Identifying the maturity of co-compost of olive mill waste and natural mineral materials: Modelling via ANN and multi-objective optimization

Aycan Dumenci N., Cagcag Yolcu Ö., Aydin Temel F., TURAN N. G.

BIORESOURCE TECHNOLOGY, cilt.338, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 338
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.biortech.2021.125516
  • Dergi Adı: BIORESOURCE TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Compendex, EMBASE, Environment Index, Food Science & Technology Abstracts, Geobase, INSPEC, MEDLINE, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Olive mill waste, Composting, Artificial neural networks, Response surface methodology, Genetic algorithm, SOIL, EMISSIONS
  • Marmara Üniversitesi Adresli: Evet

Özet

In this study, olive mill waste (OMW) and natural mineral amendments were co-composted to evaluate the compost maturity efficiency. The results were modelled by Feed-Forward Neural Networks (FF-NN) and ElmanRecurrent Neural Networks (ER-NN) and compared Response Surface Methodology (RSM). According to RSM produced a prediction error of more than 10% while Neural Networks (NNs) models were <2%. From, multiobjective optimization, the most suitable materials were expanded vermiculite and pumice with overall desirabilities of 0.60 and 0.56, respectively. The optimum amendment ratios were achieved with 14.3% of expanded vermiculite and 16.0% of pumice for OMW composting. Multivariate Analysis of Variance (MANOVA) results indicated that the materials had a strong effect on composting in parallel with the optimization results. NNs were predictors with superior properties to model the composting processes, can be used as modeling tools in many areas that are difficult and costly to perform new experiments.