Treatment of textile dye via economic fungi/MCM-41 bio-based adsorbent: Application of neural network approach

Şimşek V., Yarbay R. Z., Marttin V., Gül Ü. D.

Journal of Cleaner Production, cilt.421, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 421
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.jclepro.2023.138448
  • Dergi Adı: Journal of Cleaner Production
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Chimica, Communication Abstracts, Compendex, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Artificial neural network (ANN) modeling, Bioadsorbent, Decolorization, MCM-41, Reactive red 120, Suillus collinitus
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

There has been an increased interest in developing environmentally friendly and cost-effective technologies for treating water contaminated with dyes found in textile industry wastewater. The adsorption mechanism allows the use of cheap adsorbents. In particular, using bioadsorbents developed with biocompatible materials is an environmentally friendly approach. This study aims to determine the textile dye Reactive Red 120 (RR120) removal capacity of a novel bio-based adsorbent developed by combining an effective catalyst and naturally available fungal wastes at the batch scale level. For this purpose, morphological and molecular identification of the fungal species collected from nature was determined. Later, the dried waste biomass of the fungal species and MCM-41 were combined in different ratios to produce a bio-based adsorbent (bioadsorbent). The effects of pH, the initial dye concentration, bioadsorbent composition, bioadsorbent amount, and the contact time on RR120 removal by the new bioadsorbent were determined and optimized using artificial neural network (ANN) modeling. According to the results of this study, the optimal conditions for dye biosorption were determined. The maximum RR120 removal was achieved using the 1.67 g/L of bioadsorbent having fungi/MCM-41 (1:1) at pH 4 with an initial dye concentration of 20 mg/L after 60 min. Maximally 94.75% of RR120 has been removed. The results of FT-IR, XRD, and SEM-EDX analyses proved the adsorption of dye on the bioadsorbent surface. According to the results, the bio-based adsorbent developed in this study suits contaminated water with textile dye as an effective, biocompatible, environmentally friendly, and inexpensive adsorbent.