Automation and Control of a Dissolved Air Flotation ...

Preprints of the 20th World Congress The International Federation of Automatic Control Toulouse, France, July 9-14, 2017

Automation and Control of a Dissolved Air Flotation Pilot Plant Rodolpho R. Fonseca*. José P. Thompson, Jr.* Ivan C. Franco**. Flávio V. da Silva* 

*School of Chemical Engineering, University of Campinas, Campinas, Brazil (Tel: +55 19 35213946, e-mails: [email protected];[email protected]; [email protected]) **Department of Chemical Engineering, FEI University, São Bernardo do Campo, Brazil, (e-mail: [email protected])

Abstract: Turbidity is an important process variable on water treatment processes for safeguard health requirements, and dissolved air flotation (DAF) is a physical treatment that allows water turbidity reduction by floating particles dispersed on water. Hence, a DAF pilot plant was assembled and automated using supervisory control and data acquisition (SCADA), open platform of communication protocol (OPC), signal filters and Ethernet to test two possible strategies for water turbidity control. The results indicated that manipulating recycle ratio by needle valve opening has advantages on DAF control. Keywords: Automation, Process Control, Turbidity, Water Treatment, Dissolved Air Flotation. 

1. INTRODUCTION Water turbidity is one of the parameters that may be controlled in water treatment processes to make water potable, and it is due by small particles dispersed in water that are difficult to separate by decantation. In Brazil, government demands water distribution companies to monitor turbidity on drinking water because health safety requirements. For this reason, flocculation and flotation processes are applied in water treatment stations in attempt to reduce water turbidity (Rubio et al., 2002; Edzwald, 2010). In water treatment process, chemicals are added to water during flocculation to aggregate small particles and produce flocs with high surface area. These flocs are injected in a flotation tank with air micro-bubbles and adhere one to each other, reducing floc density and allowing it to float. The air micro-bubbles are used in dissolved air flotation systems (DAF), in which air is dissolved in clean water recycled from the flotation tank. The clean water is injected in a pressurized saturation vessel, also named saturator, to dissolve air and then be released into the flotation tank through a needle valve. Then, producing air micro-bubbles of dimensions among 20 to 100 µm that enable the removal of colloidal and ultrathin particles below 5 µm (Edzwald, 2010). The floated material is then removed on the top of flotation tank by a skimmer and the clarified water removed on the bottom. Thus, because the proven efficiency of DAF on turbidity removal, it is the most utilized technique in drinking water and industrial effluent treatments (Rubio et al., 2002). However, a very common flotation system is the column flotation that operates with macro-bubbles to remove big size particulates on water (Rubio et al., 2002) and it is mainly utilized in mining processes, specifically, for impurities separation of ore extraction. For column flotation, some control strategies have already been proposed (Carvalho et Copyright by the International Federation of Automatic Control (IFAC)

al., 1994; Carvalho and Durão, 2002; Bouchard et al., 2009, Núñez et al., 2010, Yinfei et al., 2011), with different approaches to the process variables. On the other hand, there is a lack of published works on literature concerning process automation and control of DAF systems. There are many differences between column flotation and dissolved air flotation in terms of process operation, not being possible to adapt column flotation control strategies to DAF. Analysis of the best manipulated variable to control the output stream turbidity using a SISO strategy is desirable for DAF process automation and control. Although DAF seems to be easy to operate, flotation systems have non-linear behavior (Bergh and Yianatos, 2011), requiring process control development. With DAF process automation it may be possible to ensure drinking water potability by controlling the turbidity, and also respect secure operation conditions and plant operability with the best performance as possible. Therefore, detailed studies concerning DAF automation and control strategies are needed and this paper presents the automation of a DAF pilot plant with supervisory control and data acquisition (SCADA), signal filters and other technologies. Experiments were done with different control strategies to identify the best way to regulate output stream turbidity. 2. MATERIALS AND METHODS 2.1 Effluent characterization The effluent used on the experiments was prepared dissolving clay in clean water for an effluent turbidity of almost 40 NTU. In reason of clay decantation, the effluent was continuously homogenized using a recycle stream on storage

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Preprints of the 20th IFAC World Congress Toulouse, France, July 9-14, 2017

tank. The DAF process flowsheet is illustrated in Fig. 1 for better understanding.

used an unpacked saturation vessel for sake of simplicity. It was equipped with pressure transmitter, output flow meter, differential pressure transmitter for level inference, pressure control valve and also a manometer and a pressure relief valve for safety requirements.

