of Technology, Taichung 413, Taiwan, ROC
a r t i c l e
i n f o
Article history: Received 21 February 2007 Received in revised form 26 January 2008 Accepted 26 March 2008 Available online 14 May 2008 Keywords: Artificial neural network (ANN) Chlorination and dechlorination Dynamic dosage control Oxidation-reduction potential (ORP) pH Wastewater reuse
a b s t r a c t This study applies on-line pH and oxidation-reduction potential (ORP) monitoring and artificial neural network models to dynamically control the wastewater chlorination and dechlorination dosage for reuse purposes. A series of wastewater chlorination and dechlorination experiments were conducted in a continuous laboratory-scale reactor. The ORP and pH variations in raw wastewater, and chlorination and dechlorination reactors were monitored on-line. Artificial neural networks (ANNs) were used to build control models using the monitored ORP and pH data. Another series of continuous experiments were conducted to evaluate the proposed control strategy for meeting different requirements for total coliform counts and residual chlorine concentrations for different wastewater reclamation purposes. The dynamical controlled experimental results show that chlorination and dechlorination were effectively controlled, and that appropriate disinfection efficiencies were achieved and remaining chlorine residuals in effluent were controlled simultaneously for different treatment targets. This ANN control method is simple and has potential benefits in reducing chemical costs for wastewater chlorination and dechlorination. © 2008 Elsevier B.V. All rights reserved.
1. Introduction Disinfection is one of the most important processes for wastewater reuse. Chlorination/dechlorination is the most common process used for wastewater disinfection (White, 1999). Total coliform count in wastewater effluent is a primary factor for wastewater reuse, and is affected by chlorination dose; however, concentrations of ammonia and organic-N in influent can react with chlorine to form chloramines and organochloramines (Wolfe et al., 1985; Kim and Hensley, 1997; Devokta et al., 2000). Additionally, total coliform counts cannot be monitored on-line, thereby increasing the difficulty in controlling chlorination dosage. Conversely, chlorinated effluent always contains significant chlorine residuals that are disadvantageous for the receiving water body. Therefore, dechlorination is always required. In addition, some chlorine residuals remaining in chlorinated wastewater are appropriate for some wastewater reuses. Controlling chlorine residuals in effluent is complex and dynamic, and varies depending on the influent ammonia concentration, chlorine and dechlorination dosages (Yu, 2004). Conventional batch-wise chlorination and dechlorination tests were conducted to determine the required dosage (White, 1999;
∗ Corresponding author. Tel.: +886 37 354164; fax: +886 37 381765. E-mail addresses: [email protected], [email protected] (R.-F. Yu). 0921-3449/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.resconrec.2008.03.007
Kim et al., 2006). These tests are time-consuming and cannot be applied for on-line control. Direct monitoring of residual chlorines can be utilized to control chlorination and dechlorination dosages. However, the instruments need for such monitoring are relatively expensive, difficult to maintain and easy to interfere with other substances (Finger et al., 1985; Dieu et al., 1995; White, 1999; Harp, 2000; Kim et al., 2006), and ineffective for presenting germicidal efficiency (Kim and Hensley, 1997). On-line monitoring of oxidation-reduction potential (ORP) and pH has been successfully used to control numerous biological/chemical wastewater treatment processes (Eilbeck, 1984; Zhao et al., 1999; Yu et al., 2000; Ficara and Rozzi, 2001; Feitkenhauer et al., 2001). The ORP set-point control has been employed to regulate chlorination and dechlorination dosages at a municipal wastewater treatment plant in California, USA (Kim and Hensley, 1997). However, these ORP control set points vary depending on ammonia and dissolved oxygen concentrations (Devokta et al., 2000; Yu and Cheng, 2003; Yu, 2004). A pH/ORP titration device was recently developed to determine concentrations of chlorine-consuming materials by identifying specific points on pH/ORP profiles in a titration reactor, an effective feed-forward dose control for wastewater chlorination was proposed (Yu and Cheng, 2003; Yu, 2004). However, significant chlorine residuals were found in the chlorinated effluent. Effective dosage control for dechlorination remains a problem that has not been discussed in literature. In addition, simultaneously controlling doses for wastewater chlori-
