A multilayer perceptron-based prediction of ammonium adsorption on zeolite from landfill leachate: Batch and column studies

Temel F. A., Yolcu Ö., Kuleyin A.

JOURNAL OF HAZARDOUS MATERIALS, cilt.410, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 410
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.jhazmat.2020.124670
  • Dergi Adı: JOURNAL OF HAZARDOUS MATERIALS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, Environment Index, Food Science & Technology Abstracts, Geobase, INSPEC, MEDLINE, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Adsorption, Ammonium, Multilayer perceptron neural network, Prediction, Response surface methodology, AQUEOUS-SOLUTION, REMOVAL, BEHAVIOR, ISOTHERM
  • Marmara Üniversitesi Adresli: Hayır

Özet

In this study, multilayer perceptron (MLP) artificial neural network was used to predict the adsorption rate of ammonium on zeolite. pH, inlet ammonium concentration, contact time, temperature, dosage of adsorbent, agitation speed, and particle size in the batch experiments were used as independent variables while flow rate and particle size in column mode were investigated. In MLP application, different architecture structures were tried and the architecture structures with the highest predictive performance were determined. To comparatively evaluate the predictive capabilities of MLP based prediction tool, Response Surface Methodology (RSM) was utilized. When the results were evaluated with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values (<1%) for almost all experiments, it was seen that MLP-based prediction tool produces better predictions than RSM. The scatter plots showed that predictions and actual values were quite compatible. Both regression and determination coefficients were interpreted by creating a regression of the predictions against the actual values and these coefficients were obtained as pretty close to 1. The outstanding performance of MLP in out-of-sample data sets without the need for additional experiment demonstrate that MLP can be effectively and reliably used in cases where experimental setups are difficult or costly.