Control-Oriented Modeling and Real-Time Control for the Ozone

Article pubs.acs.org/est

Control-Oriented Modeling and Real-Time Control for the Ozone Dosing Process of Drinking Water Treatment Dongsheng Wang,†,‡ Shihua Li,*,‡ and Xingpeng Zhou‡ †

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China School of Automation, Southeast University, Nanjing 210096, China

‡

S Supporting Information *

ABSTRACT: Ozonation is one of the most important steps during drinking water treatment. To improve the efficiency of ozonation and to stabilize the quality of the treated water, control-oriented modeling and a real-time control method for the ozone dosing process are developed in this study. Compared with existing ozonation models developed by bench-scale and pilot-scale batch experiments, the model reported herein is control-oriented and based on plant-scale batch experiments. A real-time control strategy for maintaining a constant ozone exposure is attempted to meet primary disinfection requirements. An internal model control scheme is proposed to maintain a constant ozone exposure by adjusting the ozone dosage. The proposed real-time control method can cope with changing water quality, water flow rate, and process operational conditions. Both simulations and experimental studies have been carried out and implemented for the ozone dosing process control system, and the results demonstrate the effectiveness and practicality of this real-time control method.

■. INTRODUCTION Ozonation has been commonly used as one of the most effective drinking water treatment processes for disinfection; oxidation of natural organic matter; degradation of hazardous micropollutants; algae inactivation; improvement of color, taste, and odor of water; and so forth. However, the ozonation process in bromide-containing water may lead to the formation of undesired byproduct bromate, which is considered a potential carcinogen for humans.1−3 A low value of 10 μg/L of bromate concentration has been set as a drinking water standard in both the United States and the European Union.4−6 For reliable disinfection and removal of water matrix compounds, sufficient ozone dosage is required. However, an overdose of ozone is uneconomical and will result in excessive bromate formation. Thus, the ideal ozone dosage should be a good trade-off between the effect of ozonation and the restriction of bromate formation.7,8 As a result of its high reactivity, ozone is an unstable oxidant in water and reacts with water matrix components easily. Many ozone decay models and ozone decomposition models have been proposed in the literature to describe the reaction process of ozone in water.9−12 All of these models are established and validated based on bench-scale or pilot-scale batch experiments. Owing to the complexity of the practical ozonation process, the characterization of ozone reacting with water matrix components in an actual drinking water treatment plant is often dissimilar from the results of bench-scale and pilot-scale experiments.13,14 More importantly, these ozone decay models and ozone decomposition models are only used for off-line decision support of operators, and they cannot be directly used © 2013 American Chemical Society

for the online real-time control purpose of a practical ozone dosing process. 15 Thus, research in control-oriented modeling for the ozone dosing process is urgently needed. Generally, there are two kinds of control strategies for the ozone dosing process of drinking water treatment. The easiest strategy is to maintain a constant ozone dosage. This control strategy is in fact an open-loop control manner. The ozone dosage is not adjusted and does not cope with the variations in water flow rate, water quality, and process operational conditions. 16 A more advanced control strategy is to maintain a constant dissolved ozone residual. This control strategy is usually implemented through a closed-loop control manner. With this control strategy, operators try to maintain the dissolved ozone residual as a constant value by manually adjusting the ozone dosage according to the deviation between the actual value and the set-point of the dissolved ozone residual.17−19 This human in the loop control manner leads to difficulty in achieving a satisfying control effort during the periods of frequent changes in raw water quality, water flow rate, and process operational conditions because it requires constant operator attention. During ozonation, the ozone exposure (Ct10) is a leading parameter in determining disinfection and bromate formation,11,15 where C denotes the dissolved ozone residual and t10 denotes the effective detention time, which is defined as the Received: Revised: Accepted: Published: 2197

April 21, 2012 December 14, 2012 January 3, 2013 February 11, 2013 dx.doi.org/10.1021/es303408q | Environ. Sci. Technol. 2013, 47, 2197−2203

Environmental Science & Technology

Article

control system of the Xiangcheng water treatment plant (XWTP) in Suzhou, China.

detention time at which 90% of water passing through the ozone contactor is retained. The nonuniform distribution of Ct10 reflects the actual detention condition for ozone in the water of contactors that have complex flow patterns and are composed of several series of tanks.20,22 Many Ct10 calculation methods have been discussed and evaluated in the literature.20,21 In practical engineering, the effluent Ct10 calculation method is the most basic and conservative method.

