EconPapers    
Economics at your fingertips  
 

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

Furkan Kartal and Uğur Özveren

Energy, 2020, vol. 209, issue C

Abstract: Aspen Plus® 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® 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®. 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 R2 > 0.99 for all datasets.

Keywords: Biomass gasification; Deep learning; Circulating fluidized bed gasifier; Artificial neural network; Lower heating value; Aspen plus® (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544220315656
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:209:y:2020:i:c:s0360544220315656

DOI: 10.1016/j.energy.2020.118457

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2022-05-07
Handle: RePEc:eee:energy:v:209:y:2020:i:c:s0360544220315656