Modeling and optimization of reactive cotton dyeing using response surface methodology combined with artificial neural network and particle swarm techniques

被引：0
|
作者：
Jorge Marcos Rosa
Flavio Guerhardt
Silvestre Eduardo Rocha Ribeiro Júnior
Peterson A. Belan
Gustavo A. Lima
José Carlos Curvelo Santana
Fernando Tobal Berssaneti
Elias Basile Tambourgi
Rosangela Maria Vanale
Sidnei Alves de Araújo
机构：
[1] State University of Campinas,Industrial Engineering Post Graduate Program
[2] Polytechnic School of University of São Paulo,Informatics and Knowledge Management Post Graduate Program
[3] USP,undefined
[4] SENAI Antoine Skaf – Textile,undefined
[5] Nove de Julho University,undefined
[6] Nove de Julho University,undefined
[7] Federal University of ABC,undefined
[8] Sorocaba Technology Park,undefined
来源：
Clean Technologies and Environmental Policy | 2021年 / 23卷
关键词：
Dyeing of cotton; Reactive dyestuff; Coloristic intensity; Response surface methodology; Artificial neural network; Particle swarm optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：2357 / 2367
页数：10
相关论文
共 50 条
  • [21] Modeling and optimization of uricase production from a novel Pseudomonas mosselii using response surface methodology and artificial neural network
    Dudala, Sai Sushma
    Venkateswarulu, T. C.
    Narayana, Venkata A.
    Babu, John D.
    BIOMASS CONVERSION AND BIOREFINERY, 2023, : 21865 - 21880
  • [22] Estimation of Number of Flight Using Particle Swarm Optimization and Artificial Neural Network
    Ozmen, Ebru Pekel
    Pekel, Engin
    ADCAIJ-ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL, 2019, 8 (03): : 27 - 33
  • [23] Autoignition Temperature Prediction Using an Artificial Neural Network with Particle Swarm Optimization
    Lazzus, Juan A.
    INTERNATIONAL JOURNAL OF THERMOPHYSICS, 2011, 32 (05) : 957 - 973
  • [24] Autoignition Temperature Prediction Using an Artificial Neural Network with Particle Swarm Optimization
    Juan A. Lazzús
    International Journal of Thermophysics, 2011, 32
  • [25] Optimizing Artificial Neural Network for Functions Approximation Using Particle Swarm Optimization
    Zaghloul, Lina
    Zaghloul, Rawan
    Hamdan, Mohammad
    ADVANCES IN SWARM INTELLIGENCE, ICSI 2021, PT I, 2021, 12689 : 223 - 231
  • [26] Modeling and optimization of tartaric acid reactive extraction from aqueous solutions: A comparison between response surface methodology and artificial neural network
    Marchitan, N.
    Cojocaru, C.
    Mereuta, A.
    Duca, Gh
    Cretescu, I.
    Gonta, M.
    SEPARATION AND PURIFICATION TECHNOLOGY, 2010, 75 (03) : 273 - 285
  • [27] Optimization of biohydrogen production by Enterobacter species using artificial neural network and response surface methodology
    Karthic, P.
    Joseph, Shiny
    Arun, Naveenji
    Kumaravel, S.
    JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2013, 5 (03)
  • [28] The algorithms optimization of artificial neural network based on particle swarm
    Yang, Xin-Quan, 1600, Bentham Science Publishers B.V., P.O. Box 294, Bussum, 1400 AG, Netherlands (08):
  • [29] Modeling of Alkali Pretreatment of Rice Husk Using Response Surface Methodology and Artificial Neural Network
    Nikzad, Maryam
    Movagharnejad, Kamyar
    Talebnia, Farid
    Aghaiy, Ziba
    Mighani, Moein
    CHEMICAL ENGINEERING COMMUNICATIONS, 2015, 202 (06) : 728 - 738
  • [30] Implementation of Neural Network in Particle Swarm Optimization (PSO) Techniques
    Chaurasia, Suhashini
    Daware, Shubhangi
    IAMA: 2009 INTERNATIONAL CONFERENCE ON INTELLIGENT AGENT & MULTI-AGENT SYSTEMS, 2009, : 109 - 110
12345