Differentiation of syngases produced by steam gasification of mono- and mixed sources feedstock: A chemometric approach

Škrbić, Biljana; Đurišić-Mladenović, Nataša; Cvejanov, J.

doi:10.1016/j.enconman.2018.06.060

Cited by 20 publications

(7 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…7, the steam cracking reaction favoured the formation of CH 4 at the expense of CO at high temperatures. These results have also been remarked by other authors [21] where it has also shown that gasification from feedstock with higher content of S and lower content of O produced higher CH 4 content and lower CO 2 .…”

Section: Principal Component Analysissupporting

confidence: 85%

“…Principal component analysis (PCA) and partial least square (PLS) regression models are two powerful analytical tools that have been successfully applied in order to determine which parameters meaningfully affect a process and how to model dependent variables. Statistical tools [19] have been applied to gasification research in recent years, in particular, the application of PCA [20,21] to study the influence of the biomass characteristics and PLS to the gasification process [22][23][24]. However, the complexity of the process, the different types of gasifiers, biomass used and experimental conditions complicate comparison with and extrapolation to dissimilar experimental installations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Principal component analysis and partial least square regression models to understand sorption-enhanced biomass gasification

Callén

Martínez

Grasa

et al. 2022

Biomass Conv. Bioref.

View full text Add to dashboard Cite

Gasification represents a potential technology for the conversion of biomass into usable energy. The influence of the main gasification parameters, i.e. the type of biomass used and its composition, as well as the composition of the outlet gas, were studied by a multivariate statistical analysis based on principal component analysis (PCA) and partial least square (PLS) regression models in order to identify the main correlations between them and to the contents of methane, ethylene and tar in the outlet gas. In this work, the experimental data used as input for the multivariate statistical analysis came from a TRL-4 gasification plant running under sorption enhanced conditions, i.e. using steam as the gasifying agent and CaO as the bed material. The composition of the biomass feed played an important role in the quality of the outlet gas composition. In fact, biomasses with high ash and sulphur contents (municipal solid waste) increased ethylene content, while those with high-volatile matter content and fixed C content (wood pellets, straw pellets and grape seeds) mainly increased CO and CO2 formation. By increasing the gasification bed temperature and the CaO/C ratio, it was possible to reduce the methane and the collected tar contents in the outlet gas. Other light hydrocarbons could also be reduced by controlling the Treactor and TFB. Methane, ethylene and tar contents were modelled, cross-validated and tested with a new set of samples by PLS obtaining results with an average overall error between 8 and 26%. The statistically significant variables to predict methane and ethylene content were positively associated to the thermal input and negatively to the CaO/C ratio. The biomass composition was also remarkable for both variables, as mentioned in the PCA analysis. As far as the tar content, which is undesirable in all gasification processes, the decrease in the tar content was favoured by high bed temperature, low thermal input and biomass with high-volatile matter content. In order to produce an outlet gas with adequate quality (e.g. low tar content), a compromise should be found to balance average bed temperature, sorbent-to-mass ratio, and ultimate and proximate analyses of the biomass feed. Graphical abstract

show abstract

Section: Principal Component Analysissupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Principal component analysis and partial least square regression models to understand sorption-enhanced biomass gasification

Callén

Martínez

Grasa

et al. 2022

Biomass Conv. Bioref.

View full text Add to dashboard Cite

show abstract

“…Gasification of biomass under with steam has attracted research attentions and a number of research works have shown that the process had great potential for large -scale development [10][11][12]. This process had strong advantage of making full use of low-grade solid fuels to produce the high economical value and cleaner products [13].…”

Section: Introductionmentioning

confidence: 99%

Steam gasification of Miscanthus derived char: the reaction kinetics and reactivity with correlation to the material composition and microstructure

