Genetic algorithm and artificial neural network model for prediction of discoloration dye from an electro-oxidation process in a press-type reactor

被引：15
|
作者：
Picos, Alain [1 ]
Peralta-Hernandez, Juan M. [1 ]
机构：
[1] Univ Guanajuato, Dept Quim, Div Ciencias Nat & Exactas, Campus Guanajuato, Guanajuato 36050, Gto, Mexico
来源：
WATER SCIENCE AND TECHNOLOGY | 2018年 / 78卷 / 04期
关键词：
artificial neural network; dye discoloration; electro-oxidation process; model prediction; press-type reactor; WASTE-WATER TREATMENT; SLUDGE BLANKET REACTOR; DOPED DIAMOND ANODE; ELECTROCATALYTIC DEGRADATION; ELECTROCHEMICAL OXIDATION; COD REMOVAL; AZO-DYE; OPTIMIZATION; FENTON; PERFORMANCE;
D O I：
10.2166/wst.2018.370
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
This study evaluates the effectiveness of an artificial neural network-genetic algorithm (ANN-GA) artificial intelligence (AI) model in the prediction of behavior and optimization of an electro-oxidation pilot press-type reactor, which treats a synthetic wastewater prepared with a dye. The ANN was built from real experimental data using as input the following variables: time, flow, j, dye concentration, and as output discoloration. The performance of the ANN was measured with MAPE (8.3868%), calculated from real and predicted values. The coupled AI model was used to find the best operational conditions: discoloration efficiency (above 90%) at j = 27 mA/cm(2) and dye concentration of 230 mg/L.
引用
收藏
页码：925 / 935
页数：11
相关论文
共 32 条
  • [21] Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network
    Xiong, Xin
    Gao, Feng
    Zhou, Keping
    Gao, Yuxu
    Yang, Chun
    GEOFLUIDS, 2021, 2021
  • [22] Artificial Neural Network-Genetic Algorithm based Prediction of Metformin Diffusion from Transdermal Films Based on Biopolymers for Diabetes Disease Treatment
    Rebouh, Samia
    Lefnaoui, Sonia
    Bouhedda, Mounir
    Yahoum, M. Madiha
    2019 3RD INTERNATIONAL CONFERENCE ON APPLIED AUTOMATION AND INDUSTRIAL DIAGNOSTICS (ICAAID 2019), 2019,
  • [23] Prediction and optimization of steroid hormone removal parameters from municipal wastewater by ultrasound probe using artificial neural network and genetic algorithm: a review
    Kia, Nasrin Mousavi
    Mohammadi, Farzaneh
    Nejhad, Hasti Hashemi
    DESALINATION AND WATER TREATMENT, 2022, 272 : 156 - 166
  • [24] An Artificial Neural Network and Genetic Algorithm Optimized Model for Biogas Production from Co-digestion of Seed Cake of Karanja and Cattle Dung
    Debabrata Barik
    S. Murugan
    Waste and Biomass Valorization, 2015, 6 : 1015 - 1027
  • [25] The Degradation of Sulfamethoxazole via the Fe2+/Ultraviolet/Sodium Percarbonate Advanced Oxidation Process: Performance, Mechanism, and Back-Propagate-Artificial Neural Network Prediction Model
    Chen, Juxiang
    Ruan, Chong
    Xie, Wanying
    Dai, Caiqiong
    Gao, Yuqiong
    Liao, Zhenliang
    Gao, Naiyun
    WATER, 2024, 16 (04)
  • [26] RankProd Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model that Revealed C1QTNF3 as a Biomarker for Prostate Cancer
    Hou, Qi
    Bing, Zhi-Tong
    Hu, Cheng
    Li, Mao-Yin
    Yang, Ke-Hu
    Mo, Zu
    Xie, Xiang-Wei
    Liao, Ji-Lin
    Lu, Yan
    Horie, Shigeo
    Lou, Ming-Wu
    EBIOMEDICINE, 2018, 32 : 234 - 244
  • [27] A new evaluation and prediction model of sound quality of high-speed permanent magnet motor based on genetic algorithm-radial basis function artificial neural network
    Hu, Kai
    Zhang, Guangming
    Zhang, Wenyi
    SCIENCE PROGRESS, 2021, 104 (03)
  • [28] Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 2): Optimization of Strain-Measurement Points by a Genetic Algorithm under Dynamic Loading
    Moon, Hyun Su
    Chun, Pang-Jo
    Kim, Moon Kyum
    Lim, Yun Mook
    APPLIED SCIENCES-BASEL, 2020, 10 (03):
  • [29] Process modeling and optimization of high yielding L-methioninase from a newly isolated Trichoderma harzianum using response surface methodology and artificial neural network coupled genetic algorithm
    Salim, Nisha
    Santhiagu, A.
    Joji, K.
    BIOCATALYSIS AND AGRICULTURAL BIOTECHNOLOGY, 2019, 17 : 299 - 308
  • [30] A kind of numerical model combined with genetic algorithm and back propagation neural network for creep-fatigue life prediction and optimization of double-layered annulus metal hydride reactor and verification of ASME-NH code
    Zhao, Ping
    Zeng, Xiangguo
    Kou, Huaqin
    Chen, Huayan
    INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2024, 54 : 1251 - 1263
1234