Artificial Neural Network Modeling to Predict the Efficiency of Phosphoric Acid-Hydrogen Peroxide Pretreatment of Wheat Straw
Phosphoric acid-hydrogen peroxide (PHP) pretreatment is an effective method to obtain a cellulose-enriched fraction from biomass. In this study, artificial neural network (ANN) was used to predict PHP pretreatment efficiency of cellulose content (C-C), cellulose recovery (C-Ry), hemicellulose remova...
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| Format: | Article |
| Language: | English |
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North Carolina State University
2023-11-01
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| Series: | BioResources |
| Subjects: | |
| Online Access: | https://ojs.cnr.ncsu.edu/index.php/BRJ/article/view/22736 |
| _version_ | 1797387020476088320 |
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| author | Qing Wang Jinxiang Hua Jinguang Hu Li Zhao Mei Huang Dong Tian Yongmei Zeng Shihuai Deng Fei Shen Xinquan Zhang |
| author_facet | Qing Wang Jinxiang Hua Jinguang Hu Li Zhao Mei Huang Dong Tian Yongmei Zeng Shihuai Deng Fei Shen Xinquan Zhang |
| author_sort | Qing Wang |
| collection | DOAJ |
| description | Phosphoric acid-hydrogen peroxide (PHP) pretreatment is an effective method to obtain a cellulose-enriched fraction from biomass. In this study, artificial neural network (ANN) was used to predict PHP pretreatment efficiency of cellulose content (C-C), cellulose recovery (C-Ry), hemicellulose removal (H-Rl), and lignin removal (L-Rl) under various conditions of pretreatment time (t), temperature (T), H3PO4 concentration (Cp), and H2O2 concentration (Ch). The final optimized topology structure of the ANN models had 1 hidden layers with 9 neurons for C-C and 10 neurons for C-Ry, 10 neurons for H-Rl, and 12 neurons for L-Rl. The actual testing data fit the predicted data with R2 values ranging from 0.8070 to 0.9989. The relative importance (RI) revealed that Cp and Ch were significant factors influencing the efficiency of PHP pretreatment with total RI values ranging from 12% to 62.6%. However, their weights for the three components of biomass were different. The value of T dominated hemicellulose removal effectiveness with an RI value of 78.6%, while t did not seem to be a main factor dominating PHP pretreatment efficiency. The results of this study provide insights into the convenient development and optimization of biomass pretreatment from ANN modeling perspectives. |
| first_indexed | 2024-03-08T22:18:54Z |
| format | Article |
| id | doaj.art-02bdec4be4724ab28455d3f45f0bf81c |
| institution | Directory Open Access Journal |
| issn | 1930-2126 |
| language | English |
| last_indexed | 2024-03-08T22:18:54Z |
| publishDate | 2023-11-01 |
| publisher | North Carolina State University |
| record_format | Article |
| series | BioResources |
| spelling | doaj.art-02bdec4be4724ab28455d3f45f0bf81c2023-12-18T15:56:54ZengNorth Carolina State UniversityBioResources1930-21262023-11-01191288305790Artificial Neural Network Modeling to Predict the Efficiency of Phosphoric Acid-Hydrogen Peroxide Pretreatment of Wheat StrawQing Wang0https://orcid.org/0000-0002-2538-4798Jinxiang Hua1Jinguang Hu2Li Zhao3Mei Huang4Dong Tian5Yongmei Zeng6Shihuai Deng7Fei Shen8Xinquan Zhang9College of Grassland Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China; Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China; Department of Chemical and Petroleum Engineering, Schulich School of Engineering, the University of Calgary, Calgary T2N 4H9, CanadaInstitute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. ChinaDepartment of Chemical and Petroleum Engineering, Schulich School of Engineering, the University of Calgary, Calgary T2N 4H9, Canada Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. ChinaCollege of Grassland Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. ChinaPhosphoric acid-hydrogen peroxide (PHP) pretreatment is an effective method to obtain a cellulose-enriched fraction from biomass. In this study, artificial neural network (ANN) was used to predict PHP pretreatment efficiency of cellulose content (C-C), cellulose recovery (C-Ry), hemicellulose removal (H-Rl), and lignin removal (L-Rl) under various conditions of pretreatment time (t), temperature (T), H3PO4 concentration (Cp), and H2O2 concentration (Ch). The final optimized topology structure of the ANN models had 1 hidden layers with 9 neurons for C-C and 10 neurons for C-Ry, 10 neurons for H-Rl, and 12 neurons for L-Rl. The actual testing data fit the predicted data with R2 values ranging from 0.8070 to 0.9989. The relative importance (RI) revealed that Cp and Ch were significant factors influencing the efficiency of PHP pretreatment with total RI values ranging from 12% to 62.6%. However, their weights for the three components of biomass were different. The value of T dominated hemicellulose removal effectiveness with an RI value of 78.6%, while t did not seem to be a main factor dominating PHP pretreatment efficiency. The results of this study provide insights into the convenient development and optimization of biomass pretreatment from ANN modeling perspectives.https://ojs.cnr.ncsu.edu/index.php/BRJ/article/view/22736lignocellulosic biomassann modelpretreatment efficiencypredictionrelative importance |
| spellingShingle | Qing Wang Jinxiang Hua Jinguang Hu Li Zhao Mei Huang Dong Tian Yongmei Zeng Shihuai Deng Fei Shen Xinquan Zhang Artificial Neural Network Modeling to Predict the Efficiency of Phosphoric Acid-Hydrogen Peroxide Pretreatment of Wheat Straw BioResources lignocellulosic biomass ann model pretreatment efficiency prediction relative importance |
| title | Artificial Neural Network Modeling to Predict the Efficiency of Phosphoric Acid-Hydrogen Peroxide Pretreatment of Wheat Straw |
| title_full | Artificial Neural Network Modeling to Predict the Efficiency of Phosphoric Acid-Hydrogen Peroxide Pretreatment of Wheat Straw |
| title_fullStr | Artificial Neural Network Modeling to Predict the Efficiency of Phosphoric Acid-Hydrogen Peroxide Pretreatment of Wheat Straw |
| title_full_unstemmed | Artificial Neural Network Modeling to Predict the Efficiency of Phosphoric Acid-Hydrogen Peroxide Pretreatment of Wheat Straw |
| title_short | Artificial Neural Network Modeling to Predict the Efficiency of Phosphoric Acid-Hydrogen Peroxide Pretreatment of Wheat Straw |
| title_sort | artificial neural network modeling to predict the efficiency of phosphoric acid hydrogen peroxide pretreatment of wheat straw |
| topic | lignocellulosic biomass ann model pretreatment efficiency prediction relative importance |
| url | https://ojs.cnr.ncsu.edu/index.php/BRJ/article/view/22736 |
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