Artificial neural network and response surface methodology for modeling reverse osmosis process in wastewater treatment

Alardhi, Saja Mohsen; Salman, Ali Dawood; Breig, Sura Jasem Mohammed; Jaber, Alaa Abdulhady; Fiyadh, Seef Saadi; AlJaberi, Forat Yasir; Duc Nguyen, D.; Van, Bao; Le, Phuoc-Cuong

doi:10.1016/j.jiec.2024.02.039

Cited by 7 publications

(2 citation statements)

References 63 publications

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“…Also, low values found for MAE and ADD demonstrate that the performance of the ANN-GWO model is more precise for this system. In addition, in MSE and RMSE, the ANN-GWO model shows lower error rates, indicating the superiority of the neural model over the RSM statistical model [20].…”

Section: Evaluation Of the Predictive Modelsmentioning

confidence: 95%

RSM and ANN Comparative Modelling with a Granulation Treatment in Mixed Waters

Sanchez‐Sanchez,

Morales‐Rivera,

Moeller‐Chávez

et al. 2024

Chem Eng & Technol

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A Box‐Behnken design was used for the analysis using a gray wolf optimizer (GWO)‐coupled artificial neural network (ANN) model and response surface methodology (RSM) to analyze the effect of three operating parameters (volumetric exchange ratio [VER], aeration rate [AR], and cycle time [CT]) manipulated during an aerobic granular sludge process (AGS) sequencing batch reactor on modeling the removal of chemical oxygen demand (COD) in mixed wastewater. The most efficient architecture for COD showed the highest efficiency for modeling the AGS. The RSM model and plot results indicate that the CT and AR were the most influential on COD removal efficiency. When compared with models with statistical indices, GWO‐ANN demonstrated higher performance compared to RSM.

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Section: Evaluation Of the Predictive Modelsmentioning

confidence: 95%

RSM and ANN Comparative Modelling with a Granulation Treatment in Mixed Waters

Sanchez‐Sanchez,

Morales‐Rivera,

Moeller‐Chávez

et al. 2024

Chem Eng & Technol

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“…Mesoporous silica has attracted interest due to its potential applications and unique properties such that these materials have been used broadly in drug delivery, imaging, and environmental catalysis. [8][9][10][11] The distinctive properties of porous silica nanoparticles, such as their high biocompatibility and control-lable particle size, have made them the focus of numerous research studies. [12,13] Mesoporous silica nanoparticles (MSNs) are well-suited for biomedical applications and drug delivery due to their pore size range (2-50 nm).…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Review of Mesoporous Silica Nanoparticles: Drug Loading, Release, and Applications as Hemostatic Agents

Albayati,

Alardhi,

Khalbas

et al. 2024

ChemistrySelect

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Recently, mesoporous silica nanoparticles (MSNs) have emerged as promising candidates in the field of hemorrhage control owing to their extended pore size, high surface area, and excellent biocompatibility. These characteristics directly influence the toxicity of cells, the loading of therapeutic agents, and the release of active ions during the hemostasis process. Therefore, understanding the fundamentals of tuning these characteristics is important to design these types of carriers. While several literature reviews have explored the role of MSNs in hemorrhage control, comprehensive studies focusing on their general characteristics and specific applications remain scarce. This review concentrates on the principles of synthesizing mesoporous silica, the general types of MSNs, techniques for loading drugs methods onto the site of injury, release kinetics models, biocompatibility, toxicity, and the unique properties of MSNs. Furthermore, the article examines the mechanism of action of MSNs as nanomaterial hemostatic agents.

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