Intelligent Prediction of Conversion Rate of Acetic Acid in Catalytic Esterification Process of Bio-Oil

您当前的位置：

首页 >

文章列表页 >

Intelligent Prediction of Conversion Rate of Acetic Acid in Catalytic Esterification Process of Bio-Oil

更新时间：2025-07-09

- Intelligent Prediction of Conversion Rate of Acetic Acid in Catalytic Esterification Process of Bio-Oil
- Vol. 45, Issue 7, Pages: 118-122(2011)
- 作者机构：
  
  东南大学能源与环境学院,南京,210096
- 作者简介：
- 基金信息：
- DOI：
  CLC： X785
- Online First：10 July 2011，
  
  Published：2011
- 稿件说明：
移动端阅览
Intelligent Prediction of Conversion Rate of Acetic Acid in Catalytic Esterification Process of Bio-Oil[J]. 2011, 45(7): 118-122.
DOI：

Intelligent Prediction of Conversion Rate of Acetic Acid in Catalytic Esterification Process of Bio-Oil[J]. 2011, 45(7): 118-122. DOI：

摘要

以生物油乙酸转化率为提质指标

选用固体超强酸SO

/SiO

-TiO

对生物油催化酯化进行了实验研究.考察了不同的实验条件

即反应温度、酸醇量比、催化剂用量等对酯化反应的影响

在实验的基础上

运用最小二乘支持向量机建立乙酸转化率智能预测模型

并选用自适应粒子群优化算法对最小二乘支持向量机进行了参数优化.实验结果表明

最佳的生物油酯化工况为反应温度80 ℃、酸醇量比1.6和催化剂用量为7.5%.通过15个检测样本的检验

发现最小二乘支持向量机预测的平均相对误差能够降低到9.7%

其性能优于常用的BP神经网络与RBF神经网络

最小二乘支持向量机法更适合于预测生物油酯化过程中乙酸的转化率.

Abstract

Experiments were conducted to study the catalytic esterification of acetic acid which was regarded as the model compound of bio-oil. The solid super-acid

/SiO

-TiO

was selected as the catalyst and the conversion rate of acetic acid was viewed as the evaluating indicator. The effects of various factors like temperature

the mass ratio of ethanol to acetic acid

and the catalyst amount were examined. On the basis of experimental data

the least square support vector machine was applied to build a model of the intelligent prediction of the conversion rate of acetic acid

and the adaptive particle swarm algorithm was used to optimize the parameters in the least square support vector machine. The results indicate that the optimal catalytic esterification of bio-oil would opera

te at the temperature of 80 ℃

the mass ratio of ethanol to acetic acid of 1.6

and the catalyst content of 7.5%. By the examination of 15 testing samples

the prediction error(absolute average relative error)of the least square support vector machine decreases to 9.7%

showing that the least square support vector machine is more suitable for predicting the conversion rate of acetic acid during the catalytic esterification process of bio-oil than the conventional BP or RBF neural network.

关键词

Keywords

references

熊万明,傅尧,来大明,等.酸性离子交换树脂催化酯化改质生物油的研究[J].高等学校化学学报,2009,30(9):1754-1758.

XIONG Wanming, FU Yao, LAI Daming,et al.Upgrading of bio-oil via esterification catalysted with acidic ion-exchange resin[J]. Chemical Journal of Chinese Universities, 2009,30(9):1754-1758.

XU Ying, WANG Tiejun, MA Longlong, et al. Upgrading of the liquid fuel from fast pyrolysis of biomass over MoNi/γ-Al2O3 catalysts[J].Applied Energy, 2010, 87(9):2886-2891.

王勇,邹献武,秦特夫.生物质转化及生物油精致的研究进展[J].化学与生物工程,2010,27(9):1-5.

WANG Yong, ZOU Xianwu, QIN Tefu. Research progress on technologies of biomass conversion and upgrading of bio-oil[J]. Chemistry Bioengineering, 2010,27(9):1-5.

侯斌,吕子安,李晓辉,等. 生物质热解产物中焦油的催化裂解[J].燃料化学学报,2001,29(2): 70-75.

HOU Bin, LU Zi'an, LI Xiaohui,et al. Catalytic cracking of tar derived from biomass pyrolysis[J]. Journal of Fuel Chemistry and Technology, 2001,29(2): 70-75.

刘荣厚,黄彩霞,蔡均猛,等.生物质热裂解生物油精致的研究进展[J].农业工程学报,2008,24(3):308-312.

