Water Desalination

Desalination 126 (1999) 15–32Process control in water desalination industry: an overview Imad Alatiqi*, Hisham Ettouney, Hisham El-Dessouky Department of Chemical Engineering, College of Engineering and Petroleum, Kuwait University, PO Box 5969, Safat 13060, Kuwait email: [email protected] Abstract Process control is an essential part of the desalination industry that requires for operation at the optimum operating conditions an increase in the lifetime of the plant and reduction of the unit product cost. A review is presented for the commonly used and newly developed control and instrumentation in MSF and RO plants. Process control may be as simple as an on/off valve that is triggered upon offset of the system measured parameters from the desired set-point. More classical and commonly used controllers have combined proportional, integral, and derivative (PID) systems. Also, proportional/integral (PI) controls are used in industry. Controls selection aims at fast response, high stability, and minimum disturbance to the system. Less common controllers include fuzzy logic-based systems. Early testing of such systems shows the need for mathematical analysis of various control loops within the plant, development of control rules, and development and testing on industrial scale. Supervisory systems such as model predictive control are also considered to obtain an integrated control systems of the whole plant. It should be stressed that although the desalination plants are highly complex, accurate and detailed mathematical models for steady state and process of various desalination processes are found in the literature. Such models are necessary for studying plant performance, various control strategies, and forms an essential part for any serious analysis and development of novel and new control systems. Keywords: Seawater desalination; Process control; Instrumentation; Steady-state and dynamic models 1. Introduction Water desalination on an industrial scale was initiated during the early part of the 20th century. However, actual expansion and spread of the *Corresponding author. industry occurred during the period from 1960 to 1980 (El-Dessouky et al., 1999a). During this period, the industry standard was the multi-stage flash (MSF) desalination plant with 24 stages and a production capacity of 6 migd (Al-Shuaib et al., 1999). During the period 1980–1999, the reverse osmosis (RO) process started to gain ground in Presented at the Conference on Desalination and the Environment, Las Palmas, Gran Canaria, November 9–12, 1999. European Desalination Society and the International Water Association. 0011-9164/99/$– See front matter © 1999 Elsevier Science B.V. All rights reserved A separate section includes discussion of measuring instruments. flow rate. ratio. 1998a). The evaporation processes include the single-effect systems (Al-Juwayhel et al. fuzzy. This paper reviews existing control systems for the MSF and RO desalination processes. pattern recognition. 1998a). 2. forward feed multiple effect (El-Dessouky. control systems. and state estimation (observer/Kalman filter). and feed forward. and parallel feed multiple effect (El-Dessouky et al. level. pressure. and energy recovery turbines. 2. and/or an alarm if necessary. The conventional strategies include manual. expert system. pH. a data logger. and Smith-predictor control. which include gain scheduling. the desalination industry expanded into new markets where the number of operating plants has increased to more than 12. Fig. Control systems can be divided into two main categories that include conventional strategies and advanced control. which may include temperature. which shows an external disturbance affecting the plant. As for the advanced control system. decoupling control. 1997). dead-time compensation. The corrective action should derive the plant back to the desired set-point. and scaling compounds. mechanical vapor compression (Ettouney et al. statistical quality. and simulators (Ismail. feed and product treatment system.. In addition. which forms an essential part of the control process. The processing of a signal is an important part of the control system where the processed signal is transmitted to the controller that may take corrective action such as opening/closing of a valve or reducing/increasing the speed of a pumping unit. Some disturbances are measured by sensors.. internal model. the MSF system remains to constitute more 55% of the market share. The sensor transmits its signal simultaneously to a readout unit. The process is described in the following points: The feed water is chemically and mechanically treated to minimize deposition of fine particles. selective/override controllers. time-delay compensation. which requires an efficient and accurate control system in the plant to maintain operation at optimum conditions that result in the minimum product cost and prevent scale formation. The review starts with description of both processes and is followed by description of various types of control loops in each system. 1 shows a simple schematic of a control system. adaptive. cascade. Other widely used advanced control techniques include model predictive. Description of the RO process The RO process.. the remaining is distributed among various evaporation systems (Ettouney et al. Alatiqi et al. New advanced control methods include optimal. a computer-based supervisory system can be used to determine the optimum plant condition and coordinate actions of various control units. mud. nonlinear. / Desalination 126 (1999) 15–32 the desalination market through progress and development of permeating membranes with high fluxes. operating at relatively low pressures and capable of withstanding the harsh conditions of the feed seawater and brine. includes membrane modules. 1999b). The last section in this review is dedicated to discussion of process modeling. Regardless. while the RO process represents close to 35%. All of the conventional strategies are well known on an industrial scale and have been used for several decades. During the 1990s.16 I. The correction process can be based on a single or multiple input/output control loop. 1999a). Desalination is a highly complex process. 