摘要
以新型的一水硫酸氢钠为催化剂,采用粉末状催化剂混合乙酸一起进料的加入方式,即流化催化精馏工艺,并利用响应面法优化乙酸乙酯催化精馏过程条件。首先,通过单因素灵敏度分析法对乙酸进料量、酸醇进料摩尔比、回流比、催化剂用量、釜底加热功率5个因素进行实验考察,确定了乙酸进料量、酸醇摩尔进料比、回流比3个关键因素的优化值及取值范围。根据单因素实验结果与精馏塔设备要求,塔釜加热功率和催化剂用量分别设定为68 W和2.0%(质量分数)乙酸,采用中心组合设计原则对乙酸进料量、酸醇进料摩尔比和回流比3个关键因素进行实验设计。以乙醇转化率为响应值,基于响应实验结果,利用响应面法对实验结果进行了方程回归,得到3个关键因素与响应值的二次关联模型。通过方差分析和平行实验,证明该模型准确可用。优化后的乙酸乙酯流态化催化精馏工艺条件为乙酸进料量3.2 mol·h-1,酸醇进料摩尔比为3.1,回流比为3.3,在此优化条件下进行实验,乙醇转化率为88.67%,比基于单因素灵敏度分析法得到的优化工艺条件下乙醇转化率高1.0%。
A novel fluidized catalytic distillation process was developed to produce ethyl acetate based on sodium bisulfate catalyst, and response surface method was used to optimize process parameters. Firstly, single factor sensitivity analysis of five parameters, such as catalyst amount, reflux ratio, bottom heating power, acid alcohol molar feed ratio, feed rate, were carried out, and three key parameters (reflux ratio, acid alcohol molar feed ratio and feed rate) and their optimum ranges were determined. Depending on the results of single factor experiments and hydraulic limit of distillation equipment, reboiler duty of column 68 W and the amount of catalyst 2.0%(mass) acetic acid were fixed. Then central composite design methodology was used to design the experimental cases. The correlation model between three key parameters and the conversion rate of ethanol was obtained by response surface methodology based on the experimental results. By the analysis of variance and parallel experiments, the model was proved to be accurate and available. Finally, the optimum conditions were found to be feed flow of acetic acid 3.2 mol·h-1 and mole ratio of acetic acid/ethanol 3.1, reflux ratio 3.3. Under the conditions, the conversion rate of ethanol is 88.67%. Compared to the optimization parameters by a single factor sensitivity analysis process, the conversion rate of ethanol is increased by 1.0%.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2014年第11期4465-4471,共7页
CIESC Journal
基金
国家自然科学基金项目(21206014
21125628)
中央高校基本科研业务费专项资金项目(DUT14LAB14)~~
关键词
乙酸乙酯
催化精馏
响应面法
优化
ethyl acetate
catalytic distillation
response surface methodology
optimization