Model-predictive control of feed flow reversal in a reverse osmosis desalination process

Bartman, Alex R.; McFall, Charles W.; Christofides, Panagiotis D.; Cohen, Yoram

doi:10.1016/j.jprocont.2008.06.016

Cited by 63 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, a RO desalination plant is a multi-input multi-output (MIMO) nonlinear system where the nonlinearity is caused by the nonlinear behavior of each unit and practical challenges such as fouling, fault and model mismatch. In some works, the model of a RO system is considered nonlinear and time-invariant with respect to the membrane, and valves fouling, membrane deformations and faults (Bartman et al, 2009). If the actuator fault is regarded as a nonlinear behavior of the system, the nonlinear dynamics of the system will become very complicated.…”

Section: Plant Descriptionmentioning

confidence: 99%

“…Additionally, based on the parity space method, (Wu et al, 2018) investigated the problem of fault detection for the linear discrete time-varying system with multiplicative noise. On the other hand, MPCs are powerful tools to control RO desalination plants (Abbas, 2006), (Bartman et al, 2009). The AFTC –based model predictive controllers seemed to be the most functional approach to control RO desalination plants according to literature (Mcfall et al, no date), (Gambier et al, 2009, 2010).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integrated model predictive fault-tolerant control, and fault detection based on the parity space approach for a reverse osmosis desalination unit

Mehrad

Kargar

2020

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Actuator faults are inevitable in small reverse osmosis desalination plants. It may cause energy losses and reduce the quality of the freshwater, which may endanger human life. This paper focuses on the integrated fault detection and fault-tolerant control approach. The primary motivation of this paper is to propose a novel integrated fault detection and fault-tolerant control approach. The actuator fault is estimated using the concept of parity space approach. Then the system model is updated in the fault-tolerant control block using the information of the estimated fault parameter. Moreover, the proposed approach uses the receding-horizon predictive control-bounded data uncertainties controller, which is the robust and stable variant of generalized predictive control. The remaining uncertainty caused by the model and observer is compensated by this controller. The structure of a small reverse osmosis desalination plant is deployed. In this plant, the permeate flow rate and conductivity are controlled by a retentate valve and a bypass valve, which add a small amount of inlet to the outlet. The performances of three predictive model controllers are evaluated, and a comparison is made between their computational costs, stability, and robustness. The plant is considered to be linear time-invariant and subject to model uncertainties, measurement noise, and actuator fault in the retentate valve as efficiency dropping. The results reveal the robustness of the proposed approach concerning noise and matched uncertainties as well as its accommodation to actuator fault up to 90%.

show abstract

Section: Plant Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated model predictive fault-tolerant control, and fault detection based on the parity space approach for a reverse osmosis desalination unit

Mehrad

Kargar

2020

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…Many studies have made great contributions to the desalination literature to control RO systems. The control schemes include conventional (Alatiqi et al 1989;AlDhaifallah et al 2012;Sobana & Panda 2014;Ashagre et al 2020), feedforward (McFall et al 2008, optimal (Gambier et al 2009;Ghobeity & Mitsos 2010), adaptive (Gu et al 2013), model predictive control (MPC) (Robertson et al 1996;Abbas 2006;Bartman et al 2009aBartman et al , 2009bYang et al 2012), neural networks (Madaeni et al 2015), fractional order (Feliu-Batlle et al 2017), androbust control (Al-haj et al 2010;Phuc et al 2015Phuc et al , 2016Zebbar et al 2019). Among those, the most common types of controllers are based on PID and MPC (Sobana & Panda 2014).…”

Section: Introductionmentioning

confidence: 99%

Control of desalination plants using sliding mode scheme with state observer

Phuc

You

Kim

et al. 2021

Journal of Water Supply: Research and Technology-Aqua

View full text Add to dashboard Cite

This paper deals with real-time control with observer to manipulate desalination plants as well as to monitor system states for smart operations. The controller plays an important role in achieving stabilization of reverse osmosis (RO) systems to guarantee the desired water product and concentration. The super-twisting (STW) sliding mode control (SMC) algorithm guarantees performance while reducing chattering. Supposing that all the state variables are not available by sensors, the observer is implemented to provide state estimation. Since smart operations depend on control algorithm and sensor availability, the proposed strategy provides robustness to ensure the water productivity even under uncertainties or under failure of sensors. The robustness is guaranteed by active controller where 80% of disturbance is eliminated in product water flow and that of product water quality is approximately 95%. As well, the state observer can produce precise predictions of the unmeasured states. Sliding mode control with observer provides the system with stability, while assuring better performances against uncertainties. Finally, the active controller with state estimator can guarantee a robust control strategy and monitoring system to extend the life of the filters and membranes, while ensuring sustainability. This control strategy is highly recommended for smart operations of desalination plants.

show abstract

“…For the critical variables' control of the seawater RO desalination plants, diverse controllers have been developed. These controllers are mainly: conventional [41][42][43], feedforward [37], optimal [28,44], adaptive [35], model predictive [45][46][47][48], fractional order [49], or robust [32,[50][51][52]. In these plants, the most implemented controller is the proportional integral derivative (PID), owing to the fact that many process engineers are much more familiar with this class of controller than with sophisticated advanced controllers, due their functional simplicity, which allows them to operate in a straightforward mode [53,54].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of an Expert Model Predictive Controller in a Pilot-Scale Reverse Osmosis Plant for Brackish and Seawater Desalination

et al. 2019

View full text Add to dashboard Cite

This article addresses the design and real-time implementation of an expert model predictive controller (Expert MPC) for the control of the brackish and seawater desalination process in a pilot-scale reverse osmosis (RO) plant. This pilot-scale plant is used in order to obtain the optimal operation conditions of the RO desalination process through the implementation of different control strategies, as well as in the training of operators in the new control and management technologies. A dynamical mathematical model of this plant has been developed based on the available field data and system identification procedures. Predictions of the obtained model were in good agreement with the available field data. The designed Expert MPC is distinguished by having a plant identification block and an expert system. The expert system, using a rule-based approach and the evolution of the plant variables, can modify the plant identification block, the plant prediction model, and/or the optimizer in order to improve the performance, robustness and operational safety of the overall control system. The real-time comparison results of the designed Expert MPC and a well-designed model predictive controller (MPC) show that the proposed Expert MPC has a significantly better performance and, therefore, higher accuracy and robustness.

show abstract

Model-predictive control of feed flow reversal in a reverse osmosis desalination process

Cited by 63 publications

References 14 publications

Integrated model predictive fault-tolerant control, and fault detection based on the parity space approach for a reverse osmosis desalination unit

Integrated model predictive fault-tolerant control, and fault detection based on the parity space approach for a reverse osmosis desalination unit

Control of desalination plants using sliding mode scheme with state observer

Real-Time Implementation of an Expert Model Predictive Controller in a Pilot-Scale Reverse Osmosis Plant for Brackish and Seawater Desalination

Contact Info

Product

Resources

About