Fig. 1. DAF process flowsheet. 2.2 Flocculation To flocculate the clay particles, sodium aluminate (NaAlO2) was added in proportion of 0.7 mL to 1.0 L of effluent in the storage tank, alkalising the pH to almost 10. Then, the effluent was pumped to the flocculation tank at a flow rate of 0.05 L/s, in which a solution of tannin SG at 10% (v/v) was added until pH decreases to 8.5, promoting the flocculation. To mixture the chemicals during flocculation, stirrers were installed on the flocculation tank. The Fig. 2 represents the flocculation system with other equipments in the DAF pilot plant. 2.3 Flotation and Filtration After flocculation, the effluent was sent to flotation tank and injected with air micro-bubbles at the bottom on the contact zone, as can be seen in Fig. 3. This allows micro-bubbles to adhere to the flocs, producing floc-bubble aggregates that float on the separation zone, what results on water turbidity reduction. In consequence, sludge accumulates on the top of the flotation tank and a skimmer was actuated to remove it. At the bottom, clarified water was sampled by an online turbidimeter for turbidity monitoring and to feed control loops with output stream turbidity value. The clarified water was also drained to a sand filter to remove few particles that remained after flotation, as a typical water treatment station. After the filter, clean water was storage to feed the saturation vessel.

Fig. 2. Flocculation system with emphasis on some instruments. An electromagnetic gear pump, defined as recycle pump in Fig. 4, was used to feed saturation vessel with clean water produced on DAF process.

2.4 Saturator An unpacked saturation vessel, presented in Fig. 4, was used to dissolve air into clean water for micro-bubbles production. Packed saturators are more efficient for air saturation in water than unpacked saturators because the high contact area (Bratby and Marais, 1975), but on the DAF pilot plant was

Fig. 3. Flotation tank with emphasis on sludge, contact zone and separation zone.

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uses the input value y(k), the past value x(k-1) and the filter level to calculate the new output value x(k). The filter level was set to 8, as presented in (1).

Fig. 4. Instrumented saturated water generation system with online turbidimeter device, sand filter and recycle pump. 2.5 DAF pilot plant automation For micro-bubbles production is necessary a great pressure drop on saturator output stream and needle valves are often used for this purpose (Crossley and Valade, 2006). At DAF pilot plant, a needle valve with Cv equal to 0,09 was used. However, needle valves are usually manual operated. Therefore, to automate the valve opening on DAF pilot plant, a step motor was coupled to the valve, as depicted in Fig. 5. The step motor was controlled using a programmable logic controller (PLC) and a step motor driver, which has digital inputs and outputs ports for PLC communication. Ladder logic was utilized on PLC to move step motor 3 steps forward or backward, depending on valve opening setpoint and its actual position. The step motor used has 27,250 positions between the valve opening limits, allowing an accurate valve opening control. However, a dead band of 15 steps was set to valve opening control to avoid actuator overloading, which can cause step motor overheating.

Fig. 5. Step motor coupled to needle valve for microbubbles generation by pressure drop on flotation tank feed stream. However, this filter was insufficient for noise rejection on saturator liquid level and output stream turbidity signals.

The PLC has Ethernet communication and it was used on DAF pilot plant automation, for sharing data via OPC with the supervisory control and data acquisition system (SCADA), developed on MatLab/Simulink® and presented in Fig. 6. The SCADA was developed with blocks to set plant control mode among open and closed loops. Also blocks to set plant operation mode were configured, once the DAF pilot plant was assembled to operate in three different modes of DAF, that are not discussed on this work. In this paper, only recycle operation mode was utilized as it is the most usual DAF process. To assist process control, SCADA also display process variables values and input blocks for configuring setpoint values, as can be observed on Fig. 6. The instrumentation signals of sensors on DAF pilot plant were analog 4 to 20 mA with 24 Vdc, and some signal filters were implemented to reduce noise interference on instrumentation signals. A pre-configured filter on PLC analog input card was used to all analog inputs. This filter

Fig. 6. Supervisory Control and Data Acquisition system interface developed to DAF pilot plant.

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The liquid level signal was provided by a differential pressure transmitter installed at the unpacked saturator, which has the clean water input stream on the top, resulting on high level turbulence that is similar to a high-frequency noise.

x( k )  x( k 1)  x( k 1) 8  y( k ) 8

(1)

In this case, a moving-average digital filter defined by Seborg (2004) and presented in Z transfer function form (G(z)) in (2) was implemented. It uses 35 past data points to calculate an average for liquid level value. The filter works as a low-pass filter to remove the influence of turbulence on level measurement.