■. MATERIAL AND METHODS Ozonation Process Description. There are two ozonation steps during the drinking water treatment process in the XWTP: A preozonation step and a main ozonation step. Main ozonation entails disinfection, removal of natural organic matter, and an increase in biodegradable organic carbon for additional removal in the subsequent biologically activated carbon filter.29,30 The effect of main ozonation is directly related to the treated water quality. Thus, the focus of the control-oriented modeling and real-time control in this work is on the main ozone dosing step. There are two parallel main ozone contactors in the XWTP, each with a rated hydraulic capacity of 6000 m3/h. As shown in Figure 1, each of the baffled main ozone contactors consists of

21

Maintaining a suitable Ct10 to satisfy a primary disinfection requirement is more preferable than the aforementioned two kinds of control strategies.15,16 With this control strategy, the ozone dosage is adjusted to keep the Ct10 at a desired constant value. Up to now, there have been no reports of this control strategy being implemented in actual drinking water treatment plants. The main reason for this is that the raw water quality in developed countries is better, whereas the safety of the drinking water in many developing countries is not taken into account in spite of poor raw water quality and lagging drinking water treatment technology.23−25 The raw water quality of the water treatment plant studied in this work has been getting worse over recent years, and the fleet changes in raw water quality have been increasingly frequent. Therefore, a more reliable control method for a practical ozone dosing process is urgently needed to stabilize the quality of treated water. To this end, a real-time control method of maintaining a constant Ct10 is developed for the practical ozone dosing process. It should be noted that, for sufficient disinfection, the set-point of Ct10 should be set as a suitable value but this is dependent on the local climatic conditions of the water treatment plant. The practical ozone dosing process exhibits obvious characteristics of nonlinearity with time-delay. With human in the loop and conventional closed-loop PI control schemes, it is difficult to achieve satisfying real-time control performances. The Smith predictor control scheme has been providing an effective control method for the time-delay process since it was first proposed in the 1950s. However, it is sensitive to modeling errors and may lead to instability in some cases. 26 The major contribution of this work is the development of practical and advanced modeling and control methods for the ozone dosing process of drinking water treatment. The authors study the characteristics of the ozone dosing process and establish a control-oriented model. Through statistical analysis based on the batch open-loop step response experiments, the parameters of the control-oriented model are determined using the multilinear regression method and they can cope with the variations in the chemical conditions. The effectiveness of internal model control (IMC) has been demonstrated in the process industry since it was first proposed in the 1980s. The simple control structure and few adjusting parameters make the design easier and more practical for the improvement of robust stability and disturbance rejection.27,28 Thus, a real-time control scheme based on IMC is proposed for maintaining a constant Ct10. One novelty of this work lies in the fact that the established model is control-oriented and can be applied directly to the real-time control of the practical ozone dosing process. The other novelty of this work is that the authors propose an IMC scheme based on the control strategy of maintaining a constant Ct10, which is more preferable than the general control strategy of maintaining a constant ozone dosage or a constant dissolved ozone residual for the disinfection requirement of safe drinking water. To date, this work has been experimented successfully in the practical ozone dosing process

Figure 1. Main ozonation process.

six cells. Owing to the effects of baffling on contactor hydraulics, the ozone contactors create highly complex water flow patterns: plug flow, mixed flow, and recirculation. The ozone/oxygen mixture gas is injected into the water by fine bubble diffusers in cell 1, cell 3, and cell 5, as shown in Figure 1. In consideration of ozonation efficiency and minimal bromate formation, a three-level serial dosing manner with a 3:1:1 ratio of ozone dosage is adopted. The ozone dosage is defined as the mass of ozone produced for 1 L of water, with a normal range of 0.6−1.4 mg/L. Three dissolved ozone concentration analyzers are installed on the outside wall of each ozone contactor to continuously monitor the dissolved ozone concentration within the contactor. The ozone probes are inside the water sampling bottles of the analyzers. The water samples are drawn from the monitoring points within the contactor (A, B, and C in Figure 1) to the water sampling bottles through sampling pipes. With the dissipation of influent water flow energy for baffle walls, the dissolved ozone residual reading of monitoring point C fluctuates more mildly than the readings of monitoring points A and B, and it is taken as the approximate dissolved ozone residual at the outlet. The targeted range of the dissolved ozone residual at the outlet is 0.2−0.45 mg/L. The ozone generators produce the required quantity of ozone, which is determined as ozone dosage multiplied by water flow rate. In this work, the ozone gas concentration is set as a suitable value. The gas flow rate is modified in real time to satisfy the varying ozone requirements. Control-Oriented Modeling. A typical characterization of the ozonation process of natural water consists of a rapid ozone consumption step followed by a slower ozone decay step.31−33 In the plant-scale ozonation process of drinking water treatment, the rapid ozone consumption step is terminated so 2198