1998). a processor. This increase is expected to continue during the first two decades in the 21st century (Wangnick. shown in Fig.500 units with a total capacity of 23×106 m3/d.. which . or concentration. feed pumps. PID. 1999b). some configurations have been used for more than 20 years. and advanced controls. Elements of a control system. Reverse osmosis desalination process. the feed pumps increase the water pressure to high values that vary between 5000–8000 kPa. Alatiqi et al.I. and biological matter. fouling materials. The feed treatment process may result in release of non-condensable gas. while most of the dissolved salts are rejected by the membrane and concentrates in the reject brine stream. / Desalination 126 (1999) 15–32 17 Fig. The highpressure feed stream enters the membrane modules where fresh water passes through the membrane. Fig. include calcium and magnesium. 1. 2. which would require subsequent dearation. Although the membrane has very high salt . Subject to feed salinity. without affecting overall operation and. 4. a brine heater. a close signal is sent to the valve. product quality. and permeate pressure. The product water is treated to adjust its pH to neutrality. the system monitors the chlorine content in the feed stream. On the other hand. in particular. The model monitors and controls five system parameters: feed temperature and pH. control loops.18 I. the main objective is to maintain a constant production rate with acceptable purity. The maximum feed pressure is prespecified by the membrane manufacturer and is controlled by measuring the feed pressure at the pump outlet. If the permeate flux drops below the desired set-point. The first multi-loop control system for RO was proposed by Alatiqi et al. which may have harmful effects on the membrane. and is controlled by measuring the pH of the feed and the conductivity of the permeate stream. Alatiqi et al. Energy recovery turbines are used to recover the high-pressure energy of the rejected brine. The last control loop is for the pH of the product stream. and venting connections. The DMC approach has the ability to allow plant operation with various permeate fluxes. It includes one pressure controller and two pH controllers (Fig. The number of stages in the heat rejection section is commonly set at 3. 1998b). Such a system must have a large storage system to meet demand surges and the system design should be based on average demand value to avoid frequent on/off sequence of the membrane modules. permeate conductivity and flux. The brine stream absorbs the latent heat of condensing steam and its temperature . (1996). the number of stages in the heat recovery section varies between 12 and 29. The second control loop is for the feed pressure and is a cascade type where the measured pressure of the feed stream as well as the permeate flux simultaneously adjust the valve located on the reject brine stream. demister. The main features of the process include the following: The brine recycle stream enters the brine heater tubes where a saturated heating steam is condensed on the outside surface of the tubes. which is based on dynamic matrix control (DMC). Each flashing stage includes condenser tubes.. Control loops of the RO process The control loops of the RO process are simple as the process itself. The first control loop is for the feed pH. As in other desalination processes. 4. water boxes. Further development in RO process control is presented by Roberson et al. distillate trays. The system consists of flashing stages. 3. This is necessary to avoid increasing scale formation at high pH or membrane attack at low pH values. (1989). this is necessary to avoid a large increase in the specific heat transfer area (El-Dessouky et al. which results in the increase of the feed pressure and a subsequent increase in the driving force for permeation.. The flashing stages are divided into heat rejection and heat recovery stages. (1989). Mindler (1986) proposed an incremental on/off control system. / Desalination 126 (1999) 15–32 rejection ratios — more than 99% — the fresh water stream may have a salinity that varies between lows of 50–500 ppm. This study utilized the RO process model developed by Alatiqi et al. The membrane manufacturer specifies the operating range for the feed pH. vacuum system. and pumping units. Common design in the Gulf countries adopts the 24-stage system with 21 stages in the heat recovery section (Al-Shuaib et al. 3). 1999). which is simple enough and suits the nature of the RO rate. is a cascade type. Description of the MSF process A schematic diagram for the MSF process is shown in Fig. In addition. . which is necessary to maintain a high-performance ratio for the system. The temperature difference between the TBT and brine recycle temperature is limited to 5 C. 4. increases to its maximum design value known as the top brine temperature (TBT). Alatiqi et al. pressure. Multi-stage flash desalination process.I.or hightemperature operation is associated with the type of antiscalent material where use of poly- phosphate limits the TBT to 90 C and use of a special polymer allows for a higher temperature of 110 C. Such value varies between 90–110 C where the saturation temperature of the heating steam is approximately higher by 10 C. / Desalination 126 (1999) 15–32 19 Fig. 3. C. conductivity. F. flux. P. Fig. Control loops of the reverse osmosis desalination process. Low. The brine recycle stream is extracted from the brine pool of the last stage in the heat rejection section and is introduced into the condenser tubes of the last stage in the heat recovery section. and corrosion. As a result. Vapor formation results in an increase of the brine stream salinity.1. The vapor condenses on the outside surface of the condenser tubes where the brine recycle flows inside the tube from the cold to the hot side of the plant. Simultaneously. Proper