Operational optimization of SWRO process with the consideration of load fluctuation and electricity price

Jiang, Aipeng; Jiangzhou, Shu; Wen, Chenglin; Wang, Jian; Ding, Qiang; Xing, Changxin

doi:10.1016/j.ifacol.2015.09.033

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Pretreatment is important because silica is the biggest problem in extracting precious metals using membranes. Several studies have been carried out on removing silica content in geothermal brine to avoid fouling phenomena [ 29 , 30 ].…”

Section: Methodsmentioning

confidence: 99%

Lithium Separation from Geothermal Brine to Develop Critical Energy Resources Using High-Pressure Nanofiltration Technology: Characterization and Optimization

et al. 2023

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There is a shift from internal combustion engines to electric vehicles (EVs), with the primary goal of reducing CO2 emissions from road transport. Battery technology is at the heart of this transition as it is vital to hybrid and fully electric vehicles’ performance, affordability, and reliability. However, it is not abundant in nature. Lithium has many uses, one of which is heat transfer applications; synthesized as an alloying agent for batteries, glass, and ceramics, it therefore has a high demand on the global market. Lithium can be attained by extraction from other natural resources in igneous rocks, in the waters of mineral springs, and geothermal brine. During the research, geothermal brine was used because, from the technological point of view, geothermal brine contains higher lithium content than other resources such as seawater. The nanofiltration separation process was operated using various solutions of pH 5, 7, and 10 at high pressures. The varying pressures are 11, 13, and 15 bar. The nanofiltration method was used as the separation process. High pressure of inert nitrogen gas was used to supply the driving force to separate lithium from other ions and elements in the sample. The research results supported the selected parameters where higher pressure and pH provided more significant lithium recovery but were limited by concentration polarization. The optimal operating conditions for lithium recovery in this research were obtained at a pH of 10 under a pressure of 15 bar, with the highest lithium recovery reaching more than 75%.

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Section: Methodsmentioning

confidence: 99%

Lithium Separation from Geothermal Brine to Develop Critical Energy Resources Using High-Pressure Nanofiltration Technology: Characterization and Optimization

et al. 2023

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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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Artificial Intelligence–Based Optimization of Reverse Osmosis Systems Operation Performance

et al. 2020

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Operational optimization of SWRO process with the consideration of load fluctuation and electricity price

Cited by 3 publications

References 32 publications

Lithium Separation from Geothermal Brine to Develop Critical Energy Resources Using High-Pressure Nanofiltration Technology: Characterization and Optimization

Lithium Separation from Geothermal Brine to Develop Critical Energy Resources Using High-Pressure Nanofiltration Technology: Characterization and Optimization

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

Artificial Intelligence–Based Optimization of Reverse Osmosis Systems Operation Performance

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