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nation and dechlorination is complex and dynamic, especially for different wastewater reuse purposes that have different requirements for water quality, total coliform counts and residual chlorine concentrations. Therefore, traditional mathematical models have difficulty in building such control models. The artificial neural network (ANN) model is very effective in representing the relationships between input and output variables in nonlinear and complex systems. Moreover, ANN models have been widely applied to address problems in process forecasting and control in water and wastewater treatment (Lee and Park, 1999; Zhang and Stanley, 1999; Choi and Park, 2001; Yu et al., 2005). This study utilized back-propagation neural (BPN) network to establish dosage control models for both wastewater chlorination and dechlorination. On-line monitored ORP/pH in different tanks was used as the main parameters of these control models. Different total coliform counts and concentrations of chlorine residuals in effluent for different wastewater reuse purposes were setup for control targets. 2. Materials and method 2.1. Reactors and wastewater samples Fig. 1 shows a schematic diagram of the laboratory-scale chlorination/dechlorination reactor with an on-line pH/ORP monitoring system. A 2.0-liter pre-mixed tank was designed to regulate influents containing different ammonia concentrations. A plug-flow chlorination tank with a volume of 2.6 L and contact time of 30 min was arranged to follow the pre-mixed tank. Finally, a complete mixed dechlorination tank with a volume of 1.3 L and contact time of 15 min was also located after the chlorination tank. Another two tanks were used to store NaOCl and Na2 S2 O3 solutions. One peristaltic pump (Master Flex, USA) was used to control influent flow and two peristaltic pumps were applied to control chlorination and dechlorination doses according to predictions generated by ANN models. The ORP probes with Ag/AgCl electrodes (Mettler/Toledo, Switzerland) and pH probes (Mettler/Toledo, Switzerland) were installed in the pre-mixing, chlorination and dechlorination tanks. All probes/meters and pumps were connected to a PC computer for
on-line monitoring and process control. The software utilized for acquiring data and controlling processes was programmed using LabVIEW 7.1 (National Instruments, USA). Typically, chlorine and sulfur dioxide gas can be used for wastewater chlorination and dechlorination in real wastewater treatment plants. However, for easy storage and maintenance in the laboratory, a NaOCl (Panreac, Spain) solution was employed as the disinfectant and sodium thiosulfate (Na2 S2 O3 ) (Panreac, Spain) was utilized for dechlorination; NaOCl and Na2 S2 O3 were stored in tanks at concentrations of 100 and 200 mg/L, respectively. The real wastewater samples procured from the Miao-Li City sewer system was used in this study, have the following characteristics: DO, 4.5–8.1 mg/L; BOD5 , 2.7–9.9 mg/L; suspended solids, 1.4–26.4 mg/L; ammonia nitrogen, 0.5–2.5 mg/L; nitrate nitrogen, 2.8–7.8 mg/L; and, total coliform counts, 104 –106 CFU/100 mL. The prepared NH4 Cl solution was added to samples to regulate influent ammonia concentrations at 1.0–5.0 mg/L, which is a typical range for ammonia in domestic nitrified biologically treated effluent in Taiwan. 2.2. ANN control model The BPN models used in this study consist of three layers (input, hidden and output layers), which were developed using PCN4 software (Yeh, 2000). Input vector (Xi ), hidden vector (Zj ) and output vector (Yk ) are defined by the following equations. Hidden layer :
I
Zj = f (
wij × Xi )
(1)
i=1
Output layer :
J
Yk = f (
wjk × Zj )
(2)
j=1
where Xi , Zk and Yj are input, hidden and output vectors, respectively; Wij and Wjk are connecting weights from Xi to Zj and Zj to Yk ; f () is the activation/transfer function: f (x) =
1 1 + e−x
Fig. 1. Schematic diagram of the laboratory-scale chlorination/dechlorination reactor and the monitoring and control channels used in this study.
