Prediction and optimization of syngas production from napier grass air gasification via kinetic modelling and response surface methodology

In this research, a kinetic model was developed for Napier grass air gasification using Aspen Plus software and thereafter collated and validated with experimental results obtained from a lab-scale fluidized bed reactor. Herein, the model was further employed to investigate the effect of gasificatio...

Full description

Saved in:
Bibliographic Details
Main Authors: Obaid Qatan, Hesham Sadeq, Wan Ab Karim Ghani, Wan Azlina, Md Said, Mohamad Syazarudin
Format: Article
Published: Elsevier 2023
Online Access:http://psasir.upm.edu.my/id/eprint/110189/
https://linkinghub.elsevier.com/retrieve/pii/S0360544223002773
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.110189
record_format eprints
spelling my.upm.eprints.1101892024-09-03T07:53:04Z http://psasir.upm.edu.my/id/eprint/110189/ Prediction and optimization of syngas production from napier grass air gasification via kinetic modelling and response surface methodology Obaid Qatan, Hesham Sadeq Wan Ab Karim Ghani, Wan Azlina Md Said, Mohamad Syazarudin In this research, a kinetic model was developed for Napier grass air gasification using Aspen Plus software and thereafter collated and validated with experimental results obtained from a lab-scale fluidized bed reactor. Herein, the model was further employed to investigate the effect of gasification operating parameters, including temperature (650–850) °C, equivalence ratio (0.2–0.4), and moisture content (0–18) wt. on products’ yields and syngas quality. The model was segregated into four sections: the drying, devolatilization, combustion, and reduction sections. The outputs of tar, gas, and char from the devolatilization section were defined by external Microsoft Excel subroutine. Kinetic parameters were incorporated in the combustion and reduction sections to simulate tar cracking, oxidation, and reduction reactions. A good agreement was observed between the predicted and experimental results. Furthermore, response surface methodology (RSM) was employed to analyze the mutual effects of process variables and perform multi-objective optimization to maximize producer gas yield, higher heating value, carbon conversion efficiency, and cold gas efficiency by incorporating the predicted results from the developed kinetic model. The optimized syngas yield and HHV were 69.42 wt and 8.14 MJ/Nm3 at 850 °C, 0.3021, and 15.69 wt for temperature, ER, and moisture content, respectively. Elsevier 2023 Article PeerReviewed Obaid Qatan, Hesham Sadeq and Wan Ab Karim Ghani, Wan Azlina and Md Said, Mohamad Syazarudin (2023) Prediction and optimization of syngas production from napier grass air gasification via kinetic modelling and response surface methodology. Energy, 270. art. no. 126883. pp. 1-13. ISSN 0360-5442; ESSN: 1873-6785 https://linkinghub.elsevier.com/retrieve/pii/S0360544223002773 10.1016/j.energy.2023.126883
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description In this research, a kinetic model was developed for Napier grass air gasification using Aspen Plus software and thereafter collated and validated with experimental results obtained from a lab-scale fluidized bed reactor. Herein, the model was further employed to investigate the effect of gasification operating parameters, including temperature (650–850) °C, equivalence ratio (0.2–0.4), and moisture content (0–18) wt. on products’ yields and syngas quality. The model was segregated into four sections: the drying, devolatilization, combustion, and reduction sections. The outputs of tar, gas, and char from the devolatilization section were defined by external Microsoft Excel subroutine. Kinetic parameters were incorporated in the combustion and reduction sections to simulate tar cracking, oxidation, and reduction reactions. A good agreement was observed between the predicted and experimental results. Furthermore, response surface methodology (RSM) was employed to analyze the mutual effects of process variables and perform multi-objective optimization to maximize producer gas yield, higher heating value, carbon conversion efficiency, and cold gas efficiency by incorporating the predicted results from the developed kinetic model. The optimized syngas yield and HHV were 69.42 wt and 8.14 MJ/Nm3 at 850 °C, 0.3021, and 15.69 wt for temperature, ER, and moisture content, respectively.
format Article
author Obaid Qatan, Hesham Sadeq
Wan Ab Karim Ghani, Wan Azlina
Md Said, Mohamad Syazarudin
spellingShingle Obaid Qatan, Hesham Sadeq
Wan Ab Karim Ghani, Wan Azlina
Md Said, Mohamad Syazarudin
Prediction and optimization of syngas production from napier grass air gasification via kinetic modelling and response surface methodology
author_facet Obaid Qatan, Hesham Sadeq
Wan Ab Karim Ghani, Wan Azlina
Md Said, Mohamad Syazarudin
author_sort Obaid Qatan, Hesham Sadeq
title Prediction and optimization of syngas production from napier grass air gasification via kinetic modelling and response surface methodology
title_short Prediction and optimization of syngas production from napier grass air gasification via kinetic modelling and response surface methodology
title_full Prediction and optimization of syngas production from napier grass air gasification via kinetic modelling and response surface methodology
title_fullStr Prediction and optimization of syngas production from napier grass air gasification via kinetic modelling and response surface methodology
title_full_unstemmed Prediction and optimization of syngas production from napier grass air gasification via kinetic modelling and response surface methodology
title_sort prediction and optimization of syngas production from napier grass air gasification via kinetic modelling and response surface methodology
publisher Elsevier
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/110189/
https://linkinghub.elsevier.com/retrieve/pii/S0360544223002773
_version_ 1811686057392996352
score 13.211869