design of these units also prevents re-entrainment of the brine droplets within the demister mesh with the vapor flow. which is extracted from the last stage in the heat rejection section. these units control the system characteristics. The demisters’ function to separate entrained brine droplets with the flashed-off vapor. Interstage weirs and submerged orifices control the brine flow rate per unit width of the flashing chamber and strongly affect the flashing rate of distillate product. however. Presence and accumulation of these gases results in corrosion of the condenser tubes and reduction of the overall heat transfer coefficient for the condenser tubes and. reduces the process efficiency and system productivity. The units include the following: 1. which are used to separate the entrained brine droplets. Design features of the MSF process The MSF process includes a number of control loops that operate to maintain high system performance and production rate. On the other hand. Treatment of the intake seawater is limited to simple screening. The warm stream of intake seawater is divided into two parts: the first is the cooling seawater. 4. in turn.000 ppm at the last stage. . In each stage the flashed-off vapors flow through the demister pads. / Desalination 126 (1999) 15–32 The hot brine enters the flashing stages in the heat recovery section and then in the heat rejection section where a small amount of fresh water vapor is formed by brine flashing in each stage. The remaining brine in the last stage of the heat rejection section is rejected to the sea.20 I. In addition. the demister results in low distillate product conductivity or salinity value with the design limits (below 5 ppm). The intake seawater stream is introduced into the condenser tubes of the heat reject section where its temperature is increased to a higher temperature by absorption of the latent heat of the condensing fresh water vapor. A steam jet ejector is used for removal of the non-condensable gases that are released during the flashing process. Current design value of the brine flow rate per unit with of the flashing chamber is 180 kg/m s. treatment of the feed seawater stream is more extensive and it includes dearation and addition of chemicals to control scaling. which is chemically treated and then mixed in the brine pool of the last flashing stage in the heat rejection section. which is rejected back to the sea. 2. This is necessary to maintain low product conductivity and scale formation on the outside surface of the condenser tubes. the stage pressure decreases since it corresponds to saturation temperature of the condensing vapor. and the second is the feed seawater. which is limited to a maximum value of 70. the system includes a number of design features which cannot be defined as a control element. foaming. The flashing process reduces the brine temperature across the stages. The condensed fresh water vapor accumulates outside the condenser tubes and accumulates across the stages and forms the distillate product stream. This energy recovery improves the process efficiency because of the increase in the brine recycle temperature to higher values. Alatiqi et al. 1999c). tube blockage is limited within individual stages. / Desalination 126 (1999) 15–32 21 3. 4. Pressure of the heating steam (controlled) and the opening of the throttling valve (manipulated).. Gate height is adjusted to 0. therefore. the low pressure steam has a pressure of 4–7 bars. As a result. all of the control loops involve a single-input/single-output system. In addition. Also. The controlled and manipulated variables include the following: 1. the temperature of the brine recycle entering the brine heater will decrease. This loop is not found in the oncethrough MSF layout (El-Dessouky et al. the system will reach a new steady state with lower flashing rates and a smaller flow rate of the distillate product. or decrease in the temperature of the brine circulation. Use of heat transfer areas much larger than the design value for clean operation affects the process economics as a result of the increase in the capital cost. The setpoint for the TBT depends on the type of antiscalent used in the plant where 90 C is suitable for a polyphosphate type and between 100–110 C is used for polymer type additives. 4. in the long-tube configuration. Commonly. Heat transfer area of the condenser tube is a major design feature that controls the temperature of the brine recycle entering the brine heater. This loop changes the steam quality from superheated to saturated where its temperature drops from 170 C to 100 or 110 C. tube blockage may result. This might be necessary to take into account an increase in the brine circulation rate. Temperature of the heating steam (controlled) and the flow rate of the condensate spray (manipulated). On the other hand. Although the initial system design provides sufficient heat transfer within various stages. the control valve and pumping system of the control loop operates to adjust the temperature of the intake seawater. An increase or decrease of the heating steam flow rate is necessary to control the TBT. Control loops of the MSF process The main control loops of the MSF process are shown in Fig. TBT (controlled) and heating steam flow rate (manipulated). The steam temperature is controlled to be higher . and it is necessary to reduce its pressure to a value of about 2 bar where it becomes superheated with a temperature close to 170 C. however. Current design of MSF plants sets the value for the specific heat transfer area within a range of 240–260 m2/(kg/s). 2. 3.5 m. fouling and scaling effects. Eventually. 5. readjustment might be necessary to prevent blow-through and control the liquid level with the stages. 