R.-F. Yu et al. / Resources, Conservation and Recycling 52 (2008) 1015–1021
Fig. 2. Typical three layers architecture of the BPN model in this study.
These BPN models use the generalized delta-learning rule as a training algorithm, the gradient descent method to minimize error, sigmoid function as the activation function, and Root Mean Square (RMS) to evaluate the performance of training and test procedures. Fig. 2 presents the BPN architecture.
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nation doses in the laboratory-scale reactor. Experimental results were used to generate control models and strategy. The stored wastewater samples were pumped into the pre-mixing tank at a constant flow rate, and the stored NH4 Cl (Panreac, Spain) solution was added simultaneously to the pre-mixing tank at various flow rates to dynamically regulate the influents containing different ammonia concentrations of 1.0–5.0 mg/L. This pre-mixed wastewater sample was pumped into the chlorination tank and then into the dechlorination tank. The computer-controlled pumps added the required doses of NaOCl and Na2 S2 O3 into the chlorination and dechlorination reactors, respectively. The influent of the chlorination reactor and effluent in the dechlorination reactor were sampled at 30-min intervals for water quality analysis. Total coliform counts and ammonia concentrations in the influent and chlorine residuals and total coliform counts in the effluent were analyzed according to Standard Methods (APHA et al., 2005). The ORP/pH values in the different tanks were monitored simultaneously on-line. Finally, another series of continuous wastewater chlorination/dechlorination experiments was conducted to verify the proposed ANN control models. Five wastewater reuse qualities of total coliform counts and residual chlorine concentrations in Taiwan were set as control targets. 3. Results and discussion
2.3. Experiments
3.1. Continuous chlorination/dechlorination experiments A series of 411 runs of continuous wastewater chlorination/dechlorination experiments was conducted under different influent ammonia concentrations, and chlorination and dechlori-
A series of 411 runs of continuous chlorination/dechlorination experiments was conducted under different influent ammonia
Table 1 Experimental conditions and results of the series of 411 runs of continuous chlorination/dechlorination Run number
Influent ammonia concentration (mg/L)
Influent total coliform counts (CFU/100 mL)
Chlorine dose (mg/L)
Na2 S2 O3 dose (mg/L)
Effluent total coliform counts (CFU/100 mL)
Effluent residual chlorine (mg/L)
411
1.12–4.80 (2.68)
1.7 × 104 –1.1 × 106 (8.2 × 104 )
1.7–10.8 (5.3)
2.1–16.3 (8.7)
ND-9500 (798)
ND-2.07 (0.82)
Monitored ORP (mV) and pH in the pre-mixing tanks
Monitored ORP (mV) and pH in the chlorination tanks
Monitored ORP (mV) and pH in the dechlorination tanks
ORP (mV)
pH
ORP (mV)
pH
ORP (mV)
pH
39.9–528.9 (298.8)
6.99–9.00 (8.40)
259.0–562.0 (400.3)
6.04–8.92 (8.45)
132.2–409.4 (262.2)
8.00–8.83 (8.45)
() means the average; ND means undetectable. Table 2 Details of the BPN control models used in this study BPN model
Input parameter
Output parameter
R2
Network architecture
Optimization algorithm Train cycles
BPN1
BPN2
BPN3
BPN4
BPN5
pH/ORP values in the pre-mixing tank and chlorination reactor, control target of effluent total coliform counts pH/ORP values in the pre-mixing tank and chlorination reactor, chlorine dose pH/ORP values in the pre-mixing tank, chlorination reactor and dechlorination reactor, chlorine dose, control target of effluent chlorine residuals pH/ORP values in the pre-mixing tank, chlorination reactor and dechlorination reactor, chlorine dose, dechlorination dose pH/ORP values in the pre-mixing tank and chlorination reactor, chlorine dose
Random seed
Learn rate
Chlorine dose
0.80
5-9-1
5,300
0.3
2.0
Effluent total coliform counts
0.92
5-20-1
4,800
0.1
2.0
Dechlorination dose
0.92
8-14-1
1,500
0.2
3.0
Effluent chlorine residuals
0.86
