Modeling of Alkali Pretreatment of Rice Husk Using Response Surface Methodology and Artificial Neural Network

被引：16

作者：

Nikzad, Maryam ^{[1
]}

Movagharnejad, Kamyar ^{[1
]}

Talebnia, Farid ^{[1
]}

Aghaiy, Ziba ^{[1
]}

Mighani, Moein ^{[1
]}

机构：

[1] Noshirvani Univ Technol, Fac Chem Engn, Babol Sar, Iran

来源：

CHEMICAL ENGINEERING COMMUNICATIONS | 2015年 / 202卷 / 06期

关键词：

Alkaline pretreatment; Artificial neural network; Enzymatic hydrolysis; Response surface methodology; Rice husk; ENZYMATIC-HYDROLYSIS; ACID PRETREATMENT; AQUEOUS-SOLUTION; OPTIMIZATION; BIOETHANOL; STRAW; SACCHARIFICATION; PARAMETERS; REGRESSION; CELLULOSE;

D O I：

10.1080/00986445.2013.871707

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Rice husk as a widely available lignocellulosic material was subjected to an alkaline pretreatment process. The alkaline pretreatment was carried out under various conditions. The influence of process parameters, such as pretreatment time, solid loading, and NaOH concentration, on the glucose and xylose yields were investigated by means of appropriate models. The maximum glucose and xylose yields obtained under optimum pretreatment condition were 68.82% and 53.77%, respectively. Response surface methodology (RSM) and artificial neural network were used to model the pretreatment processes. Both modeling methodologies were statistically compared by means of the coefficient of determination and relative mean square error. It was concluded that the artificial neural network shows a somewhat better performance compared to RSM.

引用

页码：728 / 738

页数：11

共 50 条

[1] Artificial neural network and response surface methodology: a comparative analysis for optimizing rice straw pretreatment and saccharification
Parkhey, Piyush
Ram, Aadil Keshaw
Diwan, Batul
Eswari, J. Satya
Gupta, Pratima
[J]. PREPARATIVE BIOCHEMISTRY & BIOTECHNOLOGY, 2020, 50 (08): : 768 - 780
[2] Evaluating the fluidized-bed drying of rice using response surface methodology and artificial neural network
Nanvakenari, Sara
Movagharnejad, Kamyar
Latifi, Asefeh
[J]. LWT-FOOD SCIENCE AND TECHNOLOGY, 2021, 147
[3] Artificial neural network modeling and response surface methodology of desalination by reverse osmosis
Khayet, M.
Cojocaru, C.
Essalhi, M.
[J]. JOURNAL OF MEMBRANE SCIENCE, 2011, 368 (1-2) : 202 - 214
[4] Artificial Neural Network and Response Surface Methodology Approach for Modeling and Optimization of Chromium (VI) Adsorption from Waste Water using Ragi Husk Powder
Krishna, D.
Sree, R. Padma
[J]. INDIAN CHEMICAL ENGINEER, 2013, 55 (03) : 200 - 222
[5] Modeling and optimization of coal oil agglomeration using response surface methodology and artificial neural network approaches
Yadav, Anand Mohan
Nikkam, Suresh
Gajbhiye, Pratima
Tyeb, Majid Hasan
[J]. INTERNATIONAL JOURNAL OF MINERAL PROCESSING, 2017, 163 : 55 - 63
[6] CARBON DIOXIDE REFORMING OF METHANE TO SYNGAS: MODELING USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK
Amin, Nor Aishah Saidina
Yusof, Khairiyah Mohd
Isha, Ruzinah
[J]. JURNAL TEKNOLOGI, 2005, 43
[7] Modeling of fixed-bed dye adsorption using response surface methodology and artificial neural network
Schio, R. R.
Salau, N. P. G.
Mallmann, E. S.
Dotto, G. L.
[J]. CHEMICAL ENGINEERING COMMUNICATIONS, 2021, 208 (08) : 1081 - 1092
[8] MODELING AND OPTIMIZATION OF ETHANOL FERMENTATION USING Saccharomyces cerevisiae: RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK
Esfahanian, Mehri
Nikzad, Maryam
Najafpour, Ghasem
Ghoreyshi, Ali Asghar
[J]. CHEMICAL INDUSTRY & CHEMICAL ENGINEERING QUARTERLY, 2013, 19 (02) : 241 - 252
[9] MODELING OF EXTRUSION PROCESS USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORKS
Shihani, Neelam
Kumbhar, B. K.
Kulshreshtha, Manoj
[J]. JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY, 2006, 1 (01) : 31 - 40
[10] Improving response surface methodology by using artificial neural network and simulated annealing
Abbasi, Babak
Mahlooji, Hashem
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (03) : 3461 - 3468

← 1 2 3 4 5 →