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Desalination 304 (2012) 20 24 Contents lists available at SciVerse ScienceDirect Desalination journal homepage: www.elsevier.com/locate/desal Validation of model-based optimization of brackish water reverse osmosis (BWRO) plant operation Mingheng Li a,, Brian Noh a,b a Department of Chemical and Materials Engineering, California State Polytechnic University, Pomona, CA 91768, USA b Inland Empire Utility Agency, 6075 Kimball Ave., Chino, CA 91708, USA HIGHLIGHTS Previously published model-based optimization strategies were validated in an industrial RO plant. A 10% reduction in pump energy consumption was achieved when recovery increases from 80% to 90%. The saving in disposal cost is more than the one in pump energy consumption when recovery increases from 80% to 90%. article info abstract Article history: Received 19 May 2012 Received in revised form 20 July 2012 Accepted 23 July 2012 Available online 19 August 2012 Keywords: Reverse osmosis Brackish water Desalination Specific energy consumption Disposal cost Plant operation A previously developed model-based optimization methodology is validated in an industrial brackish water reverse osmosis (BWRO) desalination plant in Southern California. A close match between s and in numerous cases indicates that the mathematical model accurately describes the main characteristics involved in typical industrial BWRO processes. It is demonstrated in this desalination plant that a 10% reduction in energy consumption can be achieved while maintaining the same permeate flow rate by adjusting operating conditions as suggested by the model. Furthermore, an opportunity for significant savings in disposal cost is identified due to a reduction in brine volume. This work clearly shows the effectiveness of model-based optimization in RO plant operation. 2012 Elsevier B.V. All rights reserved. 1. Introduction Energy consumption is widely acknowledged as an important issue in RO water desalination technology [1,2]. In recent years, model-based analysis and optimization have shed insights into the design and operation of RO desalination. For example, it has been shown that under certain conditions, operating RO near its thermodynamic limit reduces the specific energy consumption (SEC) [3]. Using mathematical models, it is possible to account for RO configurations and operating conditions in the optimization framework [4 12]. Model-based control has also been developed and applied to a pilot-scale RO system to reduce SEC [13,14]. In a series of papers [15 18], the author provided a comprehensive analysis of single- and multi-stage RO with/without energy recovery device (ERD) from first-principles as well as model-based optimization strategies for industrial RO processes. Based on Darcy's law for mass transfer and negligible pressure drop in retentate stream, the author derived an important dimensionless parameter γ=al p Δπ 0 /Q f (where A is Corresponding author. Tel.: +1 909 869 3668; fax: +1 909 869 6920. E-mail address: minghengli@csupomona.edu (M. Li). themembranearea,l p is the membrane hydraulic permeability, Δπ 0 is osmotic pressure in the feed, and Q f is the feed rate) as an indication of the operation regime as well as the best achievable SEC. The minimization of SEC was formulated as a nonlinear optimization problem and the optimal results were obtained as a function of γ for single- or multi-stage ROs with/without an ERD [15,16]. Later on, the author developed a two-parameter mathematical model that explicitly accounts for pressure drop in the retentate [17,18]: dqðþ x dx dðδpðþ xþ dx Qx ΔP x ¼ A L p ΔP Q f Q Δπ 0 ¼ k Q 2 ðþ ¼ Q f @x ¼ 0 ðþ ¼ P 0 þ ΔP pump P p @x ¼ 0 where dq is the flow rate of water across the membrane of area da (da=a dx and x is a dimensionless number. For example, 0 1 represents the first stage, 1 2 represents the second stage, and so on). Q is the retentate flow rate. P 0 is the feed pressure before the pump. ΔP is the pressure difference across the membrane. ΔP pump is the pressure ð1þ 0011-9164/$ see front matter 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.desal.2012.07.029