8-20-1
2,400
0.35
6.0
Chlorine residuals in chlorination reactor
0.95
5-10-1
9,300
0.2
4.0
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concentrations and chlorination/dechlorination doses. Table 1 presents a summary of experimental conditions and results for these 411 experiments. The influent ammonia concentrations were 1.12–4.80 mg/L and total coliform counts were 104 –106 CFU/ 100 mL. Both ammonia and organic-N in influent reacted with chlorine and formed chloramines and organochloramines that affect disinfection efficiency during chlorination (Wolfe et al., 1985; Devokta et al., 2000). The most reasonable pathways by which chlorine reacts with ammonia are as follows (White, 1999): NH4 + + HOCl → NH2 Cl + H2 O + H+
(3)
NH2 Cl + 0.5HOCl → 0.5N2 + 0.5H2 O + 1.5H+ + 1.5Cl−
(4)
The reaction of organic-N, particularly organic amino compounds, and free chlorine is as follows (Donnermair and Blatchley, 2003): RNH2 + HOCl ↔ RNHCl + H2 O
(5)
White (1999) proposed a Cl/N ratio (a weight ratio of chlorine to ammonia nitrogen) of 6 for chlorine dose control to achieve the best disinfection efficiency. However, this Cl/N ratio of 6 causes overdosing in high influent ammonia conditions (Yu, 2004). According to Yu’s study, lower Cl/N weight ratios were sufficient for high influent ammonia conditions, thereby reducing chlorination and
Fig. 3. Predict results of the ANN control models of BPN1 to BPN5 .
R.-F. Yu et al. / Resources, Conservation and Recycling 52 (2008) 1015–1021
dechlorination doses. Therefore, chlorine doses in this study were controlled at Cl/N ratios of 2.5–5.5. Monochloramine (NH2 Cl) is the major disinfectant in wastewater chlorination as the Cl/N ratio is less than 5. The reaction of monochloramine and thiosulfate is as follows (White, 1999): NH2 Cl + S2 O3 2− + 5H2 O → 2SO4 2− + Cl− + NH3 + 9H+
(6)
In addition, thiosulfate also reacts with dissolved oxygen in solutions as follows (Bedner et al., 2004; Schreiber and Pavlostathis, 1998): 2O2 + S2 O3 2− + H2 O → 2SO4 2− + 2H+
(7)
Therefore, an increased dechlorination dose may be required to remove chlorine residuals. Since the average DO concentration is 6.54 mg/L, Na2 S2 O3 doses were also controlled at 1.0–1.8 times the chlorine doses. Under these operational conditions, effluent total coliform counts were undetectable (ND)-9500 CFU/100 mL and residual chlorines concentrations were ND-2.07 mg/L. Table 1 also presents a summary of monitored ORP and pH values in the pre-mixing tank, chlorination reactor and dechlorination reactor. 3.2. ANN control models In this study, BPN models were used to represent nonlinear relationships between chlorination and dechlorination doses, monitored ORP and pH values in different tanks/reactors, and effluent total coliform counts and chlorine residuals. Five BPN models, BPN1–5 , were used as control models to dynamically regulate chlorination and dechlorination doses. BPN1 and BPN2 models were utilized for chlorination dosage control. In the BPN1 model, pH/ORP
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values in the pre-mixing tank and chlorination reactor, and control targets for effluent total coliform counts were used as input parameters to estimate the required chlorine dose for feed-forward control. In the BPN2 model, the pH/ORP values in the pre-mixing tank and chlorination reactor, and chlorine doses were used as input parameters to predict effluent total coliform counts. The BPN2 was activated at 5 min after the BPN1 model, which was used as a feedback control rule. On the other hand, BPN3–5 models were used to control dechlorination doses; BPN3 and BPN5 provide feed-forward control rules and BPN4 provides a feedback control rule. Table 2 lists detailed information for these five BPN models. Experimental results of the 411 continuous chlorination/dechlorination experiments were used to generate control rules for the BPN models. Experimental data sets of 270 runs were randomly selected as training samples, and data sets for the other 141 runs were used as test samples. Fig. 3 shows prediction results for BPN1–5 . All BPN models achieved precise prediction results, indicating that these five BPN models have high potential for controlling chlorination and dechlorination doses. 