Flowsheet Modeling and Simulation of Biomass Steam Gasification for Hydrogen Production

Inayat, Abrar; Raza, Mohsin; Khan, Zakir; Ghenaï, Chaouki; Aslam, Muhammad; Shahbaz, Muhammad; Ayoub, Muhammad

doi:10.1002/ceat.201900490

Cited by 23 publications

(4 citation statements)

References 126 publications

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“…Many oil-producing countries, including Oman, are implementing a long-term strategic policy to establish hydrogen energy system in order to diversify their petroleum-dominated economy for sustainable development (Ansari, 2022) Although hydrogen is the most abundant element in universe, it rarely exists alone naturally on Earth. Most of hydrogen on earth exists in biomass, water (H 2 O), methane (CH 4 ), coal, and petroleum, from which H 2 can be produced (Seyitoglu et al, 2017;Inayat et al, 2020;Hjeij et al, 2022). To date, steam methane reforming (SMR) is the most economic and primary method for H 2 production.…”

Section: Introductionmentioning

confidence: 99%

Numerical evaluation of hydrogen production by steam reforming of natural gas

Chen

Al-Subhi

Al-Rajhi

et al. 2022

Adv. Geo-Energy Res.

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Industry-scale hydrogen is mainly produced by steam methane reforming (SMR), which uses natural gas as the feedstock and fuel and co-produces CO 2 . This study aims to numerically evaluate hydrogen production by SMR under various reacting conditions. Unlike the previous studies with limited scenarios, the performance of SMR is continuously evaluated in a high-dimensional input-parameter space. The SMR plant including a combustor, a reformer, and a water-gas shifter is modeled in Aspen HYSYS software. The four key parameters, including methane fraction of the feedstock, reformer pressure and temperature, and shifter temperature, are treated uncertain and 50 samples are drawn from a four-dimensional parameter space defined by their ranges. Each sample is input to HYSYS model and mass ratio of each component in product streams is obtained as the output variables. Based on the 50 pairs of input-output data, response surfaces of the outputs are developed to surrogate HYSYS models. The fast response surface models are then used to calculate global sensitivity indices and evaluate SMR processes. Results show the reformer performance is controlled by temperature rather than pressure, and a temperature higher than 900 • C can maximize the reaction rate. The water-gas shifting reaction is inhibited in the reformer but significantly enhanced in the shifter. Hydrogen is mainly produced in the reformer while the major function of the shifter is to convert CO to nontoxic CO 2 .

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Section: Introductionmentioning

confidence: 99%

Numerical evaluation of hydrogen production by steam reforming of natural gas

Chen

Al-Subhi

Al-Rajhi

et al. 2022

Adv. Geo-Energy Res.

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“…In the literature, there are several excellent books on biomass, SSW and MSW management and the fundamentals of incineration, pyrolysis, and gasification technologies (see, e.g., [1][2][3][4]), as well as multiple reviews on feedstock pretreatment/aftertreatment, advanced autothermal and allothermal, catalytic and noncatalytic gasifier designs and performances [31,[45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61], and downstream technologies and syngas applications ( [1][2][3][4]28]). We refer an interested reader to these references and do not consider these issues herein.…”

Section: Introductionmentioning

confidence: 99%

Organic Waste Gasification: A Selective Review

Фролов

2021

Fuels

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This review considers the selective studies on environmentally friendly, combustion-free, allothermal, atmospheric-pressure, noncatalytic, direct H2O/CO2 gasification of organic feedstocks like biomass, sewage sludge wastes (SSW) and municipal solid wastes (MSW) to demonstrate the pros and cons of the approaches and provide future perspectives. The environmental friendliness of H2O/CO2 gasification is well known as it is accompanied by considerably less harmful emissions into the environment as compared to O2/air gasification. Comparative analysis of the various gasification technologies includes low-temperature H2O/CO2 gasification at temperatures up to 1000 °C, high-temperature plasma- and solar-assisted H2O/CO2 gasification at temperatures above 1200 °C, and an innovative gasification technology applying ultra-superheated steam (USS) with temperatures above 2000 °C obtained by pulsed or continuous gaseous detonations. Analysis shows that in terms of such characteristics as the carbon conversion efficiency (CCE), tar and char content, and the content of harmful by-products the plasma and detonation USS gasification technologies are most promising. However, as compared with plasma gasification, detonation USS gasification does not need enormous electric power with unnecessary and energy-consuming gas–plasma transition.

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“…The procedures relying on thermochemical conversion can be classified as liquefaction, pyrolysis, combustion and gasification [12,13] and, amongst them, biomass gasification has been proved as a promising green technology toward conversion of different feedstocks to various energy products [8][9][10]. Through this complex system, lignocellulosic materials are transformed to a more valuable gas, investigated as syngas by series reactions at high temperatures [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

et al. 2021

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In order to accurately anticipate the proficiency of downdraft biomass gasification linked with a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network (ANN) approach is established to estimate the specific mass flow rate of the biohydrogen output of the plant based on different types of biomasses and diverse operating parameters. The factors considered as inputs to the models are elemental and proximate analysis compositions as well as the operating parameters. The model structure includes one layer for input, a hidden layer and output layer. One thousand eight hundred samples derived from the simulation of 50 various feedstocks in different operating situations were utilized to train the developed ANN model. The established ANN in the case of product biohydrogen presents satisfactory agreement with input data: absolute fraction of variance (R2) is more than 0.999 and root mean square error (RMSE) is lower than 0.25. In addition, the relative impact of biomass properties and operating parameters on output are studied. At the end, to have a comprehensive evaluation, variations of the inputs regarding hydrogen-content are compared and evaluated together. The results show that almost all of the inputs show a significant impact on the smhydrogen output. Significantly, gasifier temperature, SBR, moisture content and hydrogen have the highest impacts on the smhydrogen with contributions of 19.96, 17.18, 15.3 and 10.48%, respectively. In addition, other variables in feed properties, like C, O, S and N present a range of 1.28–8.6% and proximate components like VM, FC and A present a range of 3.14–7.67% of impact on smhydrogen.

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Flowsheet Modeling and Simulation of Biomass Steam Gasification for Hydrogen Production

Cited by 23 publications

References 126 publications

Numerical evaluation of hydrogen production by steam reforming of natural gas

Numerical evaluation of hydrogen production by steam reforming of natural gas

Organic Waste Gasification: A Selective Review

Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

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