G( z )  1 35  Z 35  1 Z 35  Z 34



(2)

For output stream turbidity, sometimes peaks of high and low values occur on turbidity signal. It happens probably because a huge floc enters the turbidimeter assay chamber producing this peak on measurement, or even by the turbidimeter cleaning ultrasonic system that periodically removes particles that decant inside the assay chamber. This peak is undesirable for process control once turbidity is the process variable and extreme values of error may cause actuators damage. In this particular case, a noise-spike filter also described by Seborg (2004) and represented in (3) was implemented, using a maximum allowable change (Δy) of 0.2 NTU.

x( k )

 y( k ) , if    x( k 1)  y , if x  ( k 1)  y , if

y( k )  y( k 1)  y x( k 1)  y( k )  y x( k 1)  y( k )  y

(3)

Effluent inlet flow rate control was controlled manipulating effluent pump rotation frequency by using a sampling time Δt of 1 second and a digital proportional-integral controller (PI) with equation detailed in (4). An ideal PI controller was also applied for level control in the saturator by manipulating recycle pump rotation frequency.

  t k u PI ( k )  K c   e( k )   e(i )   I i 1  

(4)

These pumps had their frequency manipulated by drivers with control signal reference from analog outputs of PLC. The PI parameters for effluent flow rate and saturator level control are presented on Table 1. Also the pressure control valve that regulates the saturator pressure with compressed air was operated using a reference control signal. However, it has an internal control system that tracks the reference value. Table 1. Effluent flow rate and saturator level PI controllers tuning. Effluent flow rate PI Saturator level PI Kc 1.0000 (Hz·min/L) 0.6366 (Hz/%) τI 1.0000 (s) 212.2000 (s) For security requirements and equipment protection against unsafe operating conditions, override control loops were implemented on DAF pilot plant for pressure, level and recycle flow rate control. The override control for saturator pressure was set for safe requirements as the saturation vessel

and instruments could be damaged in case of overpressure. With safe operating limits between 7.5 to 1.0 bar, pressure was regulated to ≤ 6.0 and ≥ 2.0 bar in case of over and under pressure, respectively. The override control for liquid level was set for both equipment protection and safe requirements. If saturator drains out, only air flows through the recycle stream flow meter, damaging the sensor. On the other hand, when level achieves 100.0%, the vessel can blow out or even damage the sensors. Hence, safe operating conditions for liquid level were set between 20.0% and 60.0%, with safe recycle pump frequencies set to 45 and 28 Hz, respectively. At last, the override control for recycle flow rate was set for sensor protection, once flow rate over the limit of 0.0085 L/s may damage the sensor. For this override loop, valve opening was set to 1.0% when necessary. 2.6 Flotation process control Two different control strategies were tested on this paper to identify the best control loop for output stream turbidity regulation. In both cases, ideal PI controllers with anti-reset windup protection were applied, using coefficient value equal to 1.5 on anti-reset windup back-calculation method. To tune parameters Kc and τI of PI controllers, were used first order plus dead-time models (FOPDT) identified from DAF pilot plant with signal constraint block of MatLab/Simulink®, aiming time response lower than 1500 s. The first control strategy (A) takes the saturator pressure as manipulated variable once it affects dissolving air in water (Bratby and Marais, 1975). With more air dissolution, more micro-bubbles may be produced after needle valve pressure drop. It is well known the amount of micro-bubbles required for efficient flotation depends on flocs input concentration on flotation tank. In case of low flocs concentration, DAF system may operate with low pressure on saturation vessel, allowing an efficient flotation and also not wasting air supply. Therefore, saturation vessel pressure manipulation for turbidity control avoids air supply wasting. The second strategy (B) uses the needle valve opening as manipulated variable, what affects directly the recycle ratio on DAF process. Depending on recycle ratio value, the amount of micro-bubbles supplied on flotation tank changes and affects floc-bubble aggregate formation (Edzwald, 2010), allowing turbidity control. DAF process with high values of recycle ratio may produce clean water with low turbidity. However it represents more power consumption on micro-bubbles production because recycle flow rate is high, and also reduces DAF process productivity, once a high fraction of clean water has to be recycled to the saturator. Hence, needle valve opening manipulation for turbidity control with low recycle ratio as possible is desirable. To compare both proposed control strategies, two tests during 5200 s were performed with DAF pilot plant on closed loop. Experiments were started with similar initial condition for output stream turbidity at approximately 7 NTU. Then,

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setpoint was set to 6 NTU only for better observation of output turbidity dynamic under process control.

control strategies performance. The PI parameters tuned for both turbidity control strategies are presented on Table 2.

3. RESULTS AND DISCUSSION

Table 2. Turbidity PI controller tuning.

3.1 Signals filtering The non-filtered and filtered liquid level measurement using the digital filter described in (2) is shown in Fig. 7. As can be observed, there was a great improvement on level measurement comparing non-filtered and filtered signals with no large delay on filtered signal, what is desirable for process control.

