System identification and control of reverse osmosis desalination

Alatiqi, I.M.; Ghabris, A.H.; Ebrahim, S.

doi:10.1016/0011-9164(89)85009-x

Cited by 79 publications

(27 citation statements)

References 4 publications

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“…Many researchers have suggested identified transfer functions from input/output data of RO process. MIMO structure of RO plant at Doha can be given by Alatiqi et al (1989) using system identification techniques as Y 1 Y 2 Â Ã ¼ 0:007ð0:056sþ1Þ 0:213s 2 þ0:7sþ1 0 À7:3ð0:35sþ1Þ 0:213s 2 þ0:7sþ1…”

Section: Calculates Fluxes and Permeate Concentrationmentioning

confidence: 99%

“…The process variables are identified already and categorized under control variables (permeate salinity and permeate flow rate) and manipulated variables (feed pH and feed pump pressure). Results with conventional PI (Alatiqi et al 1989) and model predictive control are available in literature. According to Alatiqi et al (1989), permeates flux and conductivity were controlled by manipulating feed pressure and pH.…”

Section: Identificationmentioning

confidence: 99%

“…Results with conventional PI (Alatiqi et al 1989) and model predictive control are available in literature. According to Alatiqi et al (1989), permeates flux and conductivity were controlled by manipulating feed pressure and pH. Openloop step response data from RO plant was used to construct a MIMO model.…”

Section: Identificationmentioning

confidence: 99%

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Review on modelling and control of desalination system using reverse osmosis

Sobana¹,

Panda²

2011

Rev Environ Sci Biotechnol

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Dissolved salts in seawater or brackish water are reduced to a potable level through separation techniques, like, distillation, multiple effect vapor compression, evaporation, or by membrane processes such as electro-dialysis reversal, nanofiltration, and reverse osmosis (RO). RO is the most widely used desalination process. Recent advances in RO technology has led to more efficient separation and now is the most cost effective process to operate. The performance of the reverse osmosis process is dependent on concentration of dissolved solids in the feed-water, feed-water pressure, and the membrane strength to withstand system pressure, membrane solute rejection, membrane fouling characteristics, and the required permeate solute concentration. RO is a promising tool that uses cellulose acetate (or) polyamide membrane and is widely chosen as the cost of production is reduced by the use of energy efficient and process control techniques. This paper presents a review on modelling, identification of parameters from single input-outputs and multi input/ output lumped systems, dynamic modelling and control of desalination systems in the past twenty years by collecting more than 60 literatures.

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Section: Calculates Fluxes and Permeate Concentrationmentioning

confidence: 99%

Section: Identificationmentioning

confidence: 99%

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Review on modelling and control of desalination system using reverse osmosis

Sobana¹,

Panda²

2011

Rev Environ Sci Biotechnol

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“…And, only two of these are considered in the control system developed, namely, the flow rates of the acid and the retentate streams. [12] We use a similar flowsheet representation throughout this article, where, in all the figures the thin and thick lines represent material and energy streams, respectively.…”

Section: Restraining Number For Membrane Separatorsmentioning

confidence: 99%

Control degrees of freedom using the restraining number: further evaluation

Vasudevan

Konda

Rangaiah

2008

Asia-Pacific J Chem Eng

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A simple and effective procedure for the computation of control degrees of freedom (CDOF) using the concept of restraining number has been recently proposed by our group. The goal of the present contribution is to further evaluate and enhance this procedure. The restraining number is applied to a few more important units. In particular, a detailed analysis has been done for determining the restraining number of membrane separators, given their increasing importance in the industry today. The restraining number procedure is then successfully applied to compute the CDOF of several industrial-scale processes with distinctly unique characteristics. The simplicity, reliability, general applicability and effectiveness of the procedure are thus further demonstrated. To sum up, the present contribution improves upon and details the CDOF procedure clearly, discusses further issues like the restraining number for variable speed pumps and membrane separators, redundancies for absorbers and strippers and finally, the applicability of this procedure to three-phase distillation columns and complex industrial processes. 

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“…The first paper which proposed an effective closed-loop control strategy for RO utilized multiple single-input single-output (SISO) classical (i.e., proportional, integral, derivative (PID)) control loops. 3 Within a model-based control setting, step tests were used to perform system identification, resulting in a model that is a linear approximation around the operating point. The control algorithm of model predictive control (MPC) was applied to the resulting linear model.…”

Section: Introductionmentioning

confidence: 99%

Control and Monitoring of a High Recovery Reverse Osmosis Desalination Process

McFall

Bartman

Christofides

et al. 2008

Ind. Eng. Chem. Res.

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Model-based control and monitoring such as feed-forward/feedback control, fault detection and isolation (FDI), and fault-tolerant control (FTC) techniques that utilize Lyapunov-based control laws are implemented on a high recovery reverse osmosis desalination plant model. A detailed mathematical model of a high recovery reverse osmosis plant is developed. This model incorporates the large spatial variations of concentration and flow rate that occur in membrane units during high recovery operation. Bounded nonlinear feedback and feed-forward controllers are developed and applied to this system. The application of these controllers with FDI and FTC is demonstrated in the context of a high recovery reverse osmosis process simulation. The first set of simulations demonstrates the ability to compensate for the effects of large time-varying disturbances in the feed concentration on specific process outputs with and without feed-forward control. The second set of simulations demonstrates the ability of FDI and FTC techniques to recover desired plant operation subject to actuator failures.

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System identification and control of reverse osmosis desalination

Cited by 79 publications

References 4 publications

Review on modelling and control of desalination system using reverse osmosis

Review on modelling and control of desalination system using reverse osmosis

Control degrees of freedom using the restraining number: further evaluation

Control and Monitoring of a High Recovery Reverse Osmosis Desalination Process

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