4. the stage pressure will increase due to accumulation of the noncondensed vapors. This loop operates only during the winter season when the intake seawater temperature drops to values close to 15 C. which will result in an increase of the amount of the required heating steam and subsequent reduction in the system performance ratio. As is shown. blockage results in loss of the heat transfer area of the blocked tube within all stages in the module. If the heat transfer area is smaller than the thermal load of the condensing vapor. poor operation and increase in fouling resistance will reduce the effective heat transfer coefficient and create conditions where the thermal capacity of the condenser unit is lower than the thermal load of the condensing vapor. This parameter has a strong effect on the system performance ratio. Temperature of the intake seawater entering the last flashing stage (controlled) and the circulation flow rate of the warm cooling seawater (manipulated). Most of the MSF plants in the Gulf area adopt a cross-flow configuration.2. This pressure increase will reduce the amount of flashed vapor.2–0. Gate height between flashing stages prevents vapor blow-through and sets the height of the brine pool. Alatiqi et al. Set-points for the intake seawater temperature vary between 25 C for winter operation and 32 C for summer conditions.I. level. with the units of kg brine blow-down per 1 kg distillate product.22 I. It is necessary to maintain a sufficient static head above the condensate pump to prevent condensate flashing within the discharge line or the pumping unit which would result in violent vibrations.4–0. the intake seawater salinity varies between 42. single-input/single-output control loops of MSF. The set-point for this control is the flow rate ratio of distillate and brine blow down. In the Gulf. T. H. 5. . The flow rate of the feed seawater entering the brine pool of the last stage (controlled) and the valve opening on this line (manipulated). Alatiqi et al. F.314. temperature. 6.000–48. increase in the stage temperature causes reduction in the flashing range and the system performance ratio with simultaneous increase in the brine recycle flow rate. and severe erosion. 5. This loop. For a constant plant capacity.18. which is necessary to keep the salinity of the brine blowdown from the last stage at a design value of 70. The last stage temperature is set at 40 C for summer operation and 32 C for winter operation.000 ppm and accordingly.5–2. 7. The set-point of the controller is made in reference to the temperature of the last flashing stage. the conversion ratio varies between 0. flow rate. / Desalination 126 (1999) 15–32 Fig. 1984). This sets the controller ratio at 1.000 ppm. is consistent with the material balance in direction of flow principle (Stephanopoulos. than the TBT by 5–10 C. P. Condensate level in the brine heater (controlled) and the opening of the distillate discharge valve (manipulated). This is necessary to prevent the formation of hot spots and scale formation. loosening of various connections. like all other level loops in the plant. pressure. This control loop maintains a constant conversion ratio. Temperature of the reject flow rate of the cooling seawater (controlled) and the opening of the discharge valve (manipulated). Brine level in the last flashing stage (controlled) and the brine recycle flow rate (manipulated). the temperature of the brine recycle stream entering the brine heater becomes lower. Selection of the MSF controllers is made subject to the following classification: the controller proportional action works on the setpoint changes and the controller derivative action does not work on set-point changes. Such variations are caused by adjustments in the flow rate of the brine circulation stream which might be necessary to adjust the brine level in the last flashing stage. the flashing efficiency is affected in various stages as well as the amount of distillate product. the intake seawater (Mcw +Mf). 4. On the other hand. Alatiqi et al. the amount of heat released upon condensation of the distillate vapor becomes lower. Brine level in the last flashing stage (controlled) and the brine blow-down flow rate (manipulated). This control loop has a similar function to the level controller of the heating steam condensate where decrease of the distillate product static head would cause operational problems to the associated pumping unit. consequently. 5. reduction of the brine head may result in blow-through of the flashed-off vapors across the stages. Pressure of the ejector motive steam (controlled) and the opening of the throttling valve (manipulated). 13. Increase of the head reduces the flashing rates and the amount of distillate product. / Desalination 126 (1999) 15–32 23 8. Four PID temperature controllers for the TBT (To). This loop reduces the ejector motive steam temperature from 201.4 C to 165 C. Four PI flow controllers for brine recycle stream (Mr). Four PI level controllers for the brine level in the last stage. 3.I. Eventually. Variations in the brine circulation flow rate necessitate adjustment of the dosing rate of the chemical additives. Two PI pressure controllers for the heating steam pressure (Ps) and the ejector motive steam pressure (Pe). heating steam temperature (Ts). As a result. and the ejector condensate level. cooling water temperature (Tn). Simultaneously. and ejector motive steam temperature (Te). 9. the distillate level in the last stage. Spray of steam condensate results in this temperature reduction. Measurements and instrumentation in desalination plants Instrumentation and measurements form an essential part in various control loops in desalination plants where measured signals are transmitted to the control system and compared against the desired set-point. the heating steam condensate in the brine heater. This is one of the most important control loops in the MSF plant since the head in the last stage adjusts the head in previous stages. the medium pressure steam has a pressure of 16 bars and it is necessary to reduce its value to 7 bars. Subsequently. Distillate level in the last stage (controlled) and the distillate product flow rate (manipulated). The controllers in the MSF system include the following: 1. corrective action in various manipulated parameters takes place to adjust the controlled . Commonly. 