Integrated neural network and Aspen Plus model for entrained flow gasification kinetics investigation Original scientific paper
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Abstract
Entrained flow gasification is a well-established technology, however, the main obstacle in process design is complex gasification mechanism, since numerous phenomena at extreme process conditions take place simultaneously. This study is focused on integrated thermodynamic and artificial neural network approach (ANN) for entrained flow gasification kinetics investigation. Data on 102 feedstock materials composition was used in AspenPlus gasification simulation, where sensitivity analysis was performed for different equivalence ratio (0.1-0.7) and gasification temperature (1200-1500°C) values. For analyzed materials, optimal equivalence ratio range exist (usually 0.3-0.4), maximizing gasification efficiency. Obtained results were used in ANN development for each output variable (syngas composition, efficiency, heating value and carbon conversion). Matlab algorithm was used for determination of optimal number of neurons (1-20 range) in each ANN. High R2 values (>0.99) for all models suggested good agreement between simulated and predicted values. Genetic algorithm-based optimization studies for maximization of hydrogen content and cold gas efficiency result in mean ER value of 0.35 and 0.41, respectively, at temperature of 1200°C. Yoon interpretation method was used for quantifying relative impacts of each input variable on syngas content and gasification efficiency. Proposed approach represents a powerful tool which can facilitate investigation of entrained flow gasification and process design.
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Funding data
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Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja
Grant numbers 451-03-65/2024-03/200134
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