Modelling coal gasification with a hybrid neural network

Guo, Benhai; Shen, Y.; Ding-kai, LI; Zhao, Fu

doi:10.1016/s0016-2361(97)00122-1

Cited by 37 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This hybrid approach is well developed in chemical engineering [ 85 ], but it has not been yet applied systematically to the problems considered in the present review. One of the examples of its application is the study by Guo et al [ 86 ], where the hybrid model was built for gas production rates in the course of coal pyrolysis. More research with this approach would be welcome.…”

Section: Advancements In Prediction Of Conversion Data With Anns (Moving Window Hybrid Models)mentioning

confidence: 99%

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Muravyev

Luciano²,

Ornaghi

et al. 2021

Molecules

View full text Add to dashboard Cite

Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods.

show abstract

Section: Advancements In Prediction Of Conversion Data With Anns (Moving Window Hybrid Models)mentioning

confidence: 99%

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Muravyev

Luciano²,

Ornaghi

et al. 2021

Molecules

View full text Add to dashboard Cite

show abstract

“…Multiple Neural Network (MNN) for the syngas composition prediction and dynamic principal component analysis was proposed in [15]. Other researchers, e.g., Guo et al [16] have modeled coal gasification with a hybrid NN. A model of a coal gasification was developed, incorporating a first-principles model with an NN parameter estimator.…”

Section: Modeling and Prediction In Ucgmentioning

confidence: 99%

A Comparative Study of Data-Driven Modeling Methods for Soft-Sensing in Underground Coal Gasification

et al. 2019

View full text Add to dashboard Cite

Underground coal gasification (UCG) is a technological process, which converts solid coal into a gas in the underground, using injected gasification agents. In the UCG process, a lot of process variables can be measurable with common measuring devices, but there are variables that cannot be measured so easily, e.g., the temperature deep underground. It is also necessary to know the future impact of different control variables on the syngas calorific value in order to support a predictive control. This paper examines the possibility of utilizing Neural Networks, Multivariate Adaptive Regression Splines and Support Vector Regression in order to estimate the UCG process data, i.e., syngas calorific value and underground temperature. It was found that, during the training with the UCG data, the SVR and Gaussian kernel achieved the best results, but, during the prediction, the best result was obtained by the piecewise-cubic type of the MARS model. The analysis was performed on data obtained during an experimental UCG with an ex-situ reactor.

show abstract

“…The method does not provide an analytical solution, it brings numerical results. It has been widely used in fluidized bed with reasonable success to predict gas yield (Bing, G. et al, 1997;Xiao, G. et al, 2009). The University of Iowa Biomass Gasification System is located in the University of Iowa Oakdale Power Plant and the system is designed and established for research only (Fig 3.1).…”

Section: Gasification Modelingmentioning

confidence: 99%

Biomass gasification in a pilot-scale system

Shi¹

View full text Add to dashboard Cite

Modelling coal gasification with a hybrid neural network

Cited by 37 publications

References 5 publications

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

A Comparative Study of Data-Driven Modeling Methods for Soft-Sensing in Underground Coal Gasification

Biomass gasification in a pilot-scale system

Contact Info

Product

Resources

About