Optimization of sugarcane bagasse pretreatment using alkaline hydrogen peroxide through ANN and ANFIS modelling

Rego, Artur S. C.; Valim, Isabelle C.; Vieira, Anna A.S.; Vilani, Cecília; Santos, Bettina Steren dos

doi:10.1016/j.biortech.2018.07.087

Cited by 33 publications

(8 citation statements)

References 17 publications

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“…The demonstrated performances of the ANN and ANFIS models in the present study are in good agreement with the study of Rego et al (2018), in which both ANFIS and ANN well modeled the contents of lignin, glucose, xylose and oxidized lignin of sugarcane bagasse in the process of sugarcane bagasse delignification. In addition, the ANFIS models showed better performance than the ANN only for xylose prediction (Rego et al, 2018).…”

Section: Comparison Between Ann and Anfis Modelssupporting

confidence: 89%

“…Artificial neural network (ANN), which is inspired from the structure of the human brain, is considered as one of the promising procedures to master different types of correlations existing between various dependent and independent variables. To learn these correlations, ANN procedure does not need to fully comprehend the mechanistic nature and the mathematical background of the processes (Hosseinzadeh et al, 2020a;Rego et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system

Hosseinzadeh

Zhou

Altaee

et al. 2020

Bioresource Technology

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Section: Comparison Between Ann and Anfis Modelssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system

Hosseinzadeh

Zhou

Altaee

et al. 2020

Bioresource Technology

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“…In another study by Rego et al ., 44 ANN and ANFIS modeling was used to optimize the sugarcane bagasse's delignification process using alkaline hydrogen peroxide pre‐treatment. The strong prediction efficiency of both ANFIS and ANN models was confirmed from their high R 2 values (close to 1) and low RMSE values.…”

Section: Application In Pre‐treatment Process Modelingmentioning

confidence: 99%

Artificial neural networks in valorization process modeling of lignocellulosic biomass

Pradhan

Jaiswal

2022

Biofuels Bioprod Bioref

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Value-added products such as biofuels, chemicals, enzymes, and many others can be prepared from lignocellulosic biomass (LCB). To achieve high yields of these value-added products, powerful tools such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) can be utilized during process development. In this article, we have therefore reviewed the recent application of ANN and ANFIS in modeling LCB valorization processes. Studies have shown the high predictive capability of both ANN and ANFIS for a range of different processes such as pre-treatment processes (microwave-assisted, organosolv-, ultrasound-assisted pre-treatment and many others), thermal processes (pyrolysis and gasification), enzymatic hydrolysis, and fermentation processes. These tools have also shown outstanding accuracy in predicting elemental composition and thermal characteristics of biomass by using only the proximate composition of LCB as the input information. In combination with evolutionary algorithms like genetic algorithm, particle swarm optimization or ant colony optimization, the ANN and ANFIS tools have shown excellent results in obtaining operational conditions for the efficient production of bioethanol, biogas, organic acids, lignin, and enzymes. However, there are only limited reports of the application of ANN and ANFIS in enzyme, organic acid and lignin production. Further research is therefore required to assess the suitability of using these tools in process development for the production of lignin, enzymes, and organic acids.

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“…For the ANN model, MATLAB 2017b software was utilized [89]. In this study, ANN with various hidden neurons in its hidden layer (1-50) was used with the Levenberg-Marquardt back-propagation algorithm (trainlm) [90] including the hyperbolic tangent and logarithmic sigmoid as activation functions, tested in hidden and output layers, respectively. The ANN model with the low RRMSE and high correlation coefficient (r) was selected.…”

Section: Comparison Modelsmentioning

confidence: 99%

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

et al. 2019

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Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration.

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Optimization of sugarcane bagasse pretreatment using alkaline hydrogen peroxide through ANN and ANFIS modelling

Cited by 33 publications

References 17 publications

Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system

Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system

Artificial neural networks in valorization process modeling of lignocellulosic biomass

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

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