3.3. ANN dynamic dose control strategy According to prediction results by the BPN models, this study proposes a novel, dynamic dose control strategy for continuous wastewater chlorination/dechlorination (Fig. 4). An initial chlorine dose of 5.0 mg/L was added to the chlorination reactor. After 5 min of complete mixing, the monitored ORP/pH values in the premixing and chlorination tanks and the control target for effluent total coliform counts were used to predict a new chlorine dose using BPN1 . The initial dose was then changed to the new dose, and BPN2 was then activated to predict effluent total coliform counts for
Fig. 4. Proposed dynamic ANN control strategy for the continuous wastewater chlorination and dechlorination.
(0.01) 0.90–1.69 (1.27) 1.2 (11.7) 10.9–14.2 (12.2) (152) ND-12 (2) () means the average. ND means undetectable. a
b
(5.5) 6.4–9.0 (7.4) (2.7 × 105 ) 7.6 × 104 –3.0 × 105 (1.6 × 105 ) Decorative fountain
(2.96) 2.24–4.04 (3.11)
20
(1.26) ND-0.34 ND (12.7) 9.9–14.7 27–500 (9.8) 3.2–7.0 (3.4 × 104 ) 1.6 × 105 –3.9 × 105 Natural and Man-made wetlands
Sprinkle water and air conditioner cooling water
Toilet flushing
(2.90) 2.11–3.76
NDb
1000
500
1.1
0.5
1.0
9.7–13.7 (12.8) 5.3–13.0 (9.9) 10.8–15.0 ND-48 (9) ND-950 (187) ND 4.6–8.4 (7.4) 2.4–5.8 (4.3) 7.4–10.0 7.6 × 104 –3.0 × 105 (1.6 × 105 ) 1.5 × 104 –5.5 × 104 (3.6 × 104 ) 1.5 × 104 –5.5 × 104 Recreational purpose
2.20–3.50 (2.78)a 2.06–3.93 (2.89) 1.81–3.94
Target for total coliform counts (CFU/100 mL) Chlorine dose (mg/L) Influent total coliform counts (CFU/100 mL) Influent ammonia concentration (mg/L)
Table 3 Control results of continuous chlorination/dechlorination using the BPN models for different wastewater reuse purposes
Another series of continuous wastewater chlorination and dechlorination experiments were conducted to evaluate the proposed dynamic dose control strategy. Same real wastewater samples as the previous experiments were used in this continuous experiment. Five runs of continuous chlorination and dechlorination experiments were conducted and controlled using the proposed ANN control strategy. Each run was operated for >10 h. Control targets were set to achieve different water qualities for the five wastewater reuse purposes in Taiwan (Table 3). Table 3 shows the detailed influent water qualities and operational conditions of these five runs. Influent ammonia concentrations were around 1.81–4.04 mg/L, and total coliform counts were 104 –105 CFU/100 mL. Table 3 also shows ANN control results for continuous chlorination/dechlorination. For wastewater reuse for recreational purposes, total coliform counts of 50 CFU/100 mL and residual total chlorine >1.0 mg/L are recommended. Under this ANN control, chlorine doses were 4.60–8.43 mg/L and Na2 S2 O3 doses were 9.74–13.65 mg/L, resulting in effluent total coliform counts of ND-48 CFU/100 mL (average, 9 CFU/100 mL), and residual total chlorine concentrations of 0.64–1.42 mg/L (average, 1.15 mg/L). Both total coliform counts and residual total chlorine concentrations were effectively controlled. For reused wastewater for flushing toilets, total coliform counts must be <1000 CFU/100 mL and the remaining residual chlorine around 0.5 mg/L are recommended. For these control targets, chlorine doses were controlled at 2.40–5.81 mg/L, and the Na2 S2 O3 dose was 5.30–12.99 mg/L. The controlled result for total coliform counts was ND-950 CFU/100 mL (average, 186 CFU/100 mL), and that for residual total chlorine was 0.18–0.78 mg/L (average, 0.52 mg/L); both are within acceptable ranges. These required chlorine and Na2 S2 O3 doses were much lower than doses for recreational purposes, resulting in a savings of 40% for chlorine and 23% for