ANNs modeling of the normal and modified biosorbent capacities using two outputs in the removal of Pb (II)

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Oguz E.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, cilt.20, sa.5, ss.5143-5154, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 5
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s13762-022-04445-9
  • Dergi Adı: INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Compendex, Environment Index, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.5143-5154
  • Anahtar Kelimeler: Abies bornmulleriana cone, Biosorption, Modified biosorbent, Pb (II), ANNs, FIXED-BED COLUMN, NEURAL-NETWORK ANN, HEAVY-METAL IONS, AQUEOUS-SOLUTION, WASTE BIOMASS, BIOSORPTION, LEAD, ADSORPTION, PB(II), EQUILIBRIUM
  • Atatürk Üniversitesi Adresli: Evet

Özet

This study accurately predicts the biosorbent capacities of normal and modified Abies bornmulleriana cone in removing Pb (II) ions from synthetic aqueous waters using artificial neural networks (ANNs) of two outputs and a single model structure. In the present research, the neural networks' input parameters are biosorbate concentration, biosorption time, particle size, biosorbent amount, agitation rate, pH, and temperature. The biosorbent capacities of Pb (II) ions were selected as the experimental responses. The optimal network design is (7:7-9-2:2). Sensitive analysis was employed to reveal the relative relationship among model input parameters of ANNs. The biosorption time was determined as the most critical parameter affecting the biosorbent capacities of Pb (II) ions. The ANNs model employed in Pb (II) removing using normal and modified Abies bornmulleriana cone had determination coefficients of (0.982, 0.976), standard deviation ratios of (0.13, 0.15), root-mean-square errors of (3.59, 5.78), and mean absolute errors of (0.28, 1.16). The results acquired from the network signify that the ANNs model of two outputs could accurately predict the normal and modified biosorbent capacities in the removal of Pb (II) ions from aqueous water.