Artificial neural networks modeling for the prediction of Pb(II) adsorption

Kiraz, Alper; Canpolat, Onur; Erkan, Enes Furkan; Özer, Çiğdem

doi:10.1007/s13762-018-1798-4

Cited by 21 publications

(3 citation statements)

References 28 publications

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“…ANN is a tool able to processing information and establishing a relationship between inputs-outputs that may guess the behavior of a procedure under diverse circumstances. ANN represents a powerful predictive model that employs experimental data for learning and does not require knowledge of system rules [18,19].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances

Jaid¹,

Bohan²,

Salman³

2020

Rev. Chim.

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Adsorption of Pb(II) and Cd(II) from wastewater utilizing three nano-magnetic materials (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2 Fe2O4, and Cu0.7Zn0.3 Fe2O4) were studied. The nano-magnetic materials were prepared from the Cu Frites powder and then the Cu ions were replaced by Zn ions in three concentrations, these materials were characterized by X-ray diffraction (XRD) which has conformed good crystallinity with spinel structure and particle size in the range (26.5�23.9 nm). Artificial neural networks were applying to model the removal of Pb(II) and Cd(II) on three adsorbents from wastewater. The operating conditions that affect on adsorption process are adsorbent dose (0.1, 0.25, and 0.5) g, pH (3, 7, and 9), and contact time (15, 30, and 45) min. Three Multilayered feed-forward neural networks (3:9:2) were successfully used for modeling of removing heavy metals on three adsorbents. The antimicrobial effectiveness of ferrite substances was studied against two types of bacteria. The three adsorbents showed an excellent removal for Cd (II) ions 100% complete removal on Cu0.9Zn0.1 Fe2O4, Cu0.8Zn0.2 Fe2O4, and it was 95% on Cu0.7Zn0.3 Fe2O4, and less removal for Pb (II) ions on Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2 Fe2O4 were 78.4% and 78.8%, and 83.4% on Cu0.7Zn0.3 Fe2O4. ANN models show efficient simulation with a high correlation coefficient (R2 = 0.99) for all three adsorbents, Sensitivity Analysis demonstrated that pH, time, and a dose of the adsorbent have a strong impact on the process of removal.The results for antimicrobial effectiveness showed that Cu0.9Zn0.1 Fe2O4 had the most antibacterial properties against two types of bacteria and the S. aureus killing rate was less than the E. coli killing rate of all ferrite composite nanoparticles.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances

Jaid¹,

Bohan²,

Salman³

2020

Rev. Chim.

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“…A) Discontinuous narrow undercutting defect B) Continuous wide undercutting (Zong et al, 2016) The stress is concentrated at the bottom of an undercut because it has a notch-like effect (Juvinall & Marshek 1991) In butt welded joints, stress concentration occurs at the undercut which initiates a crack when the weldment is subjected to dynamic loads (Liinalampi et al, 2019;Molski & Tarasiuk, 2020;Kurtulmuş & Doğan, 2021;Kiraz et al, 2023). Fatigue cracks always start at stress raisers easily (Liinalampi et al, 2019).…”

Section: Figure 1 Two Different Undercut Discontinuities Formed In Bu...mentioning

confidence: 99%

The Effects of Undercut Depth and Length on Weldment Mechanical Properties

KURTULMUŞ

2023

Gazi University Journal of Science Part A: Engineering and Innovation

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Undercutting is a fusion welding defect that appears as a groove at the weld metal's toe. An undercut discontinuity forms when welding, particularly when the current is applied at an extremely rapid rate. It decreases the static and fatigue strength of the weld and produces stress concentration at the welding zone. The height of the reinforcement, the weld bead's contact angle, the undercut's breadth, depth, length, and root radius, as well as other factors, affect the stress concentration factor's size. In this study, two mild steel plates with a 20 mm thickness were welded together using gas metal arc welding. Each test plate was machined to have a 30° single bevel groove angle prior to welding. The butt welded plate underwent radiographic NDT testing. A weldment free of defect was created. The weldment was machined into typical test specimens for tensile strength. An undercut defect was simulated by drilling a groove through each tensile test sample. Grooves had different depths and lengths. After that, samples were put through a tensile test. The test findings allowed for the identification of the impacts of groove shape on tensile strength, ductility, and static stress concentration factor.

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“…tetapi persamaan-persamaan tersebut belum bisa menjelaskan pengaruh parameter operasi adsorpsi yang tidak hanya terbatas pada konsentrasi awal adsorbate dan jumlah adsorben, seperti pH, suhu dan karakteristik adsorben(Hafsa et al, 2020;Kiraz et al, 2019). Sehingga berkembanglah metode statistik(Hafsa et al, 2020;Pauletto et al, 2020) seperti response surface method (model machine learning dengan metode Stratified 10-fold Cross validation, terlihat bahwa model machine learning dapat digunakan untuk melakukan prediksi adsorpsi metilene biru pada karbon aktif dengan tingkat akurasi yang berbeda-beda.…”

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Prediksi Adsorpsi Zat Warna Metilene Biru Pada Karbon Aktif Menggunakan Machine Learning

Jatmiko

2022

DKB

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ABSTRAKPemanfaatan teknologi informasi dalam mengurangi kegiatan eksperimen di laboratorium semakin meningkat. Pemanfaatan teknologi informasi akan menghemat bahan, waktu dan biaya, serta mengurangi resiko bahaya yang mungkin timbul jika dilakukan eksperimen secara langsung. Salah satu teknologi informasi yang sedang berkembang adalah machine learning. Penelitian ini bertujuan untuk mengetahui unjuk kerja model machine learning dalam memprediksi adsorpsi metilene biru pada karbon aktif. data set adsorpsi metilene biru menggunakan karbon aktif biji alpukat dibagi menjadi data latihan dan data uji. data set tersebut kemudian digunakan untuk menguji performa model machine learning (neural networks, support vector machines, random forest, dan linear regression) menggunakan metode Stratified 10-fold Cross validation. Model neural network merupakan model machine learning terbaik untuk memprediksi proses adsorpsi metilene biru pada karbon aktif dibandingkan dengan model SVM, Random Forest maupun regresi linier dengan performa prediksi terbaik pada data latihan maupun data uji. Nilai MSE 14.640, RMSE 3.826, MAE 3.178 dan R 2 0.975 pada data latihan dan pada data uji nilai MSE 3.51e-06, RMSE 0.00187, MAE 0.00135 dan R 2 0.999.

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Artificial neural networks modeling for the prediction of Pb(II) adsorption

Cited by 21 publications

References 28 publications

Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances

Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances

The Effects of Undercut Depth and Length on Weldment Mechanical Properties

Prediksi Adsorpsi Zat Warna Metilene Biru Pada Karbon Aktif Menggunakan Machine Learning

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