12. This control loop has a similar function and effects on the system performance as the previous control loop (number 9). 1994). 2. the effective number of stages becomes smaller and the plant performance ratio decreases. Temperature of the ejector motive steam (controlled) and the flow rate of the condensate spray (manipulated). 10. Dosing flow rate of the chemical additives (controlled) and the opening of the discharge valve of the chemical additive (manipulated). feed seawater (Mf). which results in the increase in the amount of heating steam and reduction in the system thermal performance ratio. This is necessary to obtain a fast response and to limit system disturbances (Al-Saie and Hafez. and distillate flow rate (Md). 11. and pH measurements are made on-line. maintaining measuring instrumentation in good working condition requires frequent cleaning. some measuring instruments are simple and inexpensive to use. which include TBT. all of the measuring instrumentation may face problems related to fouling. brine level in the first stage. and the corrosive nature of the seawater. i.e. i. product flow rate. significant effects of small deviations from design conditions on plant operation. and non-optimal utilization of resources and energy. model predictive control (MPC). are made off-line in central analytical laboratories following standard methods. In various locations around the plant. vapor condensation. i. and pressure of the heating steam.e. seawater temperature. Results are reported in terms of variations of major system parameters. top brine temperature. / Desalination 126 (1999) 15–32 variable to the desired value.e. Control loops for the TBT. Aly and Marwan (1995). effects of power plant output conditions on the desalination plant. Process control and models of the MSF system Dynamic modeling of the MSF process dates back to the 1960s with the work of Delene and Ball (1960). and temperature and flow rates of intake and feed seawater. As will be discussed later. product demand. Some instrumentation is tied with a control loop. in all plants the TBT is tied with an alarm system. and brine. poor mixing. TBT. However. The authors proposed use of an internal model control (IMC). As indicated. and water temperature variations. seawater inlet temperature. lack of robust stability. detailed water analysis. pH and conductivity probes. multiple sensors might be used to obtain an average value or ensure proper measurements of sensitive parameters. brine level in the last stage. Chidambram and Al-Gobaisi (1995) presented . distillate. Other alarm units are used for the distillate flow rate. Examples include the models presented by Rimawi et al. (1994). slow dynamics. and calibration. level. this may vary from one plant to another. Lior (1996) outlined in detail the main measuring instruments in MSF plants. 1994). all of the temperature. dosing ratio of chemical additives. or pressure and temperature of heating steam. All measuring instruments are connected with indicators and alarms commonly located in the central operation and control room (Al-Gobaisi. while the measured data are taken at equal intervals and logged on a data acquisition system. they proposed the use of modern control techniques that can take into account changing conditions of steam supply. Alatiqi et al. and development of advanced control strategies. a major part of the instrumentation is delicate and requires frequent calibration and replacement of defective parts. distillate level in the last stage. these models are essential for a better understanding of the MSF dynamics.e. Hussain et al. Also. flow rate. and Alhumaizi (1997). development of control systems. Al-Gobaisi et al. robust control. and brine level in the last stage are proposed to form the adaptive control system. i. All studies show transient behavior of the system as a function of disturbances in the stream temperatures and flow rates. and adaptive control. brine level in the last stage.. A small number of the instruments is coupled with an alarm system. servicing. (1993a) outlined the drawbacks of PID controllers with their inability to suppress disturbances. Other data.e. As is shown. Features of the MSF plant include variable capacity. (1989). Instead. instability due to disturbances in steam supply. i. However. differential manometers. pressure..... dead-time characteristics due to certain load changes. and brine level. Accordingly. brine level in the last stage. A summary for these instruments is shown in Table 1. controller tuning. Falcetta and Sciubba (1998).24 I. 6. feed seawater temperature. El-Nashar (1998) proposed the use of a supervisory control system that includes an integrated mathematical and economical model. Simple transfer functions without any delay are used to perform analysis. The plant performance was examined upon introduction of a step change in the controllers set-points during load changes. The PID controller is tuned by using integral square error (ISE). level. Analysis shows superiority of the advanced control system in driving the plant to a steady state that can provide a better performance ratio. Low-pressure steam temperature (controlled) and condensate flow rate to spray system (manipulated). (1994) simulated a large MSF plant containing 15 recovery and three rejection stages. As for the manipulated variables. the brine recycle flow rate. the modified Ziegler-Nichols controller tuning for first-order plus time delay is better than the Ziegler-Nichols method. Intake seawater flow rate (controlled) and cooling seawater flow rate (manipulated). IAE. The control used in their simulation includes a PID for the TBT and PI for the recycle flow rate. The model is compared against the data for MSF plants in Jubail. they include the steam flow rate. and temperature of the brine blow-down. The model used a speed-up flow sheet and contained 153 state variables. (1994) and the relative gain analysis (RGA) was used to determine the best pairings of the controlled and manipulated variables. El-Saie and Hafez (1994) used conventional PID