Tian

Wang

et al. 2020

Energy Conversion and Management

View full text Add to dashboard Cite

This work presents a comprehensive study on the steam gasification kinetics and reactivity of Miscanthus chars (MC) prepared at different temperatures (600 °C, 800 °C and 1000 °C) with the correlations to their composition and microstructure. The results showed that the order of gasification reactivity was MC600 > MC800 > MC1000, and the microcrystalline structure and the content of inherent alkali and alkaline earth metals in the MC were the main factors affecting their reactivity under steam gasification. The reactivity also increased with the increase of gasification temperature, and the effect of gasification temperature on the reactivity of MC was far greater than that of the char production temperature. High heating rate could also effectively promote the gasification reactivity of MC. The kinetics of the steam gasification process were analyzed by different modelling methods. The Random pore model (RPM), among the three methods compared, was the most suitable one to describe the kinetics of isothermal gasification process, of which, the activation energies were in the range of 176-203 kJ/mol with a good kinetic compensation effect between activation energy and pre-exponential factor. The master-plots method proved that the F 2 mechanism was suitable for describing the early stage (X<50%) of the MC gasification reaction, and the D 1 mechanism was suitable to the late stage (X >50%) of the MC gasification process.

show abstract

“…Moreover, the +/-sign of a loading value indicates whether the variable has positive or negative correlation with the PC [23]. Data analysis was carried out using IBM SPSS Statistic 25 software.…”

Section: Multivariate Statistical Data Analysismentioning

confidence: 99%

“…Škrbić et al [23] compared the syngases obtained by gasification of biomass, polyethylene, waste tire and coal by applying PCA to literature-derived data, and they found that syngases from coal and waste tire gasification had the highest H2 content and H2/CO values. Chemometric studies by using PCA and HCA were also applied to determine the influence of fuel ash composition on the steam co-gasification of coal and biomass, which showed that higher total gas and hydrogen volumes were associated with the catalytic effects of biomass ash-derived metal oxides, mainly K2O [24,25].…”

Section: Introductionmentioning

confidence: 99%

Assessing the influence of biomass properties on the gasification process using multivariate data analysis

Gil

González-Vázquez

García

et al. 2019

Energy Conversion and Management

View full text Add to dashboard Cite

Multivariate analysis was used to study the influence of the biomass characteristics on the gasification process. Ten lignocellulosic biomass samples (almond shells-AS-, chestnut sawdust-CHE-, torrefied chestnut sawdust-CHET-, cocoa shells-CS-, grape pomace-GP-, olive stones-OS-, pine cone leafs-PCL-, pine sawdust-PIN-, torrefied pine sawdust-PINT-, and pine kernel shells-PKS-) were gasified in a bubbling fluidized bed gasifier under an air-steam atmosphere. Statistical analysis was applied to the variables that described the results of the gasification process, i.e., gas concentration, gas production (moles), calorific value of the product gas, energy density, and cold gas efficiency, together with the main biomass properties, such as those derived from the elemental and proximate analyses, the higher heating value (HHV), the particle density, and the elemental composition of the ashes. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to the data of biomass properties and gasification parameters in order to elucidate which feedstock features had a more determinant influence on the gasification process. Both HCA and PCA revealed a clear separation of the biomass samples into two main groups on the basis of the gasification results. The results indicated that PKS, PCL, PINT, OS and PIN biomasses were characterized by high production of combustible gases, such as CO and CH4, high conversion and cold gas efficiency during gasification. This indicated that the most important biomass properties for promoting the gas production, calorific value of the product gas, gasification conversion and energy efficiency were the C and H contents and the HHV of the biomass. However, biomasses CS and GP were mainly characterized by high H2 concentration and H2/CO molar ratio in the gas product, which was mainly related to the higher H/O ratio and K2O ash content of the biomass. The H2 concentration in the product gas was negatively

show abstract

Differentiation of syngases produced by steam gasification of mono- and mixed sources feedstock: A chemometric approach

Cited by 20 publications

References 58 publications

Principal component analysis and partial least square regression models to understand sorption-enhanced biomass gasification

Principal component analysis and partial least square regression models to understand sorption-enhanced biomass gasification

Steam gasification of Miscanthus derived char: the reaction kinetics and reactivity with correlation to the material composition and microstructure

Assessing the influence of biomass properties on the gasification process using multivariate data analysis

Contact Info

Product

Resources

About