Degradation of ticarcillin by subcritial water oxidation method: Application of response surface methodology and artificial neural network modeling (vol 53, pg 975, 2018)

被引:0
|
作者
Yabalak, Erdal
机构
关键词
D O I
10.1080/10934529.2018.1513491
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
引用
收藏
页码:1039 / 1039
页数:1
相关论文
共 50 条
  • [21] Modeling and optimization of Terminalia catappa L. kernel oil extraction using response surface methodology and artificial neural network (vol 4, pg 1, 2020)
    Agu, C. M.
    ARTIFICIAL INTELLIGENCE IN AGRICULTURE, 2021, 5 : 304 - 304
  • [22] Response Surface Methodology and Artificial Neural Network Modeling for the Removal of Volatile Organic Compounds in Biotrickling Filters
    Hong, Tianqiu
    Wei, Lin
    Cui, Kangping
    Dong, Yugang
    Luo, Lei
    Zhang, Tingting
    Li, Ruolan
    Li, Ziyue
    Tang, Yiming
    WATER AIR AND SOIL POLLUTION, 2023, 234 (10):
  • [23] Modeling and optimization of coal oil agglomeration using response surface methodology and artificial neural network approaches
    Yadav, Anand Mohan
    Nikkam, Suresh
    Gajbhiye, Pratima
    Tyeb, Majid Hasan
    INTERNATIONAL JOURNAL OF MINERAL PROCESSING, 2017, 163 : 55 - 63
  • [24] CARBON DIOXIDE REFORMING OF METHANE TO SYNGAS: MODELING USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK
    Amin, Nor Aishah Saidina
    Yusof, Khairiyah Mohd
    Isha, Ruzinah
    JURNAL TEKNOLOGI, 2005, 43
  • [25] Response Surface Methodology and Artificial Neural Network Modeling for the Removal of Volatile Organic Compounds in Biotrickling Filters
    Tianqiu Hong
    Lin Wei
    Kangping Cui
    Yugang Dong
    Lei Luo
    Tingting Zhang
    Ruolan Li
    Ziyue Li
    Yiming Tang
    Water, Air, & Soil Pollution, 2023, 234
  • [26] MODELING AND OPTIMIZATION OF ETHANOL FERMENTATION USING Saccharomyces cerevisiae: RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK
    Esfahanian, Mehri
    Nikzad, Maryam
    Najafpour, Ghasem
    Ghoreyshi, Ali Asghar
    CHEMICAL INDUSTRY & CHEMICAL ENGINEERING QUARTERLY, 2013, 19 (02) : 241 - 252
  • [27] Modeling of fixed-bed dye adsorption using response surface methodology and artificial neural network
    Schio, R. R.
    Salau, N. P. G.
    Mallmann, E. S.
    Dotto, G. L.
    CHEMICAL ENGINEERING COMMUNICATIONS, 2021, 208 (08) : 1081 - 1092
  • [28] A comparative study of response surface methodology and artificial neural network based algorithm genetic for modeling and optimization of EP/US/GAC oxidation process in dexamethasone degradation: Application for real wastewater, electrical energy consumption
    Salari, Mehdi
    Alahabadi, Ahmad
    Rahmani-Sani, Abolfazl
    Miri, Mohammad
    Yazdani-Aval, Mohsen
    Lotfi, Hadi
    Saghi, Mohammad Hossien
    Rastegar, Ayoob
    Sepehr, Mohammad Noori
    Darvishmotevalli, Mohammad
    Chemosphere, 2024, 349
  • [29] Application of ultrasound-assisted and subcritical water oxidation methods in the mineralisation of Procion Crimson H-EXL using response surface methodology and artificial neural network
    Yabalak, Erdal
    Kulekci, Busra
    Gizir, A. Murat
    JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH PART A-TOXIC/HAZARDOUS SUBSTANCES & ENVIRONMENTAL ENGINEERING, 2019, 54 (14): : 1412 - 1422
  • [30] Bioengineering for multiple PAHs degradation for contaminated sediments: Response surface methodology (RSM) and artificial neural network (ANN)
    Sachaniya, Bhumi K.
    Gosai, Haren B.
    Panseriya, Haresh Z.
    Dave, Bharti P.
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2020, 202