dx.doi.org/10.1021/es303408q | Environ. Sci. Technol. 2013, 47, 2197−2203


Article

water temperature, and water flow rate as the affecting factors of static amplification coefficient K and inertia time constant T. The static amplification coefficient K of the first-order process is defined as

quickly that it is impossible to be measured online, and, therefore, it is usually neglected.34 The slower ozone decay step can be taken as kinetically first-order step,19,34 which can be described as:

d[O3] = −kc[O3] dt

K= (1)

dy(t ) + y(t ) = Ku(t − τ ) dt

(3)

where y(∞) is the steady-state output, y(0) is the initial output, and Δu is the input variation. The inertia time constant T can be derived by employing the commonly used two-point method as follows:

where [O3] is the ozone concentration in water (mg/L) and kc is the first-order rate constant for ozone decay (1/min). Many studies based on bench-scale and pilot-scale experiments have been conducted to illustrate that the decay rate constant kc varies with factors such as water temperature, pH, and detention time.9,11,19 Owing to the complexity of the reaction, researchers often use empirical functional forms to predict the dissolved ozone residual instead of using chemical kinetic forms.10,13 Because the water flow rate, raw water quality, and process operational conditions vary simultaneously, the practical ozonation process is more complex than the typical characterization based on laboratory-scale experiments. To establish a control-oriented model of the practical ozone dosing process that is suitable for continued real-time control, it is necessary to predict the dissolved ozone residual of the main ozone contactor by taking full advantage of the available online information. In our work, the dissolved ozone residual of the main ozone contactor is taken as a controlled variable, and the ozone dosage is taken as a manipulated variable. To identify the practical ozone dosing process, open-loop step response experiments are repeated many times, with changes in the ozone dosage and continuous online monitoring of the dissolved ozone residual in various influent water qualities and water flow rates. The batch experimental results show that the practical ozone dosing process presents an obvious firstorder plus time-delay characteristic. Thus, the control-oriented model of the practical ozone dosing process can be described approximately in the following first-order plus time-delay equation form T

y(∞) − y(0) Δu

y(t1) = 0.39y(∞)

(4)

y(t 2) = 0.63y(∞)

(5)

T = 2(t 2 − t1)

(6)

where t1 is the time at which the output value is 39% of the steady-state output value, and t2 is the time at which the output value is 63% of the steady-state output value. It can be seen from the batch open-loop step response experiments that the static amplification coefficient K and the inertia time constant T in eq 2 change with the variations in chemical conditions. In this work, the multilinear regression method, which is commonly used in the parameter identification of water treatment modeling,13,35,36 is adopted for determining the static amplification coefficient K and inertia time constant T. ln(Y ) = b0 + b1ln(COD) + b2 ln(Tur) + b3 ln(Temp) + b4 ln(Flow)

(7)

where Y is the dependent variable (K or T), bi is the fit coefficient, COD represents the chemical oxygen demand, Tur represents the turbidity, Temp represents the water temperature, and Flow represents the water flow rate. Real-Time Control Scheme. For the convenience of control implementation, the effluent Ct10 calculation method is adopted in this work. In this method, the t10 is calculated as the hydraulic residence time (HRT) multiplied by the baffling factor (t10/HRT). 22 The HRT is calculated as the volume of water in the ozone contactor (1200 m3) divided by the water flow rate. The HRT varies from 20.6 to 12 min with the operational range of water flow rate from 3500 to 6000 m3/h. The design criterion of the baffling factor of the ozone contactor in the XWTP is 0.65, but it has not been validated because the XWTP was constructed and began operating. Thus, we have to assume the baffling factor is 0.65. The simplified block diagram of the IMC scheme is shown in Figure 2. Gp represents the actual controlled process, Gm represents the model of the actual controlled process, and Gc represents the IMC controller. Realization of the IMC controller is achieved by factorizing the process model into

