MLP ANN Equipped Approach to Measuring Scale Layer in Oil-Gas-Water Homogeneous Fluid by Capacitive and Photon Attenuation Sensors DOI
Abdulilah Mohammad Mayet, Salman Arafath Mohammed,

Evgeniya Ilyinichna Gorelkina

et al.

Journal of Nondestructive Evaluation, Journal Year: 2025, Volume and Issue: 44(2)

Published: April 1, 2025

Language: Английский

Fabrication of an asymmetric supercapacitor using a novel electrode design and introduce a robust machine learning model for its performance evaluation DOI
Samaneh Mahmoudi Qashqay, Mohammad‐Reza Zamani‐Meymian, Ali Maleki

et al.

Journal of Power Sources, Journal Year: 2024, Volume and Issue: 613, P. 234911 - 234911

Published: June 20, 2024

Language: Английский

Citations

8
Research on specific capacitance prediction of biomass carbon-based supercapacitors based on machine learning DOI
Chenxi Zhao, Xueying Lu,

Huanyu Tu

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 97, P. 112974 - 112974

Published: July 16, 2024

Language: Английский

Citations

8
Insights into the specific capacitance of CNT-based supercapacitor electrodes using artificial intelligence DOI Creative Commons
Wael Z. Tawfik, Mohamed Shaban, Athira Raveendran

et al.

RSC Advances, Journal Year: 2025, Volume and Issue: 15(5), P. 3155 - 3167

Published: Jan. 1, 2025

This study uses various ML algorithms, including artificial neural networks, random forest, k -nearest neighbors, and decision tree, based on experimental studies to predict the specific capacitance characteristics of CNT-based SC electrodes.

Language: Английский

Citations

1
Research on prediction of energy density and power density of biomass carbon-based supercapacitors based on machine learning DOI
Xueying Lu, Chenxi Zhao,

Huanyu Tu

et al.

Sustainable materials and technologies, Journal Year: 2025, Volume and Issue: unknown, P. e01309 - e01309

Published: March 1, 2025

Language: Английский

Citations

1
A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials DOI
Chuang Wang, Xingxing Cheng, Kai Luo

et al.

Materials Science and Engineering R Reports, Journal Year: 2025, Volume and Issue: 165, P. 101010 - 101010

Published: May 3, 2025

Language: Английский

Citations

1
Deep learning prediction and experimental investigation of specific capacitance of nitrogen-doped porous biochar DOI
Xiaorui Liu, Haiping Yang,

Tang Yuanjun

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 403, P. 130865 - 130865

Published: May 25, 2024

Language: Английский

Citations

6
AI-based approach for predicting the storage performance of zinc oxide-based supercapacitor electrodes DOI
Mostafa A. Ebied, Mohamed Mostafa A. Azim,

Ahmed Emad-Eldeen

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 94, P. 112292 - 112292

Published: June 11, 2024

Language: Английский

Citations

6
Sustainable graphitic carbon derived from oil palm frond biomass for supercapacitor application: Effect of redox additive and artificial neural network‑based modeling approach DOI
Mohammad Ullah, Md Munirul Hasan, Rasidi Roslan

et al.

Journal of Electroanalytical Chemistry, Journal Year: 2024, Volume and Issue: 971, P. 118570 - 118570

Published: Aug. 13, 2024

Language: Английский

Citations

5
Recent advances and future prospects of MXene-based photocatalysts in environmental remediations. DOI
Basiru O. Yusuf, Mustapha Umar, Mansur Aliyu

et al.

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(6), P. 114812 - 114812

Published: Nov. 14, 2024

Language: Английский

Citations

5
Machine learning models for capacitance prediction of porous carbon-based supercapacitor electrodes DOI
Wael Z. Tawfik,

Samar N. Mohammad,

Kamel H. Rahouma

et al.

Physica Scripta, Journal Year: 2023, Volume and Issue: 99(2), P. 026001 - 026001

Published: Dec. 27, 2023

Abstract Energy storage devices and systems with better performance, higher reliability, longer life, wiser management strategies are needed for daily technology advancement. Among these devices, the supercapacitor is most preferable due to its high-limit capacitance that esteems more than different capacitors. Today, it considered a significant challenge design high-performance materials supercapacitors by exploring interaction between characteristics structural features of materials. Because this, essential predict when assessing material’s potential use in constructing supercapacitor-electrode applications. Machine learning (ML) can significantly speed up computation, capture complex mechanisms enhance accuracy prediction make best choices based on detailed status data. We aimed develop new strategy assisted applying ML analyze relationship porous carbon (PCMs) using hundreds experimental data literature. In present study, Linear Regression (LR), Tree (RT), Adaptive Neuro-Fuzzy Inference System (ANFIS) were used estimate supercapacitor’s capacitance. The effectiveness models was evaluated terms root mean square error (RMSE), absolute (MAE), correlation expected yield system-provided yield. developed ANFIS model, RMSE, MAE, R values 22.8, 39.7647, 0.90004, respectively, compares favourably regarding performance other built this purpose.

Language: Английский

Citations

10