Modeling and control of reverse osmosis desalination process using centralized and decentralized techniques

Sobana, S.; Panda, Rames C.

doi:10.1016/j.desal.2014.03.014

Cited by 25 publications

(11 citation statements)

References 16 publications

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“…The proposed method is compared with the main steam temperature model by Valsalam [18] . The MSE value for the proposed method is 1.8595e-08 and the MSE value for the Valsalam model is 7.2531.…”

Section: Resultsmentioning

confidence: 99%

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Statistical modeling of an integrated boiler for coal fired thermal power plant

Chandrasekharan

Panda

Swaminathan

2017

Heliyon

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The coal fired thermal power plants plays major role in the power production in the world as they are available in abundance. Many of the existing power plants are based on the subcritical technology which can produce power with the efficiency of around 33%. But the newer plants are built on either supercritical or ultra-supercritical technology whose efficiency can be up to 50%. Main objective of the work is to enhance the efficiency of the existing subcritical power plants to compensate for the increasing demand. For achieving the objective, the statistical modeling of the boiler units such as economizer, drum and the superheater are initially carried out. The effectiveness of the developed models is tested using analysis methods like R2 analysis and ANOVA (Analysis of Variance). The dependability of the process variable (temperature) on different manipulated variables is analyzed in the paper. Validations of the model are provided with their error analysis. Response surface methodology (RSM) supported by DOE (design of experiments) are implemented to optimize the operating parameters. Individual models along with the integrated model are used to study and design the predictive control of the coal-fired thermal power plant.

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Section: Resultsmentioning

confidence: 99%

“…Statistical model for the desalination of sea water was proposed by Sobana and Panda [18] . The regression analysis and ANOVA was performed for the developed statistical model.…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling of an integrated boiler for coal fired thermal power plant

Chandrasekharan

Panda

Swaminathan

2017

Heliyon

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“…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). PID-based controllers include the classical one (AlDhaifallah et al 2012;Sobana & Panda 2014), or modified PID such as auto-tuned PID (Rathore et al 2013;Chithra et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Among those, the most common types of controllers are based on PID and MPC (Sobana & Panda 2014). PID-based controllers include the classical one (AlDhaifallah et al 2012;Sobana & Panda 2014), or modified PID such as auto-tuned PID (Rathore et al 2013;Chithra et al 2015). Those controllers can work well but with poor robustness even without uncertainty.…”

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

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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.

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“…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

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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.

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Modeling and control of reverse osmosis desalination process using centralized and decentralized techniques

Cited by 25 publications

References 16 publications

Statistical modeling of an integrated boiler for coal fired thermal power plant

Statistical modeling of an integrated boiler for coal fired thermal power plant

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

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