(2)

where y is the dissolved ozone residual, u is the ozone dosage, K is the static amplification coefficient, T is the inertia time constant, and τ is the time-delay parameter. Model Parameter Identification. Open-loop step response experiments were carried out under different chemical conditions (water quality and water flow rate) from Nov. 2009 to Oct. 2011. The ranges of chemical conditions for the water at the ozone contactor inlet are listed in Table S1 of the Supporting Information. It is noted that the chemical conditions should be approximately stable when the experiments are performed. In most drinking water treatment plants in China, the chemical oxygen demand (COD) is usually available online and can be adopted for the real-time characterization of organic compounds in water. Other organic compound parameters, such as DOC, TOC, and UV, are monitored by laboratory analysis (daily/weekly/monthly). It should be pointed out that the range of pH for the water of the main ozone contactor inlet in the XWTP is 7.2−7.5, and we consider the pH to be approximately stable in this work. Thus, to identify online the control-oriented model, and for it to be convenient for the modeling process, we choose COD together with turbidity,

Figure 2. Internal model control scheme. 2199



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Figure 3. Modeling results of four different chemical conditions: (a) case a of Table S2, (b) case b of Table S2, (c) case c of Table S2, (d) case d of Table S2 of the Supporting Information.

Figure 4. Simulation results for the real-time control of nominal case: (a) Dissolved ozone residual, (b) ozone exposure.

invertible and noninvertible parts. For the first-order plus timedelay model here, that is, eq 2, the invertible part is the firstorder part, which is described by a rational proper transfer function. The noninvertible part includes time-delay and righthalf-plane zeros. The IMC controller design consists of inverting the invertible part of the process model and cascading it with a linear filter to make the controller proper, as given by eq 8. The order of the filter is chosen to be 1 because the ozone dosing process considered here can be described as a first-order form. The filter-tuning parameter τc should be selected appropriately according to the closed-loop performance and the robustness of the IMC control process.

Gc(s) =

(Ts + 1) (τcs + 1)K

(8)

where T is the inertia time constant and K is the static amplification coefficient.

■. RESULTS AND DISCUSSION Modeling Results. From the open-loop step response experiments under different chemical conditions, the static amplification coefficient K and the inertia time constant T are determined as 2200



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Table 1. Performance Indices of Output Response of Dissolved Ozone Residual for the Simulation Results of Real-Time Control of Nominal Case 0−50 min

Table 2. Performance Indices of Output Response of Dissolved Ozone Residual for the Simulation Results of Real-Time Control of Model Mismatch Case

50−100 min

0−50 min

method

overshoot (%)

settling time (min)

IAE (mg/L)

overshoot (%)

settling time (min)

IAE (mg/L)

IMC PI

0 8.3

13 25

0.061 0.095

0 2.0

12 16

0.019 0.034

ln(K ) = −1.043 − 0.038ln(COD) − 0.027ln(Tur) − 0.071ln(Temp) + 0.039ln(Flow)

(10)

A higher positive value of the coefficient of regression equations yields a more increased contribution to K and T, whereas a lower negative value has a more reduced effect. This shows that the contribution of water temperature to K is the biggest among all the factors, whereas the contribution of water flow rate to T is the biggest among all the factors. In fact, the water flow rate influences the K and T by the contact time of ozone with water. The following Theil’s inequality coefficient (TIC) value represents the goodness-of-fit between calculation and measurement values, where yc,i is the calculation value and ym,i is the measurement value: TIC =

+

2 ∑i ym,i

settling time (min)

IAE (mg/L)

overshoot (%)

settling time (min)

IAE (mg/L)

IMC PI

0 0

17 24

0.068 0.106

0 0

18 26

0.021 0.038

Gp(s) =

0.46 −7.8s e 2.6s + 1

(12)

is considered here as the actual process. The corresponding chemical conditions are listed in Table S3 of the Supporting Information. Note that, in this work, the unit of time constants is minutes (min)ar. The overshoot, settling time, and integral of absolute error (IAE),

∑i (yc,i − ym,i )2 2 ∑i yc,i

method

overshoot (%)

During the practical ozone dosing process, all of the factors of the chemical conditions change simultaneously and cannot be controlled artificially. It is difficult to cover all of the chemical conditions for modeling. Thus, more open-loop step response experiments with different chemical conditions are necessary to further validate the proposed control-oriented model. Simulation Results of Real-time Control. The simulation of the IMC scheme is conducted to maintain Ct10 at a constant value of 3.5 mg·min/L. A PI scheme is also included in the simulation for comparison purposes. The following first-order plus time-delay model:

