Zeolite-Y-based catalyst synthesis from Nigerian Elefun Metakaolin: computer-aided batch simulation, comparative predictive response surface and neuro-fuzzy modelling with optimization

2021 ◽  
Author(s):  
Kazeem Kolapo Salam ◽  
Emmanuel Olusola Oke ◽  
Chiamaka Joan Ude ◽  
Umar Yahaya
Keyword(s):  
Response Surface ◽  
Zeolite Y ◽  
Fuzzy Modelling ◽  
Catalyst Synthesis ◽  
Neuro Fuzzy ◽  
Computer Aided ◽  
Batch Simulation ◽  
Predictive Response

Luffa Seed Oil Extraction: Response Surface and Neuro-Fuzzy Modelling Performance Evaluation and Optimization

2021 ◽  
Author(s):  
Kenechi Nwosu-Obieogu ◽  
Goziya W. Dzarma ◽  
Chijioke B. Ugwuodo ◽  
Linus I. Chiemenem ◽  
Kelechi N. Akatobi
Keyword(s):  
Performance Evaluation ◽  
Response Surface ◽  
Seed Oil ◽  
Oil Extraction ◽  
Fuzzy Modelling ◽  
Neuro Fuzzy

scholarly journals Coactive neuro-fuzzy modelling for colour recipe prediction

2002 ◽  
Author(s):  
E. Mizutani ◽  
J.-S.R. Jang ◽  
K. Nishio ◽  
H. Takagi ◽  
D.M. Anslander
Keyword(s):  
Fuzzy Modelling ◽  
Neuro Fuzzy

Comparison of different modeling methods toward predictive capability evaluation of the bonding strength of wood laminated products

2021 ◽  
pp. 095440892110530
Author(s):  
Morteza Nazerian ◽  
Seyed Ali Razavi ◽  
Ali Partovinia ◽  
Elham Vatankhah ◽  
Zahra Razmpour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Bonding Strength ◽  
Fuzzy Inference ◽  
Molar Ratio ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural

The main aim of this study is usability evaluation of different approaches, including response surface methodoloy, adaptive neuro-fuzzy inference system, and artificial neural network models to predict and evaluate the bonding strength of glued laminated timber (glulam) manufactured using walnut wood layers and a natural adhesive (oxidized starch adhesive), with respect to this fact that using the modified starch can decrease the formaldehyde emission. In this survey, four variables taken as the input data include the molar ratio of formaldehyde to urea (1.12–1.52), nanocellulose content (0%–4%, based on the dry weight of the adhesive), the mass ratio of the oxidized starch adhesive to the urea formaldehyde resin (30:70–70:30), and the press time (4–8 min). In order to find the best predictive performance of each selected evaluation approach, different membership functions were used. The optimal results were obtained when the molar ratio, nanocellulose content, mass ratio of the oxidised starch, and press time were set at 1.22, 3%, 70:30, and 7 min, respectively. Based on the performance criteria including root mean square error (RMSE) and mean absolute percentage error (MAPE) obtained from the modeling of response surface methodology, adaptive neuro-fuzzy inference network, and artificial neural network, it became evident that response surface methodology could offer a better prediction of the response with the lowest level of errors. Beside, artificial neural network and adaptive neuro-fuzzy inference system support the response surface methodology approach to evaluate bonding strength response with high precision as well as to determine the optimal point in fabrication of laminated products.

scholarly journals Neuro-fuzzy Modelling of a Linear Fresnel-type Solar Collector System as a Digital Twin

2021 ◽  
Author(s):  
William D. Chicaiza ◽  
Adolfo J. Sánchez ◽  
Antonio J. Gallego ◽  
Juan M. Escaño
Keyword(s):  
Solar Collector ◽  
Fuzzy Modelling ◽  
Digital Twin ◽  
Collector System ◽  
Neuro Fuzzy
Sign in / Sign up

Export Citation Format

Share Document