M. Li, B. Noh / Desalination 304 (2012) 20 24 21 increase across the pump. P p is the permeate pressure. k is a coefficient correlating the pressure drop as a function of the retentate flow rate. A is available from vendor data sheet. Q f, P 0, ΔP pump,andp p are based on plant measurements. L p and k can be derived from normal plant operation results [17]. An integration of this model would provide profiles of Q and ΔP along the RO train. Based on Eq. (1), the author formulated and solved a nonlinear optimization problem in order to reduce SEC while maintaining the same permeate production in an industrial RO train in Chino Desalter I located in Chino, California [17]. It was predicted that the SEC in the RO train of interest can be gradually reduced by increasing recovery up to 93 94% from the normal production set point of 80%. The present work is to provide validation of model-based optimization results using recently collected from this facility. 2. Plant description The Chino Desalter I consists of three major treatment processes (volatile organic compound stripping, ion exchange, and reverse osmosis) with the combined capacity of 14 million gallons per day (MGD), or 5.3 10 4 m 3 /day. The treated water from all three processes are blended in a clearwell which is equipped with on-line monitoring instruments for ph, chlorine residual, conductivity, and nitrate. Typical power demand for the entire plant is 1.7 MW, of which 0.5 MW is for the operation of the RO units, accounting for roughly 30% of the total power consumption. 14 different wellhead facilities convey the raw ground water to the treatment plant at approximately 40 psig (or 2.7 barg) of pressure. The groundwater has an average total dissolved solids (TDS) concentration of 950 mg/l and nitrate concentration of 170 mg/l. Pretreatment chemicals are added to condition the raw water before being fed to the RO membranes. The sulfuric acid is injected at the upstream of the pretreatment filters and the RO membranes to prevent the precipitation of calcium carbonate within the RO system. The sulfuric acid treatment reduces the ph of the feed water to approximately 6.5 and increases the solubility of calcium carbonate which is a key precipitate in Chino Desalter I. The threshold and silica inhibitor system adds proprietary chemicals to the RO feed water to inhibit the precipitation of soluble salts rejected by the RO process membrane. Five cartridge filter housings are situated in the treatment system immediately downstream of the sulfuric acid and threshold inhibitor injection points. The cartridge filters equipped with 1 μm filter remove suspended solids or debris that may be present in the feedwater, thereby giving protection to the high pressure RO feed pumps and RO membranes. Removal of the suspended solids is desirable, as they may cause premature fouling of the RO membrane elements and increase the resistance to water flow through the membrane. If abrasive, the suspended solids could damage the high-pressure RO feed pumps or the pressure vessels and membrane elements. Located downstream of the pretreatment chemicals, the cartridge filters provide additional mixing of these chemicals. A single differential pressure transmitter reads the common pressure drop across all five cartridge filter housings. The filtered feedwater is analyzed for turbidity, ph, and conductivity upstream of the RO feed pumps. The RO system consists of four parallel process streams (see Fig. 1). Each RO train has a dedicated feed pump to elevate the feed pressure to maintain the sufficient driving force across the membrane. The RO system is controlled by a programmable logic controller (PLC). Automated control sequences are programmed into the RO PLC for start-up, shut down, flush and normal operation of each RO train. Based on the operator selected set points of permeate flow and water recovery, the PLC adjusts the speed of the RO feed pump and modulate the position of the retentate control valve [17]. The plant is currently operated at a permeate flow of 1235 gpm (or 280 m 3 /h) and a recovery about 80% in each train in order to meet the production need and brine discharge requirement [17,18]. Each RO train consists of 42 pressure vessels in a 28:14 two-stage array with no inter-stage booster pumps. The reject from the last element is routed from the pressure vessel to the high-pressure reject manifold and fed to the second stage pressure vessels for additional RO treatment. The retentate from the second stage, or brine, is considered a non-reclaimable industrial waste and is properly discharged in accordance with the local ordinances. The disposal cost is based on connection, capacity, volume and the mass rate of biochemical oxygen demand (BOD) and total suspended solid (TSS). The brine discharge ultimately ends up at an extremely high capacity coastal wastewater treatment plant with tertiary treatment capacity [19]. 