(9)

ln(T ) = −0.337 + 0.018ln(COD) + 0.078ln(Tur) − 0.069ln(Temp) + 0.178ln(Flow)

50−100 min

IAE(t ) =

(11)

Figure 3 shows the modeling results for open-loop step response experiments with ozone dosage changes from 0.6 to 0.75 mg/L at t = 10 min in four different chemical conditions. The corresponding chemical conditions, model parameters, and TIC values are listed in Table S2 of the Supporting Information. It can be seen that the control-oriented model appropriately predicts the measurement value. The TIC values are all much lower than 0.3, indicating good agreement, and they can be applied for the real-time control of the ozone dosing process. 37

1 N

N

∑ |r(t ) − y(t )| t=1

(13)

are chosen as the quantitative indices to evaluate the closedloop performance, where r(t) is the reference signal and y(t) is the actual process output. Simulations for real-time control of the nominal case and the model mismatch case are conducted with a change in the water flow rate from 4000 to 5300 m3/h at t = 50 min. The t10 values are changed from 11.7 to 8.8 min resulting in a set-point change in the dissolved ozone residual from 0.3 to 0.4 mg/L. Figure 4 shows the simulation results of the nominal case, where the established process model matches the actual process, eq 12.

Figure 5. Simulation results for the real-time control of model mismatch case: (a) Dissolved ozone residual, (b) ozone exposure. 2201



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Figure 6. Experimental results for the real-time control: (a) Dissolved ozone residual, (b) ozone exposure.

and control schemes are programmed on the PC and executed through a programmable logic controller (PLC). The experimental results of the proposed IMC scheme and the PI scheme under similar chemical conditions are shown in Figure 6. The corresponding performance indices are shown in Table 3. It can be seen from Figure 6 and Table 3 that the IMC scheme provides a better control performance than the PI scheme. This is basically consistent with the simulation results. In addition, the PI controller exhibits obvious performance degradation when the process operational conditions are changed, whereas the IMC controller demonstrates good robustness against such variation. This can also be explained from the analysis of the simulation results of the model mismatch case. To realize more reliable real-time control of the ozone dosing process, the proposed IMC scheme should be tested for a longer period of time.

Table 3. Performance Indices of Output Response of Dissolved Ozone Residual for the Experimental Results of Real-Time Control method

overshoot (%)

settling time (min)

IAE (mg/L)

IMC PI

2.2 4.8

15 19

0.013 0.016

The corresponding performance indexes are listed in Table 1. It is clear that both the proposed IMC scheme and the PI scheme can track the changing set-point of the dissolved ozone residual. It also indicates that the IMC scheme provides no overshoot, a shorter settling time, and a faster set-point tracking performance, whereas the PI scheme exhibits a considerable overshoot, a longer settling time, and a slower set-point tracking performance. In the drinking water treatment process, the raw water quality is sometimes changes abruptly owing to bad weather or a polluted water source. This results in the influent water quality of the ozone contactor overstepping the normal range and in a mismatch between the established model process and the actual process. In addition, changes in the process operational conditions, such as turbidity control and filter backwashing, may also affect the influent water quality of the ozone contactor and cause model mismatch. To confirm the robustness of the proposed IMC scheme, 30% decreases in K and T in eq 12 are simulated for the model mismatch case. The corresponding simulation results and performance indices are shown in Figure 5 and Table 2, respectively. It can be observed from Figure 5 and Table 2 that the IMC scheme also provides a good set-point tracking performance, whereas the PI scheme shows obvious performance degradation. This is mainly because the model mismatch is handled by adjusting the filter-tuning parameter τc in eq 8 for robustness. The simulation results of the model mismatch case demonstrate that the IMC scheme is remarkably superior in the presence of abrupt changes in raw water quality and process operational conditions, which are close to the features that exist in real practice. Experimental Results of Real-Time Control. The proposed IMC scheme and the PI scheme are tested to maintain the Ct10 as a constant value 3.5 of mg·min/L in the XWTP. All of the online signals from or to the ozone dosing process are interconnected through a distributed control system (DCS). Process data are saved in the database of a PC server,

■

ASSOCIATED CONTENT

S Supporting Information *

This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected], tel: +86 25 83793785. Notes

The authors declare no competing financial interest.

■

REFERENCES

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Control-Oriented Modeling and Real-Time Control for the Ozone

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