LIU Ronghou, HUANG Caixia, CAI Junmeng, et al. Research progress in the upgrading of bio-oil from biomass pyrolysis[J]. Transactions of the CSAE, 2008,24(3):308-312.

张健. 生物油乳化及其乳化液性质研究[D].合肥:中国科技大学,2009.

熊万明.生物油的分离与精致研究[D].合肥:中国科技大学,2010.

张琦,常杰,王铁军,等.固体酸改质生物油的研究[J].燃烧化学学报,2006,34(6):680-683.

ZHANG Qi, CHANG Jie, WANG Tiejun, et al.Study on upgrading of bio-oil catalyzed over solid acids[J]. Journal of Fuel Chemistry and Technology, 2006,34(6):680-683.

KUH A, WILDE P D. Comments on pruning error minimization in least squares support vector machines[J]. IEEE Transactions on Neural Networks, 2007, 18(2): 606-609.

HUANG C, DAVIS L S, TOWNSHEND J R G. An assessment of support vector machines for land cover classification [J]. International Journal of Remote Sensing, 2002, 23(2): 725-749.

UMEHARA S, YAMAZAKI T, SUGAI Y. A precipitation estimation system based on support vector machine and neural network[J]. Electronics and Communication in Japan, 2006, 89(3): 38-47.

LAMORSKI K, PACHEPSKY Y, SLAWINSKI C. Using support vector machines to develop pedotransfer functions for water retention of soils in Poland[J]. Soil Sci Soci Amj, 2008, 72(5): 1243-1247.

WANG Chunhua, ZHONG Zhaoping, RUI Li, et al. Prediction of jet penetration depth based on least square support vector machine[J]. Power Technology, 2010,203: 404-411.

王自健,高晓蕾,宋宏杰.乙酸乙酯的合成及酯化反应的影响因素[J].郑州工学院学报,1994,15(3):57-59.

WANG Zijian, GAO Xiaolei, SONG Hongjie. The synthesis of acetate and the influence factors in the esterification[J]. Journal of Zhengzhou Institute of Technology, 1994,15(3):57-59.

贾少刚,王翠苹,聂兆广,等.生物油模化物催化酯化的实验研究[J].青岛大学学报,2009, 24(4):72-75.

JIA Shaogang, WANG Cuiping, NIE Zhaoguang, et al. Experimental study on catalytic esterification of bio-oil model[J]. Journal of Qingdao University, 2009, 24(4):72-75.

于荟,朱银华,刘畅,等.新型介孔SO4/TiO2固体酸的制备及其催化酯化性能[J].催化学报,2009,30(3):265-271.

YU Hui, ZHU Yinhua, LIU Chang, et al. Preparation of novel mesoporous SO42-/TiO2 solid acid catalyst and its catalytic activity for esterification[J]. Chinese Journal of Catalysis,2009,30(3):265-271.

徐刚,瞿金平,杨智韬.一种改进的自适应粒子群优化算法[J].华南理工大学学报:自然科学版, 2008,36(9):31-36.

XU Gang, QU Jinping, YANG Zhitao. An improved adaptive particle swarm optimization algorithm[J]. Journal of South China University of Technology: Natural Science Edition, 2008, 36(9):31-36.

袁文化,鄂加强,龚金科,等.自适应粒子群优化的高压共轨燃油喷嘴多学科优化设计[J].内燃机工程,2009,30(5):63-65.

YUAN Wenhua, E Jiaqiang, GONG Jinke, et al. Multidisciplinary design optimization for fuel nozzle of high pressure common-rail injection system based on self-adaptive particle swarm optimization algorithm[J]. Chinese Internal Combustion Engine Engineering, 2009,30(5):63-65.

张立明.人工神经网络的模型及应用[M].上海:复旦大学出版社,1999: 100-145.

鄂加强.智能故障诊断及其应用[M].长沙:湖南大学出版社,2006: 135-165.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Density Weighted Pruning Method for Sparse Least Squares Support Vector Machines

Knowledge Graph Representation Learning Driven by Semantic Discrimination and Subgraph Integration

Sensitivity Analysis of Foil Geometrical Parameters for the Third-Generation CO2 Gas Foil Bearings Based on Design of Experiments Method

Numerical Simulation of the Energy Conversion Process in Thermoacoustic Engines with Different Stack Materials

A Fast Calculation Method for Thermal Flow Fields in Transformer Windings Based on PI-DeepONet

Related Author

司刚全

曹晖

张彦斌

贾立新

Related Institution

School of Electrical Engineering, Xi'an Jiaotong University

⁰