controllers tuned to their minimum integral absolute error (IAE) to regulate disturbances in MSF plants. Feed flow rate in the last flashing stage (controlled) and brine level in the last stage (manipulated). last stage brine level. Maniar and Deshpande (1996) analyzed control of a MSF plant using a constrained model predictive control (CMPC) technique. Saudi Arabia. The analysis includes tuning of flow. Woldai et al. and seawater flow rate. brine and distillate levels in the last stage. product flow rate. Temperature of flashing brine in last stage (controlled) and brine circulation flow rate (manipulated). They used the dynamic model by Hussain et al. feed seawater flow rate. The controlled variables include the TBT. Analysis showed that fixed controllers are not suitable for such desalination plant whose operating conditions are subject to changes. (1995) simulated an 18-stage MSF plant. The model frequently reviews the plant operating conditions and obtains a new optimum for the operating cost . Distillate product flow rate (controlled) and steam flow rate to brine heater (manipulated). The proposed system was applied to an existing 6 migd plant. including: TBT (controlled) and brine circulation flow rate (manipulated). and brine heater condensate level. and Zhuang and Atherton (ZA). Also. The dynamic model by Hussain et al. The main control loops considered are the TBT.I. integral time absolute error (ITAE). The study included evaluation of a PID controller for the TBT and its effect on the plant performance. Their analysis focused on system performance as a function various system parameters. Alhumaizi (1997) developed a non-linear model predictive control for the MSF process. Results show that the pole placement method is superior to the synthesis method. / Desalination 126 (1999) 15–32 25 a sequence of controller tuning for MSF plants. and six outputs. and the cooling seawater flow rate. and temperature controls. Alatiqi et al. The authors proposed to use an optimal conventional PID controller based on the standard first-order plus dead time (FODT) approximation. intake seawater temperature. six inputs. The proposed system is found to satisfactorily regulate the disturbances with minimum controller tuning. Results show that the brine level in the flashing stages is strongly affected by the steam temperature and flow rate. function. and internal temperature compensation Visual indicators. reliable . Tap fouling and blockage gives partial large measure readings of pressure and large errors. Immersion of the sensor in a condensate cup open to the vapor space or use of wicked waterconducting sleeve. Avoid installation at bottoms of pipes and vessels. measurement may alter due to presence of gradients or heat losses. pipes.Non-condensable gases may generate large errors. Use of differential thermocouples which produces a signal directly proportional to the temperature difference. 2. 2. and cables in presence of high temperature seawater and brine. inexpensive. This optimum defines the new Table 1 Measuring instruments used in the desalination industry Measured parameter Temperature Instrument Thermocouples Resistance temperature detector Feature Cheap More stable with higher sensitivity Technical difficulties 1. 3. High corrosion rates of sensors. 4. where condensable gas concentration is high. Improvement 1. span. Use four equally spaced taps along the circumference to measure the average pressure. Two phase temperature measurements yield a high error due to vapor condensation and presence of non-condensable gases. 4. 4. Measuring error can be in the same range as the temperature difference between two stages. Pressure taps should not be placed in cold zones or high locations. 3. 3. 2. Small temperature differences. Pressure U-tube differential manometers Force balance differential transducers 1. 2. Use gravity drainage to condensate pots to ensure similar height of the condensate in the tubing. 1. 3. wires. Vapor condensation in the tubing leading to the sensing sides of the transducers. Pressure variations along the circumference of High reliability. 4. Place sensors in well mixed zones. corrosion proof cables. Use of corrosion-resistant thermometer wells. Temperature non-uniformity. Samples are analyzed in analytical laboratories 1. . 3. and orifices Magnetic flow meters Suitable for non. brine recycle and blow-down Expensive and delicate. Accumulation of dirt in the liquid leg may generate erroneous errors.Flow Rotameters. Perform frequent calibration and replace defective parts. 1. replacement of defective parts. 2. Table 1. expansion. venturi. 3. and installation in good flow areas. Vapor/gas leg is maintained full with dearated water. Calibration problems. span. 1. Protect detector and perform frequent cleaning. 3. Fouling and scaling may hinders motion of saturation or use magnetic measuring wide measuring moving parts and block small openings. SO2. Liquid leg is sloped downward to prevent accumulation of fouling and dirt. industry standards. 2. Use orifices or large venturi. 2. and rare gases Not used in the desalination industry Measure pH of product. 1. Improvement 1. Follow manufacturer’s recommendations. Install in regions far from thermal High accuracy. Oscillation effects due to violent flashing effects or turbulence. flashing liquid 2. used on a wide scale Technical difficulties 1. continued Measured parameter Level Instrument Differential pressure between liquid bottom and vapor/gas space Ultrasonic Feature Simple. pH pH probes Gas Concentration O2 and CO2 detectors H2S. costly 3. 2. 2. Fouling problems. Average readings over a longer period of time. flow meters with moving parts. Frequent calibration.1. Fouling problems around sensors. Errors caused by bends. and contraction. 2. devices. Flashing effects gives erroneous errors for and perform device calibration. Frequent cleaning. Poor reading due the delicate nature of the sensor or bad mixing. Filling of the vapor/gas leg with condensate may result in large errors. inexpensive. 2. 