An efficient deep reinforcement machine learning-based control reverse osmosis system for water desalination

Bonny, Talal; Kashkash, Mariam; Ahmed, Farah Ejaz

doi:10.1016/j.desal.2021.115443

Cited by 33 publications

(13 citation statements)

References 11 publications

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“…In order to optimize the phosphate adsorption process and direct the choice and preparation of adsorption materials, we can simultaneously apply the ML approach to the adsorption of phosphate. Previous documents have reported the applications of ML in solving multiple environmental problems such as heavy metal removal, , micropollutant oxidation, , seawater desalination, , carbon dioxide adsorption, , and municipal solid-waste treatment. , A number of ML models such as bagging, linear regression (LR), neural networks (NNs), and support vector machines (SVMs), and tree-based ML models have been developed in previous studies. Particularly, decision tree-based algorithms, including gradient boosting decision tree (GBDT), decision tree (DT), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and random forest (RF), are a subcategory of supervised ML models. ,− DT is a tree structure that is like a binary tree or multitree.…”

Section: Introductionmentioning

confidence: 99%

“…CatBoost, short for classification enhancement, is a state-of-the-art, open-source toolbox for gradient improvement that can handle the challenge of addressing the fundamentally distinct ideas of classification features. In comparison to deep learning models, RF in the scikit-learn package is a resilient ensemble model that can be used to make accurate predictions with a limited number of model parameters and is gaining more interest in the scientific and technical communities . These tree-based algorithms have gained increasing popularity due to their ability to handle relatively small data sets (200–1000 data points) with more robust and faster hyperparameter tuning compared with widely used ANN and SVM models .…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents

Jiang

et al. 2023

ACS EST Eng.

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Phosphate removal is a crucial objective in wastewater engineering to reduce harmful environmental impacts like eutrophication. Adsorption, a low-cost and efficient process for phosphate abatement, primarily relies on trapping phosphate on lowsolubility solid surfaces. Metal-based materials, due to their abundance, low cost, environmental friendliness, and chemical stability, are considered the most promising phosphate adsorbents. However, the synthesis of appropriate adsorbents is complex and timeconsuming. In addition, the diverse textural properties, the presence of various metals, and the selection of adsorption parameters make it challenging to the underlying mechanism of phosphate adsorption. In this study, we compiled a data set including 1800 data points mined from 128 peer-reviewed papers and adopted machine learning (ML) to systematically evaluate phosphate adsorption concerning textural properties, metal compositions, and adsorption parameters. We applied three different tree-based algorithms, including random forest (RF), decision trees (DTs), and extreme gradient boosting (XGBoost), to guide the design of adsorbents and predict the phosphate adsorption performances. Among the three algorithms, RF showed the best predictive performance with a high R 2 of 0.984 and a low root-mean-squared error (RMSE) of 0.650. Feature importance, based on the Shapley values, demonstrated the contributions of adsorbents' textural properties (e.g., surface area), adsorption parameters, and metal types in the order of precedence of phosphate adsorption, providing critical insights into guiding adsorbents design and synthesis for phosphate adsorption applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents

Jiang

et al. 2023

ACS EST Eng.

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“…Lastly, the pressure drop distribution across the membrane was analyzed by means of a framework. Bonny et al [ 27 ] suggested a novel and efficient approach to determining transmembrane pressure using Deep Reinforcement Learning (DRL) and used a Deep Deterministic Policy Gradient (DDPG) agent to adjust the pressure across a membrane.…”

Section: Introductionmentioning

confidence: 99%

“…It is known that there are few studies on modeling the wastewater treatment process of coal-fired power plants. Cai, Bonny, Salgado-Reyna, and Al-Obaidi et al [ 25 , 27 , 28 , 29 ] used intelligent algorithms to model and analyze the RO process. These excellent studies used intelligent algorithms for research analysis; unfortunately, there may be some uncertainty in the analytic process.…”

Section: Introductionmentioning

confidence: 99%

Simulation and Operational Optimization of RO Based Desalination for Coal-Fired Power Plants’ Wastewater

Xia

et al. 2022

Membranes

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Focusing on the problems of opaqueness and high energy consumption in coal-fired power plant wastewater recycling processes, this paper studies the simulation and operational optimization of coal-fired power plant wastewater treatment by taking a coal-fired power plant system in Inner Mongolia as an example. Firstly, based on the solution–diffusion theory, pressure drop, and osmotic concentration polarization, a mechanistic model equation for coal-fired power plant wastewater treatment is developed. Secondly, the equation fitness and equation parameters are calibrated to obtain an accurate model. Thirdly, the system is simulated and analyzed so as to obtain the influence and change trajectories of different feed flowrates, temperatures, pressures, and concentrations on various aspects of the system’s performance, such as water recovery rate, salt rejection rate, and so on. Finally, in order to reduce the operating cost of the system, an optimization analysis is carried out, with the lowest specific energy consumption and average daily operating cost as optimization goals, and the performance changes of the system before and after optimization under three different working conditions are compared. The results show that adopting the given optimal strategy can significantly reduce the system’s operational cost. This research is helpful for the digitization and low-carbon operation of coal-fired power plant wastewater treatment systems.

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“…The QL is one of the Reinforcement Learning Algorithms that is used in different applications (Bonny et al, 2022). Reinforcement Learning is the type of machine learning technique that depends on a computational approach to learning from interaction with the environment (Sutton & Barto, 2018).…”

Section: Introductionmentioning

confidence: 99%

Highly optimized Q‐learning‐based bees approach for mobile robot path planning in static and dynamic environments

Bonny

Kashkash

2021

Journal of Field Robotics

Self Cite

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This paper proposes a new novel approach to find an optimal path for a mobile robot in a two‐dimensional environment. Finding the optimal path is done using the Bees Algorithm (BA) with the Q‐Learning Algorithm. A new method to build the initial population is proposed to find the initial population regardless of the number and location of obstacles in the environment. Q‐Learning is implemented as a local search function of the BA. The hybridization of the BA and the Q‐Learning aims to find the optimal path with a fewer number of iterations of the BA. This method takes advantage of the BA to solve the problem without constraints and the sterilization in the Q‐Learning to find the shortest path. The experiment is run on some different maps to validate the proposed method in the static and dynamic case. The experimental results show the robustness and effectiveness of the proposed method in finding the optimal path. The comparison is executed to view the superiority of this method in finding the shortest path in the comparison of the results of other algorithms.

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An efficient deep reinforcement machine learning-based control reverse osmosis system for water desalination

Cited by 33 publications

References 11 publications

Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents

Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents

Simulation and Operational Optimization of RO Based Desalination for Coal-Fired Power Plants’ Wastewater

Highly optimized Q‐learning‐based bees approach for mobile robot path planning in static and dynamic environments

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