Study on Ethanol Coupling Reaction Based on BP Neural Network and Correlation

R. Cheng, Shuya Peng, Ziheng Dai
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Abstract

Butanol and C4 olefins, as important chemical raw materials, are widely used in the production of chemical products and pharmaceutical intermediates. Traditional production methods use fossil energy as raw materials, but with the shortage of fossil energy production and the aggravation of its impact on the environment, the energy supply gradually tends to be diversified, and the development of new clean energy is becoming more and more urgent. Ethanol molecules can be prepared by biomass fermentation. They have a wide range of sources and are green and clean. They are used as platform molecules to produce high value-added butanol and C_4 olefins have great application prospects and economic benefits, and have attracted extensive attention at home and abroad. However, in the current industrial production, the catalyst combination and temperature have a great impact on the conversion of ethylene and the selectivity of C4 olefins, and its selection and control greatly affect the production efficiency of C4 olefins. This paper focuses on the influence effect and degree of two factors on two dependent variables in the process of preparing C4 olefins by ethylene coupling reaction. By establishing the least square curve to fit the temperature and ethanol conversion and the temperature and C4 olefin selectivity, the fitting curve is obtained. It can be seen that the temperature has a primary or quadratic function relationship with the ethanol conversion or C4 olefin selectivity, so it is judged that it has a certain influence, Then the effects of temperature, catalyst group and loading method on ethanol conversion and C4 olefin selectivity were obtained by Spearman correlation coefficient and random forest regression algorithm. Based on this result, the model is established, optimized and analyzed, and the optimal catalyst combination and temperature are obtained, so as to obtain the highest C4 olefin yield and achieve the maximum industrial benefit
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基于BP神经网络和相关性的乙醇偶联反应研究
丁醇和C4烯烃作为重要的化工原料,广泛应用于化工产品和医药中间体的生产。传统的生产方式以化石能源为原料,但随着化石能源生产的短缺及其对环境影响的加剧,能源供应逐渐趋向多样化,开发新型清洁能源的需求越来越迫切。乙醇分子可以通过生物质发酵制备。它们有广泛的来源,是绿色和清洁的。它们作为平台分子用于生产高附加值的丁醇和C_4烯烃,具有很大的应用前景和经济效益,引起了国内外的广泛关注。但在目前的工业生产中,催化剂的组合和温度对乙烯的转化率和C4烯烃的选择性影响很大,其选择和控制对C4烯烃的生产效率影响很大。研究了乙烯偶联反应制备C4烯烃过程中两个因素对两个因变量的影响作用和程度。通过建立最小二乘曲线拟合温度与乙醇转化率、温度与C4烯烃选择性,得到拟合曲线。可以看出,温度与乙醇转化率或C4烯烃选择性呈一次或二次函数关系,因此判断温度对乙醇转化率和C4烯烃选择性有一定的影响,然后通过Spearman相关系数和随机森林回归算法得到温度、催化剂基团和负载方式对乙醇转化率和C4烯烃选择性的影响。在此基础上,建立模型并进行优化分析,得到最佳催化剂组合和温度,以获得最高的C4烯烃收率,实现最大的工业效益
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