Prediction of tar and particulate in biomass gasification using adaptive neuro fuzzy inference system

Rameshkumar, R.; Mayilsamy, K.

doi:10.3233/ifs-131004

Cited by 6 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to the literature, high coefficients of determination with R 2 higher than 0.90 and 0.98 were found by different authors [28,66] by using artificial networks ANN to predict tar from biomass gasification. Despite the essence of this model is the same that PLS, the ANN has also got limitations related to an oversimplified approach, where some parameters are assumed to be constant.…”

Section: Partial Least Square Regression For Collected Tarmentioning

confidence: 83%

“…It requires a very large number of samples and it has got 'black box' nature in comparison with other models. Other authors have mainly proposed models based on kinetic models finding overestimation of the tar content or even the models are not able to foresee the tar content in the product gas [35,[66][67][68][69]. The lack of kinetic data and the theoretical comprehension of the tar reaction during gasification point out the difficult task of modelling this variable.…”

Section: Partial Least Square Regression For Collected Tarmentioning

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: Partial Least Square Regression For Collected Tarmentioning

confidence: 83%

Section: Partial Least Square Regression For Collected Tarmentioning

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

“…Nevertheless, as highlighted by the authors, the models are not able to foresee the tar content in the product gas, resulting in a negative R 2 value. The model proposed et al [55]; ► Hejazi et al [56]; ○ Rameshkumar and Mayilsamy [58]. Dotted and dashed lines correspond with a relative error of 10 and 20 %, respectively.…”

Section: Comparison Of Ann Modelling With Existing Literature Modelsmentioning

confidence: 98%

Tar prediction in bubbling fluidized bed gasification through artificial neural networks

Serrano

Castelló

2020

Chemical Engineering Journal

View full text Add to dashboard Cite

“…Kumar et al used FNN to determine optimal operating conditions based on the ultimate analysis data of biomass feedstock (Kumar et al, 2018). Rameshkumar et al trained an ANFIS model to predict the tar content after gasification to improve the syngas quality (Rameshkumar & Mayilsamy, 2014).…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

View full text Add to dashboard Cite

Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

show abstract

Prediction of tar and particulate in biomass gasification using adaptive neuro fuzzy inference system

Cited by 6 publications

References 11 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

Tar prediction in bubbling fluidized bed gasification through artificial neural networks

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Contact Info

Product

Resources

About