1. fluoride. / Desalination 126 (1999) 15–32 Conductivity meters Ion specific detectors Heavy metals. Poor mixing and dead zones. 2. Protect sensor from fouling. organics. Alatiqi et al. 3. pH Measure total salt content 1. 1. Delicate nature of sensors.28 Water composition I. Samples 3. 2. Frequent cleaning and installation in good mixing regions and far from bottoms of vessels and tubes. Perform frequent calibration. analyzed using standard methods . Locate in good flowing areas and avoid high turbulence or violent flashing regions. cyanides. The process model is a steady-state model that is based on a fundamentally sound physical model that takes into consideration material and energy balances. and as discussed in the previous sections. However. The system achieves full automa-tion for the large plant and is recommended by authors. Other simple relations can be written for flow rates and stream salinity as well as for the heat transfer area and thermal load. and in the heat transfer coefficient as a function of the system geometry and transport and physical properties of the stream. However. although in the desalination industry several system para-meters are simple to model and obtain a well defined relation. As a result. Several preliminary studies can be found in the literature on the application of the fuzzy control logic to the MSF process. The measured parameters are grouped and classified into a predefined set of categories. minimizing its salinity. or high rates of scale. distillate flashing. Results show that operation of the plant at conditions other than the optimum for the TBT causes an increase of the unit product cost by 7%. and maximizing the plant . maximizing the product. the model by ElDessouky et al. the cost for fresh water and electricity is minimized and the demand for both products is satisfied. simultaneous measurements of high evaporator temperature and low product flow rate may imply high rate of scale formation. and Darwish (1991) developed steady-state mathematics for the MSF process. scale formation. The analysis results are then used to trigger the corrective action. a fully automatic analogue back-up system is still necessary in case of computer failure. Omar (1983). Optimization of the integrated power and desalination plant where optimization takes into consideration variations in demand for electric power and fresh water. The fuzzy logic was originally developed to provide a control system for a complex system where it is difficult to formulate a predictive system model. which may include low. Fariegh and Arazzini (1985) described and analyzed a supervisory control and data acquisition system (SCADA) for large-scale MSF plants. Helal (1986). A supervisory and fuzzy logic control. (1995) has several additional features from previous models found in the literature. 12 migd. The heart of the control system is the mathematical model that evaluates the fouling factors and stage efficiency as well as the heat balance equations. which is based on simple measurements of system parameters and is taken as a measure for another parameter that is more difficult to model or measure. Even relatively simple models can be found in literature on estimation of scale formation rates. pressure drop in the demister and during condensation. heat transfer. the full and complete mathematical model for the MSF is rather complex and may require an iterative solution procedure. for more fine tuning of the system control. Alatiqi et al. The measured parameters may include temperature. remains to be adopted on full commercial scale in the desalination industry.e. However. pressure drop. The following is a summary of these studies: 1. 2. Of course. i. medium. Reduction in fuel consumption. For example. For example. dependence of physical properties on the temperature and salinity. thermodynamic losses. and various correlations for the heat transfer coefficient and physical properties. which may involve injection of an acid treatment system or start of a sponge ball cleaning cycle. Analysis by El-Nashar is applied to an existing MSF plant in UAE. fouling and wall resistance. simple energy balance can describe the relation between the TBT and the heating steam temperature. Such features take into consideration the effect of the non-condensable gases. / Desalination 126 (1999) 15–32 29 set-points. This motivates the fuzzy logic approach. the number of categories can be increased.I.. or flow rate. although developed more than three decades ago. Campbell (1995) presented attempts to develop fuzzy control systems for the desali- nation industry. As a result. desuperheater pressure. The proposed system replaces expert operators and linear controllers with the fuzzy logic control system. These factors are intimately related for having an efficient and accurate control system. The system employs several microprocessor-controlled subsystems and various pretreatment and scale removal technologies that allow the system to operate autonomously for extended periods. which includes the following: dedicated sequential and regulatory controls. A scale control protocol was developed based on time and pH. Al-Gobaisi et al. Raymond et al. The fuzzy control system provides various actions which include no change. steam by-pass control. and concentration ratio. Akbarzadeh-T. The results include performance comparison of the . liquid level. (1999d) where a comprehensive program was proposed for the qualifying of manpower in the desalination industry.30 I. compressors. The author concludes that further development and testing of the rules is necessary to design and construct an efficient and accurate fuzzy controller. (1995) applied the genetic algorithm to the fuzzy logic control system. 4. 