3. Results and discussion Fig. 1. RO trains in Chino Desalter I. Numerous experiments were performed on the first RO train of Chino Desalter I by maintaining the permeate production rate at 1235 gpm (or 280 m 3 /h) while varying the recovery from 79 95%. In an effort to minimize possible interruptions in plant production, the experiments were conducted over three days in February and March 2012. The recovery was changed in small 1 2% increments. When a set point change in the recovery was placed, sufficient time was allowed for the system to stabilize. The motor speed, retentate flow rate, retentate valve position, conductance and other relevant data were recorded from the control system (see Fig. 2 for a snapshot of the control screen). The recovery was verified based on permeate and retentate flow rates. It was noticed that the control system overwrites any recovery that is greater than 90%, probably due to some safety interlocks built into the programming. The actual plant water recovery varies from 79% to 90%. As shown in Fig. 3, the control system is very effective and maintains the water flow rate around the same set point in all cases. This serves as a good basis for the comparison between s and experimental data. Figs. 4 and 5 show the pressure differences across the membrane at both the entrance and exit of the RO train. It is predicted by the model that when the recovery is between 80 90%, ΔP at the inlet of the RO unit is roughly constant while ΔP at the outlet of the RO unit increases linearly with the recovery. Both match closely with plant observations. The indicate that ΔP at the inlet of the RO unit varies about 1% while ΔP at the outlet of the RO unit about 15% in the recovery range 79 90%. The high ΔP at the outlet of the RO unit, as pointed out in the author's theoretical study, is achieved by closing the retentate valve [17]. This is consistent with the plant data shown in Fig. 6, which indicates that the retentate valve position gradually reduces from 58% to 27% when the recovery increases from 79% to 90%.

22 M. Li, B. Noh / Desalination 304 (2012) 20 24 Fig. 2. A snapshot of the RO monitoring and control interface. Because ΔP at the inlet of the RO unit does not change much in the recovery range of 79 90% and P p and P 0 are fairly constant, the pump head should also be roughly constant. This is shown in Fig. 7. A reduced flow with a roughly constant pump head implies a higher flow resistance, which is consistent with the valve throttling effect shown in Fig. 6. The pump energy consumption normalized by the one under normal production conditions in both s and plant experiments are shown in Fig. 8. In the mathematical model, the motor horsepower is calculated using W=Q f ΔP pump /η pump /η motor, where η pump is pump efficiency determined from the vendor-provided pump characteristic map [20] and η motor is motor efficiency assumed to be constant in the author's previous work [17]. In the plant, the power consumption is based on voltage and current readings. It is seen that the general trend predicted by the model matches with plant observation, i.e., energy consumption can be saved by gradually increasing recovery from 79% to 90%. However, a deviation is noticed at high recoveries. In fact, suggest that η pump η motor is fairly constant. This term can be calculated based on the ratio of hydraulic power (the product of feed flow rate and pressure increase across pump) to actual power consumption (the product of current and voltage readings). η pump η motor normalized by its value at normal production conditions is shown in Fig. 9. The variation is less than 1% in almost all cases. If η pump η motor is assumed to be constant at all recoveries, a closer match between s and plant trials can be obtained, as shown 360 350 16.5 16 Feed rate (m 3 /hr) 340 330 320 310 ΔP feed (bar) 15.5 15 14.5 14 13.5 13 300 12.5 290 12 Fig. 3. Feed rate as a function of recovery. Fig. 4. ΔP at the inlet of the RO unit as a function of recovery.

