Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis

Bhuyan, Nilutpal; Narzari, Rumi; Baruah, Satyabrat Malla Bujar; Kataki, Rupam

doi:10.1007/s13399-020-00806-x

Cited by 19 publications

(16 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the results of the sensitivity analysis, it was determined that especially, the pyrolysis temperature and sample moisture content had a significant impact among the input parameters in biochar production. Bhuyan et al 52 used sensitivity analysis to determine the factors affecting bio‐oil yield. As a result of the analysis, it was concluded that the effects of temperature, heating rate and gas flow rate on bio‐oil yield were higher than other parameters.…”

Section: Resultsmentioning

confidence: 99%

Application of new hybrid models based on artificial neural networks for modeling pyrolysis yields of Atriplex nitens S.

Altikat

Alma

2021

Intl J of Energy Research

View full text Add to dashboard Cite

Summary Biochar, bio‐oil, and synthesis gas are products obtained from the pyrolysis process, which have alternative usage areas. Biochar is used for adsorption of pollutants, soil conditioner, and as an alternative heating source. Bio‐oil is used as a fuel for conventional engines after various upgrading methods, and the synthesis gas is a heat source in energy production. Biochar, bio‐oil, and synthesis gas yields obtained from the Atriplex nitens S. plant at the end of the pyrolysis process were modeled using artificial neural networks (ANNs) and hybrid models in this study. Multiple linear regression, ANNs, principal component analysis + multiple linear regression, and principal component analysis + ANN models were used. In addition, 48 different network architectures in ANNs were tested. At the end of the study, the best prediction results of biochar, bio‐oil, and synthesis gas were obtained from the ANN 35 (R2 = 0.977), ANN 17 (R2 = 0.985), and ANN 44 (R2 = 0.969) ANN architecture, respectively. The sensitivity analyses were performed using these best models for biochar, bio‐oil, and synthesis gas. As a result of the sensitivity analysis, it was determined that the most effective factor in the production of biochar and bio‐oil was the gas flow rate, and this was followed by the holding time and pyrolysis temperature. However, the most effective factor in synthesis gas yield was determined as the holding time, and this was followed by gas flow rate and pyrolysis temperature parameters.

show abstract

Section: Resultsmentioning

confidence: 99%

Application of new hybrid models based on artificial neural networks for modeling pyrolysis yields of Atriplex nitens S.

Altikat

Alma

2021

Intl J of Energy Research

View full text Add to dashboard Cite

show abstract

“…seaweed [ 77 ], cotton cocoon shell, tea waste, and olive husk [ 66 ], mechanoactivated coals [ 78 ], cattle manure [ 79 ], lignocellulosic forest residue and olive oil residue [ 80 ], cotton cocoon shell, fabricated tea waste, olive husk, and hazelnut shell [ 81 ]) and in some minor details, but the general concept remains the same. Thus, we illustrate it with the study by Kataki et al [ 63 ], who used a neural network model (4-14-1) to predict the product yield for the pyrolysis of dried weed biomass. The four input parameters have been the pyrolysis temperature (during the 30 min isothermal segment), heating rate used to attain this temperature, nitrogen flow rate, and particle size of the specimen.…”

Section: Prediction Of Conversion Data (Single Value Whole Curve)mentioning

confidence: 98%

“…There is a number of papers [ 63 , 64 , 65 , 66 ] on the application of ANNs for obtaining the total conversion (e.g., mass loss by TGA) using experimental conditions as an input. One of the first papers on the topic has been by Carsky and Kuwornoo [ 67 ] who used ANN to predict the main quantities for coal pyrolysis: tar, volatiles, and char yields.…”

Section: Prediction Of Conversion Data (Single Value Whole Curve)mentioning

confidence: 99%

“…In more recent studies, the idea has been extended to employ ANNs for predicting the conversion values at various moments, i.e., a TA signal itself. Such studies differ drastically in experimental systems ( Tithonia diversifolia weed biomass [ 63 ], low-density polyethylene [ 64 ], high-density polyethylene [ 68 ], safflower seed press cake [ 65 ], durian rinds [ 69 ], rape straw [ 70 ], coal gangue and peanut shell [ 71 ], pet coke [ 72 ], sewage sludge and peanut shell [ 73 ], sewage sludge and coffee grounds [ 74 ], vegetable fibers [ 75 ], rice husk and sewage sludge [ 45 ], sludge, watermelon rind, corncob, and eucalyptus [ 76 ], Sargassum sp. seaweed [ 77 ], cotton cocoon shell, tea waste, and olive husk [ 66 ], mechanoactivated coals [ 78 ], cattle manure [ 79 ], lignocellulosic forest residue and olive oil residue [ 80 ], cotton cocoon shell, fabricated tea waste, olive husk, and hazelnut shell [ 81 ]) and in some minor details, but the general concept remains the same.…”

Section: Prediction Of Conversion Data (Single Value Whole Curve)mentioning

confidence: 99%

“… The example of relative importance analysis for input descriptors of trained neural network model [ 63 ]. Reprinted by permission from Springer Nature.…”

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

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

Prediction of bio‐oil yield during pyrolysis of lignocellulosic biomass using machine learning algorithms

2022

View full text Add to dashboard Cite

This work aims to implement and use machine learning algorithms to predict the yield of bio-oil during the pyrolysis of lignocellulosic biomass based on the physicochemical properties and composition of the biomass feed and pyrolysis conditions. The biomass pyrolysis process is influenced by different process parameters, such as pyrolysis temperature, heating rate, composition of biomass, and purge gas flow rate. The inter-relation between the yield of different pyrolysis products and process parameters can be well predicted by using different machine learning algorithms. In this study, different machine learning algorithms, namely, multi-linear regression, gradient boosting, random forest, and decision tree, have been trained on the dataset and the models are compared to identify the optimum method for the determination of bio-oil yield prediction model. Analysis of the results showed the gradient boosting method to possess a regression score of 0.97 and 0.89 for the training and testing sets with root-mean-squared error (RMSE) values of 1.19 and 2.39, respectively, and overcome the problem of overfitting. Therefore, the present study provides an approach to train a generalized machine learning model, which can be employed on large datasets while avoiding the error of overfitting.

show abstract

Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis

Cited by 19 publications

References 36 publications

Application of new hybrid models based on artificial neural networks for modeling pyrolysis yields of Atriplex nitens S.

Application of new hybrid models based on artificial neural networks for modeling pyrolysis yields of Atriplex nitens S.

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

Prediction of bio‐oil yield during pyrolysis of lignocellulosic biomass using machine learning algorithms

Contact Info

Product

Resources

About