A deep learning approach for prediction of syngas lower heating value from CFB gasifier in Aspen plus®

Kartal F., ÖZVEREN U.

Energy, cilt.209, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 209
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.energy.2020.118457
  • Dergi Adı: Energy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Biomass gasification, Deep learning, Circulating fluidized bed gasifier, Artificial neural network, Lower heating value, Aspen plus (R), GIBBS ENERGY MINIMIZATION, FLUIDIZED-BED REACTOR, BIOMASS GASIFICATION, HYDROGEN-PRODUCTION, STEAM GASIFICATION, CO-GASIFICATION, PART I, SIMULATION, MODEL, EQUILIBRIUM
  • Marmara Üniversitesi Adresli: Evet

Özet

Aspen Plus (R) is one of the practicable software for investigation of the biomass gasification characteristics. Also, artificial neural networks (ANN) as a deep learning approach are often used in the prediction of parameters such as syngas composition, lower heating value (LHV), exergy, etc. However, to our best knowledge, a universal deep learning model based on the thermodynamic equilibrium approach to predict LHV of syngas in circulating fluidized bed (CFB) gasifier is not available in literature yet. In this paper, a unique CFB gasifier model was developed in Aspen Plus (R) as a tool to create a total of over 1 million datasets for the training of a deep learning model that predicts the LHV of the syngas. The CFB gasifier model was found to be in agreement with the results when compared with the experimental data in the literature. 56 biomass with various elemental and proximate properties were gasified in a newly developed CFB gasifier model under different operating conditions by using sensitivity analysis in Aspen Plus (R). A novel artificial neural network model, which is regularized with Levenberg-Marquardt algorithm was used as a deep learning model with a 6-12-1 tangent sigmoid architecture to predict LHV of syngas in circulating fluidized bed (CFB) gasifier, requiring minimal specificity as compared to commercial simulators require significant modelling effort and test runs. Results showed that the estimated LHV of the syngas in an agreement with the calculated values. The coefficient of determination score was calculated as R-2 > 0.99 for all datasets. (C) 2020 Elsevier Ltd. All rights reserved.