[HTML][HTML] Machine learning applications in biomass pyrolysis: from biorefinery to end-of-life product management

DA Akinpelu, OA Adekoya, PO Oladoye… - Digital Chemical …, 2023 - Elsevier
The thermochemical conversion of biomass is a promising technology due to its cost-
effectiveness and feedstock flexibility, with pyrolysis being a particularly noteworthy method …

Tar formation in gasification systems: a holistic review of remediation approaches and removal methods

A Jayanarasimhan, RM Pathak, AM Shivapuji… - ACS omega, 2024 - ACS Publications
Gasification is an advanced thermochemical process that converts carbonaceous feedstock
into syngas, a mixture of hydrogen, carbon monoxide, and other gases. However, the …

Prediction of syngas properties of biomass steam gasification in fluidized bed based on machine learning method

P Xue, T Chen, X Huang, Q Hu, J Hu, H Zhang… - International Journal of …, 2024 - Elsevier
Steam gasification of biomass is a promising technology to produce hydrogen-rich syngas.
While the complex correlation between gasification process and syngas properties has not …

[HTML][HTML] Development of a neural network model predictive controller for the fluidized bed biomass gasification process

IK Faridi, E Tsotsas, W Heineken, M Koegler… - Chemical Engineering …, 2024 - Elsevier
In this study, an advanced controller for the fluidized bed biomass gasification (FBG) process
is proposed. The controller is based on the model predictive control method, utilizing a long …

Physics-informed dynamic mode decomposition for short-term and long-term prediction of gas-solid flows

D Li, B Zhao, S Lu, J Wang - Chemical Engineering Science, 2024 - Elsevier
Integration of physics principles with data-driven methods has attracted great attention in
recent few years. In this study, a physics-informed dynamic mode decomposition (piDMD) …

A novel combined model for heat load prediction in district heating systems

Y Wang, Z Li, J Liu, Y Zhao, S Sun - Applied Thermal Engineering, 2023 - Elsevier
Accurate heat load prediction is essential for improving the operational efficiency of district
heating systems (DHSs). Numerous heat load prediction models have been proposed to …

A data-driven method for fast predicting the long-term hydrodynamics of gas–solid flows: Optimized dynamic mode decomposition with control

D Li, B Zhao, S Lu, J Wang - Physics of Fluids, 2024 - pubs.aip.org
Data-driven methods are of great interest in studying the hydrodynamics of gas–solid flows.
In this paper, we developed an optimized dynamic mode decomposition with control (DMDc) …

Long short-term memory and bidirectional long short-term memory modeling and prediction of hexavalent and total chromium removal capacity kinetics of Cupressus …

JC Cruz-Victoria, AR Netzahuatl-Muñoz… - Sustainability, 2024 - mdpi.com
Hexavalent chromium [Cr (VI)] is a high-priority environmental pollutant because of its
toxicity and potential to contaminate water sources. Biosorption, using low-cost biomaterials …

A comparative assessment of CFD based LSTM and GRU for hydrodynamic predictions of gas-solid fluidized bed

M Nadda, K Singh, S Roy, A Yadav - Powder Technology, 2024 - Elsevier
Numerical simulation of large-scale fluidized beds presents significant challenges for both
scaling up the simulations and evaluating their performance, as full-scale computational …

Investigations into the flow dynamics of mixed biomass particles in a fluidized bed through Hilbert-Huang transformation and data-driven modelling

B Qi, Y Yan, W Zhang - Particuology, 2024 - Elsevier
Flow dynamics of binary particles are investigated to realize the monitoring and optimization
of fluidized beds. It is a challenge to accurately classify the mass fraction of mixed biomass …