An artificial neural network combined with response surface methodology approach for modelling and optimization of the electro-coagulation for cationic dye

Response Surface ◽

Cationic Dye ◽

Artificial Neural ◽

Electro Coagulation ◽

Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process

Bioresource Technology ◽

10.1016/j.biortech.2014.01.021 ◽

2014 ◽

Vol 160 ◽

pp. 150-160 ◽

Cited By ~ 248

Author(s):

Anna Witek-Krowiak ◽

Katarzyna Chojnacka ◽

Daria Podstawczyk ◽

Anna Dawiec ◽

Karol Pokomeda

Keyword(s):

Neural Network ◽

Response Surface ◽

Biosorption Process ◽

Network Methods ◽

Artificial Neural ◽

Modelling and optimization of Nd:YAG laser micro-turning process during machining of aluminum oxide (Al2O3) ceramics using response surface methodology and artificial neural network

Manufacturing Review ◽

10.1051/mfreview/2014011 ◽

2014 ◽

Vol 1 ◽

pp. 12 ◽

Cited By ~ 1

Author(s):

Golam Kibria ◽

Biswanath Doloi ◽

Bijoy Bhattacharyya

Keyword(s):

Neural Network ◽

Aluminum Oxide ◽

Response Surface ◽

Nd:Yag Laser ◽

Turning Process ◽

Micro Turning ◽

Artificial Neural ◽

Corrigendum to “Modelling and optimization of photocatalytic degradation of phenol via TiO2 nanoparticles: An insight into response surface methodology and artificial neural network” [J. Photochem. Photobiol. A: Chem. 384 (2019) 1–15/112039]

Journal of Photochemistry and Photobiology A Chemistry ◽

10.1016/j.jphotochem.2020.112649 ◽

2020 ◽

Vol 400 ◽

pp. 112649

Author(s):

Muhammad Zulfiqar ◽

Mohamad Fakhrul Ridhwan Samsudin ◽

Syed Ali Ammar Taqvi ◽

Suriati Sufian

Keyword(s):

Neural Network ◽

Photocatalytic Degradation ◽

Response Surface ◽

Tio2 Nanoparticles ◽

Artificial Neural ◽

Insight Into ◽

Modelling and optimization of photocatalytic degradation of phenol via TiO2 nanoparticles: An insight into response surface methodology and artificial neural network

Journal of Photochemistry and Photobiology A Chemistry ◽

10.1016/j.jphotochem.2019.112039 ◽

2019 ◽

Vol 384 ◽

pp. 112039 ◽

Cited By ~ 27

Author(s):

Muhammad Zulfiqar ◽

Mohamad Fakhrul Ridhwan Samsudin ◽

Suriati Sufian

Keyword(s):

Neural Network ◽

Photocatalytic Degradation ◽

Response Surface ◽

Tio2 Nanoparticles ◽

Artificial Neural ◽

Insight Into ◽

Mathematical Modelling and Optimization of Cutting Force, Tool Wear and Surface Roughness by Using Artificial Neural Network and Response Surface Methodology in Milling of Ti-6242S

Applied Sciences ◽

10.3390/app7101064 ◽

2017 ◽

Vol 7 (10) ◽

pp. 1064 ◽

Cited By ~ 37

Author(s):

Erol Kilickap ◽

Ahmet Yardimeden ◽

Yahya Hışman Çelik

Keyword(s):

Neural Network ◽

Surface Roughness ◽

Tool Wear ◽

Mathematical Modelling ◽

Cutting Force ◽

Response Surface ◽

Artificial Neural ◽

Chironji (Buchanania lanzan) fruit juice extraction using cellulase enzyme: modelling and optimization of process by artificial neural network and response surface methodology

Journal of Food Science and Technology ◽

10.1007/s13197-020-04619-8 ◽

2020 ◽

Cited By ~ 1

Author(s):

Dileswar Pradhan ◽

S. Abdullah ◽

Rama Chandra Pradhan

Keyword(s):

Neural Network ◽

Response Surface ◽

Fruit Juice ◽

Cellulase Enzyme ◽

Juice Extraction ◽

Artificial Neural ◽

Comparative Study of Artificial Neural Network and Response Surface Methodology for Modelling and Optimization the Adsorption Capacity of Fluoride onto Apatitic Tricalcium Phosphate

Universal Journal of Applied Mathematics ◽

10.13189/ujam.2014.020202 ◽

2014 ◽

Vol 2 (2) ◽

pp. 84-91

Author(s):

M. Mourabet ◽

A. El Rhilassi ◽

M.Bennani Ziatni ◽

A. Taitai

Keyword(s):

Neural Network ◽

Comparative Study ◽

Adsorption Capacity ◽

Response Surface ◽

Tricalcium Phosphate ◽

Artificial Neural ◽

Navigating viscosity of ferrofluid using response surface methodology and artificial neural network

Journal of Materials Research and Technology ◽

10.1016/j.jmrt.2020.11.087 ◽

2020 ◽

Vol 9 (6) ◽

pp. 16339-16348

Author(s):

Nidal H. Abu-Hamdeh ◽

Ali Golmohammadzadeh ◽

Aliakbar Karimipour

Keyword(s):

Neural Network ◽

Response Surface ◽

Artificial Neural

Modeling of phenol separation from water by NiFe2O4 nanocomposite using response surface methodology and artificial neural network techniques

Journal of Environmental Chemical Engineering ◽

10.1016/j.jece.2021.105576 ◽

2021 ◽

pp. 105576

Author(s):

Leili Mohammadi ◽

Muhammad Nadeem Zafar ◽

Maqzia Bashir ◽

Sajjad Hussain Sumrra ◽

Syed Salman Shafqat ◽

...

Keyword(s):

Neural Network ◽

Response Surface ◽

Artificial Neural

Comparative study of response surface methodology and artificial neural network in the optimization of the ultrasound-assisted extraction of diarylheptanoid phytoestrogens from Curcuma comosa rhizomes

Chemical Engineering and Processing - Process Intensification ◽

10.1016/j.cep.2021.108461 ◽

2021 ◽

Vol 165 ◽

pp. 108461

Author(s):

Bancha Yingngam ◽

Abhiruj Navabhatra ◽

Wandee Rungseevijitprapa ◽

Chutinun Prasitpuriprecha ◽

Adelheid Brantner

Keyword(s):

Neural Network ◽

Comparative Study ◽

Response Surface ◽

Ultrasound Assisted Extraction ◽

Curcuma Comosa ◽

Assisted Extraction ◽

Ultrasound Assisted ◽

Artificial Neural