Na2 S2 O3 . For wastewater reuse as air conditioner cooling water, an undetectable total coliform count and residual total chlorine concentration of >1.1 mg/L are required. Under ANN control, chlorine doses were 7.37–10.04 mg/L, and Na2 S2 O3 doses were 10.79–15.00 mg/L. Control results show that total coliform counts in all effluents were undetectable, and the effluent residual total chlorine concentration was 0.88–1.66 mg/L (average, 1.26 mg/L). These control results are acceptable for the reuse of air conditioner cooling water. Table 3 shows control results for other wastewater reclamation purposes. Generally, acceptable effluent water qualities were achieved. These experimental findings indicate that continuous chlorination and dechlorination were effectively controlled using the proposed ANN dynamic dose control strategy. Additionally, sig-
Control effluent total coliform counts (CFU/100 mL)
3.4. Dynamic dose control of wastewater chlorination/dechlorination for reuse
50
Na2 S2 O3 dose (mg/L)
Target for residual chlorine (mg/L)
Control effluent residual chlorine (mg/L)
roughly the next 5 min. When the predicted effluent total coliform count was located within 10% of the control target, this chlorine dose was confirmed; otherwise, the predicted chlorine dose was altered according to the difference between predicted and targeted total coliform counts. Control of the Na2 S2 O3 dose utilized a similar process as that for controlling the chlorine dose. The monitored ORP/pH values in the pre-mixing, chlorination and dechlorination tanks, and chlorine dose were used in BPN3 to predict the required Na2 S2 O3 dose. Then, the residual chlorine concentration predicted by BPN4 was used to compare the control target of residual chlorine concentration, and was used to tune the required Na2 S2 O3 dose. Then BPN5 was used to predict the forming residual chlorine concentrations in the chlorination tank, which can be employed as a reference for the required Na2 S2 O3 dose, thereby increasing control stability.
0.64–1.42 (1.15) 0.18–0.78 (0.52) 0.88–1.66
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Wastewater reclamation purpose
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nificantly different chlorine and Na2 S2 O3 doses were required to meet regulations for wastewater reuse proposes, indicating the potential to reduce chemical costs. 4. Conclusions This study applies ORP/pH monitoring and ANN models to dynamically control chlorination and dechlorination doses for wastewater reuse. Five BPN control models were developed to build dynamic dose control strategy for chlorination and dechlorination. A series of 411 continuous chlorination and dechlorination experiments were conducted under different influent ammonia concentrations and operational conditions. Experimental data were used to generate control rules for these ANN control models. Another series of continuous chlorination and dechlorination experiments were conducted to evaluate the proposed dynamic dose control strategy. Real wastewater samples were treated to meet different water quality requirements for wastewater reuse based on total coliform counts and residual chlorine concentrations. Experimental results show that chlorination and dechlorination doses were effectively controlled using the ANN control models. Different control targets for different wastewater reuse purpose were achieved. The potential benefits in reducing chemicals costs were also obtained. Acknowledgement The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC-93-2622-E-239-004-CC3. References APHA, AWWA, WEF. Standard methods for the examination of water and wastewater, 21st ed. Washington, DC: American Public Health Association; 2005. Bedner M, MacCrehan WA, Helz GR. Making chlorine greener: investigation of alternatives to sulfite for dechlorination. Water Res 2004;38:2505–14. Choi DJ, Park HY. A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process. Water Res 2001;35:3959–67.
