Artificial intelligence simulation of water treatment using a novel bimodal micromesoporous nanocomposite

Yang, Jianxun; Du, Qian; Ma, Rongfu; Khan, Afrasyab

doi:10.1016/j.molliq.2021.117296

Cited by 12 publications

(2 citation statements)

References 48 publications

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“…Prediction of drugs solubility using artificial intelligence (AI) method has currently attracted the attention as a noteworthy option for validating the actual data obtained from experimental research. Development of predictive modeling and simulation via this technique for different industries (i.e., separation, purification, extraction and drug delivery) can considerably decline computation time and guarantee the accuracy of conducted experimental results 11 , 12 .…”

Section: Introductionmentioning

confidence: 99%

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

Abdelbasset

Elkholi

Ismail

et al. 2022

Sci Rep

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Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).

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Section: Introductionmentioning

confidence: 99%

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

Abdelbasset

Elkholi

Ismail

et al. 2022

Sci Rep

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“…The modeling results confirmed the high accuracy of these models, but the ANN results were more reliable than the outcomes obtained from ANFIS model. Usually, if the model is validated through comparing with experimental results, then the model can be used to map the adsorption process and find the optimum conditions 56 , 57 .…”

Section: Introductionmentioning

confidence: 99%

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

Parsaei

Roudbari

Piri

et al. 2022

Sci Rep

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We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model’s training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.

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Machine learning method for simulation of adsorption separation: Comparisons of model’s performance in predicting equilibrium concentrations

Yin

Alazzawi

Mironov

et al. 2022

Arabian Journal of Chemistry

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Artificial intelligence simulation of water treatment using a novel bimodal micromesoporous nanocomposite

Cited by 12 publications

References 48 publications

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

Machine learning method for simulation of adsorption separation: Comparisons of model’s performance in predicting equilibrium concentrations

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