Development of a transient model for the desalination of sea/brackish water through reverse osmosis

Sobana, S.; Panda, Rames C.

doi:10.1080/19443994.2012.749376

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…DESCRIPTION Sobana and Panda (2013) [38] have designed a transfer function model (shown in equation 1) for control of a RO desalination plant with a capacity of 3.80 MLD drinking water in the Village of Narippaiyur, District Ramanathapuram, Tamil Nadu, India. Here, two-input twooutput (TITO) parameters were considered for PID controller design [39].…”

Section: Ro Desalination Plant's Processmentioning

confidence: 99%

“…The input parameters considered were pump pressure (∆P) and the ratio of flow rates of seawater feed to that of brine stream (RFB), while the output parameters ware permeate flow rate p F (m 3 /h) and pH. The schematic diagram of the RO desalination plants is shown in Figure .1 [38].…”

Section: Ro Desalination Plant's Processmentioning

confidence: 99%

“…Then, it is pumped to the RO tubular membrane chamber using the high-pressure pump and separated into freshwater and brine. In this plant, the RO membrane is made by Dow Filmtech with a diameter of 2.5 inches [38]. Finally, the freshwater collected into the freshwater tank is ready for portable water, while the concentrated brine is returned to the equalization tank for reuse.…”

Section: Ro Desalination Plant's Processmentioning

confidence: 99%

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PID Control Design Using AGPSO Technique and Its Application in TITO Reverse Osmosis Desalination Plant

et al. 2022

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Desalination plants have an important concern regarding controlling the permeate flow rate and pH during operability. This paper proposes the proportional integral derivative (PID) control design using modified Particle Swarm Optimization (PSO) techniques called autonomous groups PSO (AGPSO) in the two-input two-output (TITO) RO desalination plant to control the permeate flow rate and pH. Here, three different versions (AGPSO1, AGPSO2, and AGPSO3) of the AGPSO algorithm are utilized to design PID control for the same TITO plant. In addition, an integral time absolute error (JITAE) based objective function is utilized to design a PID controller. The simulation results suggest that the proposed controller designs are flexible, self-tuning, and have stable characteristics, while the AGPSO3-PID control design attained a robust design for optimum tuning compared to existing improved grey wolf optimization PID (IGWO-PID) and other versions of AGPSO based PIDs (AGPSO1-PID, AGPSO2-PID). The design of AGPSO-PID achieved a minimum objective function than existing IGWO-PID and other versions of AGPSO based PIDs {AGPSO1-PID, AGPSO2-PID}. Finally, the proposed controller designs outperform the existing IGWO-PID design in literature in terms of control performance, demonstrating a precise control for and the improvement of plant performance.

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Section: Ro Desalination Plant's Processmentioning

confidence: 99%

Section: Ro Desalination Plant's Processmentioning

confidence: 99%

Section: Ro Desalination Plant's Processmentioning

confidence: 99%

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PID Control Design Using AGPSO Technique and Its Application in TITO Reverse Osmosis Desalination Plant

et al. 2022

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“…The temperature of feed water is supposed to be a disturbance in the process while the feed salinity is constant. The system model can be represented by the transfer function matrix as (13).…”

Section: System Identification This Section Aims To Developmentioning

confidence: 99%

“…The model can be validated by selecting the highest percentage fit of model and correlation study of residuals between inputs and outputs [12]. Nonlinear models and single input-single output (SISO) models were presented to describe the transient behavior of RO plants [13][14][15]. However, these models offer some drawbacks because of the selection of process parameters to be controlled and monitored or for limited parameters such as SISO models.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of energy consumption and model identification of reverse osmosis desalination system fed by hybrid renewable energy source under different operating conditions

Hossam-Eldin

Abed

Youssef

et al. 2019

IEEJ Transactions Elec Engng

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In this article, the specific energy consumption (SEC) and water production cost (Cw) of a reverse osmosis (RO) desalination plant are investigated experimentally under different working conditions. The plant is fed by a hybrid renewable energy source. The frequency of the high‐pressure pump motor is changed from 35 to 50 Hz using a variable frequency drive. In addition, the feed water temperature and the reject control valve are controlled. This article investigates the effects of SEC, Cw, permeate salt concentration, and plant productivity. The results show that both SEC and Cw are reduced by increasing the frequency up to 45 Hz before rising thereafter. Also, SEC and Cw decrease with increasing feed temperature and reduced reject valve opening. As a result, the optimum values of SEC and Cw are found at 45 Hz and 40 °C with 25% valve opening, representing 4.5 kWh/m3 and 0.55 $/m3, respectively. Then, a model is proposed for the RO plant using the Matlab identification toolbox. The model is validated by comparing the simulation results with the experimental data. Based on the best fit ratio, the model presents good results for verification, which can be used for a process control design. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods

Golabi,

Erradi,

Qiblawey

et al. 2024

Appl Intell

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The reverse osmosis (RO) process is a well-established desalination technology, wherein energy-efficient techniques and advanced process control methods significantly reduce production costs. This study proposes an optimal real-time management method to minimize the total daily operation cost of an RO desalination plant, integrating a storage tank system to meet varying daily freshwater demand. Utilizing the dynamic model of the RO process, a cascade structure with two reinforcement learning (RL) agents, namely the deep deterministic policy gradient (DDPG) and deep Q-Network (DQN), is developed to optimize the operation of the RO plant. The DDPG agent, manipulating the high-pressure pump, controls the permeate flow rate to track a reference setpoint value. Simultaneously, the DQN agent selects the optimal setpoint value and communicates it to the DDPG controller to minimize the plant’s operation cost. Monitoring storage tanks, permeate flow rates, and water demand enables the DQN agent to determine the required amount of permeate water, optimizing water quality and energy consumption. Additionally, the DQN agent monitors the storage tank’s water level to prevent overflow or underflow of permeate water. Simulation results demonstrate the effectiveness and practicality of the designed RL agents.

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Development of a transient model for the desalination of sea/brackish water through reverse osmosis

Cited by 11 publications

References 19 publications

PID Control Design Using AGPSO Technique and Its Application in TITO Reverse Osmosis Desalination Plant

PID Control Design Using AGPSO Technique and Its Application in TITO Reverse Osmosis Desalination Plant

Experimental investigation of energy consumption and model identification of reverse osmosis desalination system fed by hybrid renewable energy source under different operating conditions

Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods

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