1021
Devokta LM, Williams DS, Matta JH, Albertson OE. Variation of oxidation-reduction potential along breakpoint curves in low-ammonia effluent. Water Environ Res 2000;72:610–7. Donnermair MM, Blatchley ER. Disinfection efficacy of organic chloramines. Water Res 2003;37:1557–70. Dieu B, Garrett MTJ, Ahmad Z, Young S. Applications of automatic control system for chlorination and dechlorination processes in wastewater treatment plants. ISA Trans 1995;34:21–8. Eilbeck WJ. Redox control in breakpoint chlorination of ammonia and metal ammine complexes. Water Res 1984;18:21–4. Feitkenhauer H, Sachs JV, Meyer U. On-line titration of volatile fatty acids for the process control of anaerobic digestion plants. Water Res 2001;36:212–8. Ficara E, Rozzi A. pH-stat titration to assess nitrification inhibition. J Environ Eng, ASCE 2001;127:698–704. Finger RE, Harrington D, Paxton LA. Development of an zero chlorine residual measurement and control system. J WPCF 1985;57:1068–73. Harp DL. Specific determination of inorganic monochloramine in chlorinated wastewaters. Water Environ Res 2000;72:706–16. Kim YH, Hensley R. Effective control of chlorination and dechlorination at wastewater treatment plants using redox potential. Water Environ Res 1997;69:1008– 14. Kim H, Kwon S, Han S, Yu M, Gong S, Colosimo MF. New process control strategy for wastewater chlorination and dechlorination using ORP/pH. Water Sci Technol 2006;53:431–8. Lee DS, Park JM. Neural network modeling for on-line estimation of nutrient dynamics in a sequentially operated batch reactor. J Biotechnol 1999;75:299–339. Schreiber DC, Pavlostathis SG. Biological oxidation of thiosulfate in mixed heterotrophic/autotrophic cultures. Water Res 1998;32:1363–72. White GC. Handbook of chlorination and alternative disinfectants, 4th ed. NY: John Wiley & Sons Inc.; 1999, 212–85, 537–74. Wolfe RL, Ward NR, Olson BH. Interference in the bactericidal properties of inorganic chloramines by organic nitrogen compounds. Environ Sci Technol 1985;19:1192–5. Yeh YC. Artificial neural network models and applications, 2nd ed. Taipei: Rue-Lin Publisher; 2000, 23–145, 214–55 (in Chinese). Yu RF, Liaw SL, Cheng WY, Chang CN. Performance enhancement of SBR applying real-time control. J Environ Eng, ASCE 2000;126:943–8. Yu RF, Cheng WP. Determination of chlorine demand in water and wastewater chlorination by oxidation-reduction potential. Water Sci Technol Water Sup 2003;3:313–20. Yu RF. Feed-forward dose control of wastewater chlorination using on-line pH and ORP titration. Chemosphere 2004;56:973–80. Yu RF, Cheng WP, Chu ML. On-line monitoring of wastewater true color using digital image analysis and ANN. J Environ Eng, ASCE 2005;131:71–9. Zhang Q, Stanley SJ. Real-time water treatment process control with artificial neural network. J Environ Eng ASCE 1999;125:153–60. Zhao HW, Mavinic DS, Oldham WK, Koch FA. Controlling factors for simultaneous nitrification and denitrification in a two-stage intermittent aeration process treating domestic sewage. Water Res 1999;33:961–70.