Dynamic Modeling for Temperature Prediction in a Fluidized Bed Biomass Gasification Process

2021 ◽  
Author(s):  
Ibtihaj Khurram Faridi ◽  
Evangelos Tsotsas ◽  
Wolfram Heineken ◽  
Marcus Koegler ◽  
Abdolreza Kharaghani
Keyword(s):  
Fluidized Bed ◽  
Dynamic Modeling ◽  
Biomass Gasification ◽  
Temperature Prediction ◽  
Gasification Process

scholarly journals Experimental analysis of a bubbling fluidized bed gasification plant fed by biomass: Design, implementation and validation of the control system

2021 ◽  
Vol 239 ◽  
pp. 00007
Author(s):  
Carlos Vargas-Salgado ◽  
Lina Montuori ◽  
Manuel Alcázar-Ortega
Keyword(s):  
Control System ◽  
Fluidized Bed ◽  
Biomass Gasification ◽  
Screw Conveyor ◽  
Air Velocity ◽  
Gasification Process ◽  
Control Functions ◽  
Bubbling Fluidized Bed ◽  
Measurement Devices ◽  
Gasification Plant

Despite being a renewable source, biomass as fuel for power generation is still not completely exploited. In biomass gasification plants, control operations are crucial for the proper management of the plant. This paper describes the results of a regulation control applied to an experimental biomass bubbling fluidized bed (BFB) gasification plant. The aim of implementing the system is to improve the biomass gasification process, increasing the efficiency and ensuring the safety in the plant operation. The equivalence ratio (ER) is one of the main parameters in a gasification process. To improve the ER, the airflow input is controlled, measuring the air velocity through an anemometer. On the other hand, the biomass flow is controlled modifying the speed of the screw conveyor using an inverter for regulating the frequency of its electric motor. A PLC is used for programming the instructions to implement control functions and to store the data given by the measurement devices. Once implemented the control system, the biomass gasification plant could work either; manually o automatically, allowing to adjust ER, increasing efficiency of the process. Finally, some tests are done to validate the control system, using the acquired data to improve the process.

scholarly journals Solubility of Tar Model Compounds in Various Solvents for Tar Removal in a Dual Fluidized Bed Biomass Gasification Process

2021 ◽  
Vol 798 (1) ◽  
pp. 012010
Author(s):  
Pimnara Tonpakdee ◽  
Janjira Hongrapipat ◽  
Vilailuck Siriwongrungson ◽  
Shusheng Pang ◽  
Reinhard Rauch ◽  
...  
Keyword(s):  
Fluidized Bed ◽  
Biomass Gasification ◽  
Model Compounds ◽  
Tar Removal ◽  
Gasification Process ◽  
Dual Fluidized Bed

Simulation of Enriched Air-Steam Biomass Gasification in a Bubbling Fluidized Bed Gasifier

2014 ◽  
Vol 699 ◽  
pp. 510-515
Author(s):  
Miao Miao Niu ◽  
Ya Ji Huang ◽  
Bao Sheng Jin
Keyword(s):  
Fluidized Bed ◽  
Steam Injection ◽  
Biomass Gasification ◽  
Carbon Conversion ◽  
Gasification Process ◽  
Cold Gas Efficiency ◽  
Oxygen Percentage ◽  
Bubbling Fluidized Bed ◽  
Gas Efficiency ◽  
Cold Gas

A model was developed for the enriched air-steam biomass gasification in a bubbling fluidized bed (BFB) gasifier using Aspen Plus. Restricted equilibrium method was used to eliminate the deviation caused by the diffusion effect of gas-particle. The model has been divided into three stages (drying and pyrolysis, partial combustion and gasification) for predicting the gasifier performance. Simulation results for gas composition, carbon conversion and cold gas efficiency versus oxygen percentage and steam to biomass ratio (S/B) were compared with the experimental results. Higher oxygen percentage improves the gasification process, increases the production of H2 and CO and results in better gasification efficiency. With increasing oxygen percentage, the production of CO2 and CH4 show decreasing trends. Steam injection enhances the H2 and CO2 production but decreases CO and CH4 production. Carbon conversion presents a slight decrease trend over the S/B range, while cold gas efficiency is first constant and then decreased.

scholarly journals Hydrogen Production by Fluidized Bed Reactors: A Quantitative Perspective Using the Supervised Machine Learning Approach

J ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 266-287
Author(s):  
Zheng Lian ◽  
Yixiao Wang ◽  
Xiyue Zhang ◽  
Abubakar Yusuf ◽  
Lord Famiyeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hydrogen Production ◽  
Fluidized Bed ◽  
Biomass Gasification ◽  
Supervised Machine Learning ◽  
Fluidized Bed Reactors ◽  
Hydrogen Yield ◽  
Carbon Conversion ◽  
Operational Parameters ◽  
Operational Conditions

The current hydrogen generation technologies, especially biomass gasification using fluidized bed reactors (FBRs), were rigorously reviewed. There are involute operational parameters in a fluidized bed gasifier that determine the anticipated outcomes for hydrogen production purposes. However, limited reviews are present that link these parametric conditions with the corresponding performances based on experimental data collection. Using the constructed artificial neural networks (ANNs) as the supervised machine learning algorithm for data training, the operational parameters from 52 literature reports were utilized to perform both the qualitative and quantitative assessments of the performance, such as the hydrogen yield (HY), hydrogen content (HC) and carbon conversion efficiency (CCE). Seven types of operational parameters, including the steam-to-biomass ratio (SBR), equivalent ratio (ER), temperature, particle size of the feedstock, residence time, lower heating value (LHV) and carbon content (CC), were closely investigated. Six binary parameters have been identified to be statistically significant to the performance parameters (hydrogen yield (HY)), hydrogen content (HC) and carbon conversion efficiency (CCE)) by analysis of variance (ANOVA). The optimal operational conditions derived from the machine leaning were recommended according to the needs of the outcomes. This review may provide helpful insights for researchers to comprehensively consider the operational conditions in order to achieve high hydrogen production using fluidized bed reactors during biomass gasification.

Simulation of biomass gasification in bubbling fluidized bed reactor using aspen plus®

2021 ◽  
Vol 235 ◽  
pp. 113981
Author(s):  
M. Puig-Gamero ◽  
D.T. Pio ◽  
L.A.C. Tarelho ◽  
P. Sánchez ◽  
L. Sanchez-Silva
Keyword(s):  
Fluidized Bed ◽  
Aspen Plus ◽  
Fluidized Bed Reactor ◽  
Biomass Gasification ◽  
Bubbling Fluidized Bed
Sign in / Sign up

Export Citation Format

Share Document