Biomass fast pyrolysis prediction model through data-based prediction models coupling with CPFD simulation

Hydrodynamics Behavior of Ternary Biomass Mixtures with Silica Sand in Fluidized Beds: Experiment and Artificial Neural Network Model Development

“…The combination of tansig and purelin transfer functions gave the best result in the previous studies. , The Levenberg–Marquardt training algorithm was applied to adjust the MLP parameters, weights, and biases throughout the training stage. In addition, the 2137 experimental data sets (input and output) were normalized in the range of 0 to 1 and then used to train the MLP model. , …”

Development and Evaluation of Deep Learning Models for Predicting Instantaneous Mass Flow Rates of Biomass Fast Pyrolysis in Bubbling Fluidized Beds

“…With significantly less calculation effort but still high accuracy, time-averaged species distributions in the reactor at various temperatures were forecasted. Kim et al 13 T h i s c o n t e n t i s proposed a novel numerical approach that coupled ML with computational particle fluid dynamics (CPFD) simulation. Predicted product yields generated by this method were in good accord with CPFD results, outperforming lumped process models.…”

“…Machine learning (ML) has recently emerged as a critical technique to investigate multiphase flows and reactors owing to its fast advancement of computational theory and capability, − and shows great potential in speed up CFD modeling of biomass fast pyrolysis in fluidized-bed reactors. − Zhong et al designed a reduced-order model based on a back-propagation network, which trained CFD data from multifluid model (MFM) simulations. With significantly less calculation effort but still high accuracy, time-averaged species distributions in the reactor at various temperatures were forecasted.…”

Prediction of instantaneous yield of bio-oil in fluidized biomass pyrolysis using long short-term memory network based on computational fluid dynamics data

“…Specially for biomass fast pyrolysis in fluidized-bed reactors,Zhong et al (2020) developed back-propagation (BP) ANNs based on CFD data from multi-fluid model (MFM) simulation. Time-averaged species distributions in the reactor at different temperatures were predicted with much less computational time but good accuracy Kim et al (2022). trained eight ML models based on computational particle fluid dynamics (CPFD)…”