Fig. 7. Comparison between saturation vessel level filtered and non-filtered signals. For turbidity measurement, Fig. 8 represents non-filtered and filtered signals using the filter described in (3). It can be observed that noise-spike filter only rejected spikes on turbidity signal with changes higher than Δy. In Fig. 8b, an emphasis is taken on two spikes between times 5000 s and 5100 s and, as expected, the filter rejected them. This noisespike rejection improves turbidity control once it avoids high changes on control action caused by high values of error, protecting the actuators from overloading.

Fig. 8. Comparison between filtered and non-filtered signals of flotation output stream turbidity using noise-spike filter (a) with emphasis on spike at 5000 s (b). 3.2 Control strategies performance After steady state achievement, DAF pilot plant was set to closed loop with turbidity setpoint at 6 NTU to analyze

Control strategy A Control strategy B Kc 0.260 (bar/NTU) 0.048 (%/NTU) τI 185.700 (s) 19.200 (s) The process and manipulated variables dynamics using control strategy A are shown in Fig. 9. At the beginning, the turbidity was kept close to its setpoint, but after 1500 s it increased and also the saturator pressure in attempt to regulate the turbidity. However, the manipulated variable saturated on upper limit at time 2100 s and process variable started to decrease slowly.

Fig. 9. Dynamics of process and manipulated variables under control strategy A on DAF pilot plant. As can be observed, at the end of experiment, turbidity did not achieve setpoint value. This sluggish response for flotation output stream turbidity is undesirable, once takes too much time to regulate the process variable and, obviously, consumes more power to control the output turbidity. Therefore, this sluggish control also may cause early sand filter saturation, affecting drinking water quality. Thus, even being possible to control output stream turbidity using saturator pressure as manipulated variable, it is not the best choice for turbidity control on DAF based on its high time response. On the other hand, using the recycle ratio as manipulated variable by changing needle valve opening demonstrates to be a better control strategy, as observed in Fig. 10. For recycle ratio, limits were set to avoid recycle stream overflow that causes effluent input dilution with turbidity masking, and also to prevent low recycle flow that compromises microbubbles production caused by small valve opening (Rykaart and Haarhoff, 1995). The upper and lower limits for needle valve opening were respectively set to 1.5% and 2.3%, as depicted in Fig. 11. With these valve opening limits, recycle ratio would vary among 15.0% and 8.0%, respectively. In Fig. 10, it possible to observe that applying control strategy B, process time response was small compared to strategy A. This behavior is desirable and indicates that

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output stream turbidity has a lower sensitivity to saturation vessel pressure than needle valve opening, or indirectly the recycle ratio. Even increasing the operating pressure range on saturator in attempt to improve turbidity control, it would represent more power consumption on supplying air and pumping water to saturator, becoming a disadvantage. Though output stream turbidity did not stabilized with control strategy B but oscillated around the setpoint value, its time response was reduced. This oscillatory behavior was observed probably in reason of PI control parameters that were not well tuned using a FOPDT model, once flotation process has non-linear dynamic. As consequence, manipulated variable saturated like an ON/OFF control system, as shown in Fig. 11. However, with better PI parameters tuning, for example decreasing Kc value, or even applying an adaptive control, process variable stabilization on setpoint should be reached.

for remote plant monitoring and control using OPC and Ethernet communication. Also, two different signal filters were successfully implemented. A moving-average digital filter for saturator liquid level, once a noisy measurement of level was observed, as well a noise-spike filter for turbidity to reject spikes. For output stream turbidity control, two strategies were tested, one using saturator pressure and other using needle valve opening as manipulated variable. The results indicate that output turbidity is more sensitive to needle valve opening manipulation than saturator pressure. Thus, it is possible to conclude that output stream turbidity is better controlled by acting on needle valve opening, that reflects on low process time response and power saving. ACKNOWLEDGEMENT The authors appreciate the financial support provided by FAPESP under the project N° 2015/05207-8. REFERENCES

Fig. 10. Dynamics of process and manipulated variables under control strategy B on DAF pilot plant.

Fig. 11. Needle valve opening (actuator) dynamic compared to its control signal value under control strategy B. In both experiments, no unsafe operating condition was observed. Therefore, none of implemented override controls were requested, what is desirable because indicates that control loops did not driven the process to insecure conditions. 4. CONCLUSIONS An automatic DAF pilot plant was assembled with effluent flow rate, saturator pressure and liquid level controls, and output stream turbidity control using a step motor coupled to a needle valve on recycle stream. A SCADA was developed

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