Abstract:

'Molecular refining' is a technology that describes the reaction process at the molecular level and achieves an accurate description of the specific composition and quality of oil products. It is one of the digital modeling methods for petrochemical production processes. Based on the diesel hydrofining unit of a petrochemical enterprise in China, this paper constructs a diesel molecular reconstruction model and a diesel hydrofining reaction kinetic model to meet the needs of intelligent factory construction. Firstly, the physical property library of diesel molecules including 1352 molecules was established, and the molecular type-homologous (MTHS) matrix was used to construct the diesel molecular reconstruction model. The maximum relative error between the simulated value and the real value was 4.83%, which proved that the model was reliable. Secondly, combined with the characteristics of the device, a reaction network containing 246 reactions and 282 molecules was constructed by screening and determining the reaction molecules and establishing the reaction rules of diesel hydrofining. Based on the influence degree of the alkyl side chain on the chemical reaction rate constant, the reaction rate constant influence factor was set up, and the reaction rate constant correlation model was established. The 492 reaction parameters were reduced to 116, which greatly reduced the number of variables, and the kinetic correlation model of diesel hydrofining reaction at the molecular level was constructed. The results showed that under 398℃, 9MPa and space velocity of 1h^-1, the relative error between the calculated value and the real value of the sulfur content of the product after hydrodesulfurization was less than 10%, and the model had good stability, which proved that the model was reliable. Based on the established model, the prediction of product composition under different operating conditions could be realized. This study can provide research ideas for the construction of intelligent factory models in petrochemical enterprises.

Key words: molecular refining, diesel hydrofining, molecular reconstruction model, reaction network, genetic algorithm, kinetic model, computer simulation

摘要：

“分子炼油”是从分子层面描述反应过程、实现对油品特定组成与质量精确描述的一种技术，是石化生产过程数字化建模方法之一。本文以国内某石化企业柴油加氢精制装置为背景，通过构建柴油分子重构模型和柴油加氢精制反应动力学模型，满足智能工厂建设的需要。首先，建立了包括1352个分子的柴油分子物性库，并采用分子类型-同系物（MTHS）矩阵构建柴油分子重构模型，模拟值与真实值最大相对误差为4.83%，证明模型可靠。其次，结合装置特点，通过筛选确定反应分子、建立柴油加氢精制反应规则，构建了包含246个反应、涉及282个分子的反应网络，并基于烷基侧链对化学反应速率常数的影响程度设置反应速度常数影响因子，建立反应速率常数关联模型，将492个反应参数减少至116个，大大减少了变量个数，由此构建分子水平柴油加氢精制反应动力学关联模型。结果表明，在温度398℃、压力9MPa、空速为1h^-1条件下，加氢脱硫后产品含硫量的计算值与真实值相对误差均小于10%，且模型稳定性好，证明模型可靠。基于所建立的模型，可实现不同操作工况下产品组成的预测。本文可为石化企业智能工厂模型建设提供研究思路。

关键词: 分子炼油, 柴油加氢精制, 分子重构模型, 反应网络, 遗传算法, 动力学模型, 计算机模拟