Characterization of biodiesel based on plastic pyrolysis oil (PPO) and coconut oil: Performance and emission analysis using RSM-ANN approach

Afzal A., Roy R., Koshy C., Alex Y., Abbas M., CÜCE E., ...Daha Fazla

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, cilt.56, ss.103046, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 56
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.seta.2023.103046
  • Dergi Adı: SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Geobase, INSPEC
  • Sayfa Sayıları: ss.103046
  • Anahtar Kelimeler: Biodiesel, Plastic pyrolysis, Transesterification, Virgin coconut oil
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

    The work presents comparative study of the production and characterisation of sustainable biodiesel fuel made from waste plastics and virgin coconut oil. For characterisation, various chemical tests were performed, including Fourier Transform Infrared (FTIR) spectroscopy, Thermogravimetric analysis (TGA), and Gas chromatography–mass spectrometry (GCMS). The emissions and performance of this fuel were investigated to determine whether it could be utilised in diesel engines without modification. RSM (response surface method) was used to design the experiments. ANN (artificial neural network) was used to model the relationship between the input and output parameters. It was seen that, 20 % hybrid blend with diesel showed a better output (33 %) than 10 % and 30 % blend. For 75 % of load, a value of 0.12 % (minimum) CO emission was obtained for the same blend with diesel fuel. Low proportion levels blends (10 %) have less amount of oxygen content, hence reduced in NOx (400 ppm). The ANN and RSM models were found to be fitting correctly with the experimental readings, with R2 ratios varying from 90 % to 93.5 %, respectively. The outcomes demonstrated that RSM and ANN were excellent modelling techniques with good accuracy. In addition, ANN's prediction performance (R2 = 0.9978 for BTE) was somewhat better than RSM's (R2 = 0.960 for BTE).

      The work presents comparative study of the production and characterisation of sustainable biodiesel fuel made from waste plastics and virgin coconut oil. For characterisation, various chemical tests were performed, including Fourier Transform Infrared (FTIR) spectroscopy, Thermogravimetric analysis (TGA), and Gas chromatography–mass spectrometry (GCMS). The emissions and performance of this fuel were investigated to determine whether it could be utilised in diesel engines without modification. RSM (response surface method) was used to design the experiments. ANN (artificial neural network) was used to model the relationship between the input and output parameters. It was seen that, 20 % hybrid blend with diesel showed a better output (33 %) than 10 % and 30 % blend. For 75 % of load, a value of 0.12 % (minimum) CO emission was obtained for the same blend with diesel fuel. Low proportion levels blends (10 %) have less amount of oxygen content, hence reduced in NOx (400 ppm). The ANN and RSM models were found to be fitting correctly with the experimental readings, with R2 ratios varying from 90 % to 93.5 %, respectively. The outcomes demonstrated that RSM and ANN were excellent modelling techniques with good accuracy. In addition, ANN's prediction performance (R2 = 0.9978 for BTE) was somewhat better than RSM's (R2 = 0.960 for BTE).