Simulation of preparation of acrylic acid from lactic acid dehydration based on BP neural network

WANG Yang; YU Dinghua; SUN Peng; GU Mingyan; XIE Yusheng; HUANG He

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Simulation of preparation of acrylic acid from lactic acid dehydration based on BP neural network

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- Simulation of preparation of acrylic acid from lactic acid dehydration based on BP neural network
- CIESC Journal Issue 1, Pages: 83-88(2009)
- 作者机构：
  
  1. 南京工业大学制药与生命科学学院
  2. 江苏省工业生物技术创新中心
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 January 2009，
  
  Published：05 January 2009
- 稿件说明：
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WANG Yang, YU Dinghua, SUN Peng, et al. Simulation of preparation of acrylic acid from lactic acid dehydration based on BP neural network[J]. CIESC Journal, 2009, (1): 83-88.
DOI：

WANG Yang, YU Dinghua, SUN Peng, et al. Simulation of preparation of acrylic acid from lactic acid dehydration based on BP neural network[J]. CIESC Journal, 2009, (1): 83-88. DOI：

摘要

通过生物质发酵产生的乳酸（2-羟基丙酸）在食品和医药领域具有广泛的商业应用价值，作为可再生资源的生物质乳酸已成为备受关注的化学原料。本文采用BP神经网络对乳酸脱水制丙烯酸的催化过程进行了仿真研究。采用正交实验设计确定实验点，主要考察原料液pH、原料液流速、载气流量和温度4个因素对丙烯酸产率的影响。针对正交实验的缺陷，将正交实验设计和神经网络结合起来，采用基于DOS界面并能方便调节BP神经网络计算的程序包对正交实验结果进行训练，用训练好的网络模拟催化反应体系的动态过程。结果表明，由神经网络仿真模拟出的三维图可以直观地体现各个反应条件对丙烯酸产率的影响，并用穷举法求出最佳反应条件，在该条件下的神经网络模拟产率为27.45%，与实验结果较吻合，相对误差约为－0.4391%。

Abstract

Lactic acid (LA) is a commercial fine chemical

used in food and medicine

which is readily available by biomass fermentation.As a chemical feedstock

LA is widely used due to the renewable biomass resources.In this paper

BP neural network was applied to simulating the dehydration reaction of lactic acid.The optimal reaction conditions were determined through orthogonal design that could be used to investigate the interrelated effects of pH

feed liquid flow rate

carrier gas flow rate and temperature on the yield of acrylic acid(AA).With the training samples from experimental data of orthogonal design

a new model that could simulate the process of the dehydrated reaction was established based on BP neural network theory.Then the network system trained was used to anticipate the effects of different ingredients and their interactions on AA yield.The three-dimensional graphs produced by the network could effectively express the relationships between reaction conditions and catalytic activity.Finally

with the help of the network

the highest AA yield of 27.45% was obtained

which was very close to the experimental result with the relative error of -0.4391%.

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