M. Li, B. Noh / Desalination 304 (2012) 20 24 23 16 15 150 145 140 ΔP brine (bar) 14 13 12 11 Pump head (m) 135 130 125 120 115 10 110 9 Fig. 5. ΔP at the exit of the RO unit as a function of recovery. 105 Fig. 7. Pump head as a function of recovery. in Fig. 10. Plant engineers disclosed that there have been several pump maintenance events since the desalter was first put into service. It is not clear if these could make the pump efficiency behave slightly different from the characteristic curve originally provided by the vendor 13 years ago [20]. Another possible reason is that VFD/motor efficiency might reduce slightly when the load reduces [21], which cancels the effect of increased η pump. The author's previous work [17] also provides pump speed calculation using pump affinity laws. A comparison between s and plant observations is shown in Fig. 11. They match very well, and both indicate that the pump speed reduces when recovery increases from 80% to 90%. This trend is also consistent with the energy consumption shown in Fig. 10. It was predicted by model-based optimization that the SEC in this train reaches its minimum at a recovery around 93 94%, beyond which the SEC will increase [17]. Even though the lowest SEC cannot be demonstrated experimentally due to current controller settings, it is reasonable to conclude that it occurs between 90 100%. This is because the retentate osmotic pressure will skyrocket when the recovery approaches 100%, so will the pump head and motor horsepower. A brief economic analysis of implementing the validated model-based optimization results is shown as follows. A change of the recovery set point from 80% to 90% in all four trains in this plant would lead to an annual saving of $35,000 in electricity consumption Retentate valve position (%) 60 55 50 45 40 35 30 25 0.78 0.8 0.82 0.84 0.86 0.88 0.9 0.92 Fig. 6. Retentate valve position as a function of recovery. based on $0.08/kWh. There are additional savings for pumping less water from the wells to the desalter plant. Moreover, operating at a high recovery could result in a reduction in brine volume, which consequently leads to a reduction in disposal cost. For this particular industrial desalination plant, the disposal cost is based on both the volume of brine and the mass rate of BOD and TSS. A change of recovery from 80% to 90% would reduce the brine volume by 1 MGD (or 3.8 10 3 m 3 /day), or more than 50%. The annual saving due to the reduced brine volume is $316,000 based on the current brine volume charge rate of $891/MG. Furthermore, the mass rate of BOD and TSS may decrease proportionally with reduced intake flow, leading to an additional reduction in disposal cost. It is advised by plant engineers that membrane might prematurely foul if it is operated at very high recoveries, possibly due to solubility limits of several sparingly soluble salts in the feedwater and high osmotic pressure of the retentate stream. Statistical data over 10 years in this plant indicate that each membrane element produces about 10.5 MG before approaching the end of its lifetime. The average annual cost of membrane replacement is $127,000 based on $520/element. It is still economically feasible even if the membrane lifetime is reduced by a half. In practice, it is recommended to make small changes so that operating cost is reduced without substantially subsidizing membrane lifetime. There is a research need to elucidate the membrane degradation Normalized pump energy consumption 1.05 1 0.95 0.9 0.85 0.8 Fig. 8. Normalized pump energy consumption as a function of recovery. η pump is based on a vendor-provided pump characteristic map. η motor is assumed to be constant.