3. integrated control. Sarkodi-Gyan et al. the adoption of the fuzzy control reduces the number of rules to operate the control system in a multi-variable environment. Jamshidi (1995) applied the fuzzy logic and fuzzy control to a simulation step change in the TBT. The proposed system shows an 80% reduction in the overshoot. and the set-point change in steam pressure. As is shown. The system constitutes an on-line dialogue that determines variations in the mass flow rates. valves. brine recycle flow rate. 6. which include the TBT. et al. (1995) described a supervisory fuzzy control system for MSF plants. increase/ decrease output flow. which include the parameters of the sequential and regulatory controls as well as the control loops for the combined-cycle power plant. and simultaneous increase/ decrease of input/output flows. An overview is presented for the fuzzy logic control with some examples for control of various parameters in the desalination plants. (1986) proposed a three-level control system. The discussion includes functional and hierarchical structures of control systems. management information control. Keyes et al. Alatiqi et al. distributed control. 8. steam pressure. and manpower training. / Desalination 126 (1999) 15–32 factor all contribute to reduction of the product unit cost. temperature and flow rate of feed seawater. supervisory and optimizing controls. and production rate. (1995) analyzed performance of fuzzy controllers for handling a multiinput/multi-output fuzzy controller. (1993b) discussed various automation concepts applied to power and desalination plants. acid slug is injected into the feed water to purge the soft scale within the heat exchanger and the evaporator. gas turbine firing rate controls. 5. 7. The system adopts low-cost sensors and allows for rapid development cycles and tolerates high levels of signal noise. and steam turbine hot well level. The system is triggered by a significant decline in product production or certain temperature differentials with the evaporator/condenser. fault tolerance. The later topic was discussed in a separate study by El-Dessouky et al. Analysis for the response time in the TBT shows significant improvement for the proposed system in reduction of oscillations and convergence to the desired set-point. A similar approach is proposed for control pumps. The system is a decision aid to the operator of MSF plants. The system performs exhaustive examinations where it derives auxiliary control regimes dependent on the process situations. man/machine interface. increase/decrease of input flow. flow rate of circulating brine. presence of oscillations. product quality.I. 10. Krause and Hassan (1996) discussed the performance of MSF plants using state. Alatiqi et al. and distillate necessitate protection of . The corrosive and fouling nature of the seawater. signal transmission. level 3 is production control. It concludes that further studies are necessary to obtain the best conditions for the fuzzy logic controller that gives optimum operation. The analysis evaluates the use of optimization routines for a single MSF unit or an integral plant. or self-setting PID controllers. Ismail (1998b) examined the performance of a learning fuzzy control system for the TBT against conventional PID and direct fuzzy logic. artificial intelligence). and emergency operation. condensers. level 2 is the cell or group control where some supervisory and coordinating function. the MSF system contains more than 12 single input/single output control loops. Neither fuzzy controller exhibits oscillatory behavior. specifically. indicators. level 4 is plant manage-ment. disturbance rejection. Most of the literature studies show the need for further mathematical analysis and evaluation of an integrated fuzzy control system. The existing studies are limited to few system parameters. Conclusions A review is presented for the control loops and instrumentation used in the two major desalination industries. The following conclusions are made in light of the above review: Current practice adopts the classical control system. coordination of production processes. 7. safety is improved. decoupling of systems. which includes PID and PI controllers. adaptive. and settling time. employment of knowledge sources (databases. It is shown that use of software packages for early detection of faults and immediate initiation of counter measures is necessary to avoid unplanned shut-downs. Performance indicators are obtained for pumps. optimization of the static and dynamic plant behavior. MSF and RO. On the other hand. The major part of the control systems is based on single input/single output configuration. supervisory control: guidance according to a fixed program. the TBT in MSF. product cost is reduced. preheaters. 11. turbines. However and as discussed before. Results show advantages of the learning system over the other systems. a supervisory control system would include several levels where level 0 represents the plant and actuators. 9. and level 5 is corporate management. cost functions. In this regard. / Desalination 126 (1999) 15–32 31 fuzzy logic controller and PID controller with emphasis on the magnitude of the over shoot. Subsequently. stabilization: tracking of reference values. the PID gives an initial large rise followed by oscillation that damps after a longer period than the other control systems. brine. Instrumentation. level 1 is process control. the direct fuzzy controller adjusts to the desired set-point after a longer period than the learning fuzzy controller does. data logging. and hardware availability is increased. The author stresses the fact that the fuzzy logic control is not a replacement for conventional control systems but instead is an extension of available tools for control engineering. Unbehauen (1995) outlined various elements of the automation functions in continuous or batch production processes: supervision and safety considerations: visualization. and alarms form an integral part of the control system. and compressors. The above literature review shows that the fuzzy control approach remains in the development stage in the desalination industry. and economic profit. Al-Gobaisi.E. El-Saie. D. Advanced Control. and Al-Roumi. El-Dessouky. S. H.H. F. 97 (1994) 529. El-Dessouky.. Falcetta.and .T. E.G. Workshop on Desalination Technologies for Small. 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