24 M. Li, B. Noh / Desalination 304 (2012) 20 24 1.2 1.15 88 87 Normalized η pump η motor 1.1 1.05 1 0.95 0.9 0.85 mechanism. If a relationship between recovery and membrane lifetime can be established, it is possible to include both capital investment and operating cost in the optimization framework to identify the best design and operation strategy for BWRO desalination. 4. Conclusions 0.8 0.78 0.8 0.82 0.84 0.86 0.88 0.9 0.92 Fig. 9. Normalized η pump η motor calculated from. The mathematical model developed in the author's work [17] predicts the process behaviors of an industrial BWRO train very well, including pump head, pressures at various locations and pump speed at various recoveries and a constant permeate flow rate of 1235 gpm. If assuming constant η pump η motor, the model-predicted energy reductions closely match the plant observations. A 10% reduction in energy consumption is demonstrated in this desalination plant by implementing strategies suggested by the model, which clearly shows the effectiveness of model-based optimization in RO plant operation. It is shown that an opportunity for even greater savings exists in disposal cost due to reduced brine volume. Due to the lack of quantitative relationship between membrane lifetime and recovery, it is suggested to gradually increase the recovery set point when implementing the results in this work. Normalized pump energy consumption 1.2 1.1 1 0.9 0.8 Fig. 10. Normalized pump energy consumption as a function of recovery assuming constant η pump η motor in the model. Pump speed (%) 86 85 84 83 82 81 Acknowledgement The authors would like to thank Moustafa Aly from Inland Empire Utility Agency for assistance in plant experiments. References 80 Fig. 11. Pump speed as a function of recovery. [1] R. Semiat, Energy issues in desalination processes, Environ. Sci. Technol. 42 (2008) 8193 8201. [2] M. Elimelech, W.A. Phillip, The future of seawater desalination: energy, technology, and the environment, Science 333 (2011) 712 717. [3] L. Song, J.Y. Hu, S.L. Ong, W.J. Ng, M. Elimelech, M. Wilf, Emergence of thermodynamic restriction and its implications for full-scale reverse osmosis processes, Desalination 155 (2003) 213 228. [4] M. Zhu, M.M. El-Halwagi, M. Al-Ahmad, Optimal design and scheduling of flexible reverse osmosis networks, J. Membr. Sci. 129 (1997) 161 174. [5] F. Vince, F. Marechal, E. Aoustin, P. Breant, Multi-objective optimization of RO desalination plants, Desalination 222 (2008) 96 118. [6] I.J. Esfahani, A. Ataei, M. Kim, O. Kang, C. Yoo, Parametric analysis and optimization of combined gas turbine and reverse osmosis system using refrigeration cycle, Desalin. Water Treat. 43 (2012) 149 158. [7] H. Oh, T. Hwang, S. Lee, A simplified simulation model of RO systems for seawater desalination, Desalination 238 (2009) 128 139. [8] M. Skiborowski, A. Mhamdi, K. Kraemer, W. Marquardt, Model-based structural optimization of seawater desalination plants, Desalination 292 (2012) 30 44. [9] A. Altaee, Computational model for estimating reverse osmosis system design and performance: part-one binary feed solution, Desalination 291 (2012) 101 105. [10] A. Yechiel, Y. Shevah, Optimization of energy costs for SWRO desalination plants, Desalin. Water Treat. 46 (2012) 304 311. [11] A. Zhu, P.D. Christofides, Y. Cohen, Effect of thermodynamic restriction on energy cost optimization of RO membrane water desalination, Ind. Eng. Chem. Res. 48 (2009) 6010 6021. [12] A. Zhu, P.D. Christofides, Y. Cohen, On RO membrane and energy costs and associated incentives for future enhancements of membrane permeability, J. Membr. Sci. 344 (2009) 1 5. [13] A. Bartman, P.D. Christofides, Y. Cohen, Nonlinear model-based control of an experimental reverse osmosis water desalination system, Ind. Eng. Chem. Res. 48 (2009) 6126 6136. [14] A. Bartman, A. Zhu, P.D. Christofides, Y. Cohen, Minimizing energy consumption in reverse osmosis membrane desalination using optimization-based control, J. Process. Control. 20 (2010) 1261 1269. [15] M. Li, Minimization of energy in reverse osmosis water desalination using constrained nonlinear optimization, Ind. Eng. Chem. Res. 49 (2010) 1822 1831. [16] M. Li, Reducing specific energy consumption in reverse osmosis (RO) water desalination: an analysis from first principles, Desalination 276 (2011) 128 135. [17] M. Li, Optimal plant operation of brackish water reverse osmosis (BWRO) desalination, Desalination 293 (2012) 61 68. [18] M. Li, Optimization of multitrain brackish water reverse osmosis (BWRO) desalination, Ind. Eng. Chem. Res. 51 (2012) 3732 3739. [19] Inland Empire Utility Agency, Chino I Desalter Operation and Maintenance Manual, 2005. [20] Johnston Pump Company, Pump Performance Test Setup and Test Data Sheet, 1999. [21] M.A. Bernier, B. Bourret, Pumping energy and variable frequency drives, ASHRAE J. 41 (1999) 37 40.