Principles of creating a digital twin prototype for the process of alkylation of benzene with propylene based on a neural network

K. G. Kichatov, T. R. Prosochkina, I. S. Vorobyova
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Abstract

Objectives. To identify the principles of creating digital twins of an operating technological unit along the example of the process of liquid-phase alkylation of benzene with propylene, and to establish the sequence of stages of formation of a digital twin, which can be applied to optimize oil and gas chemical production.Methods. The chemical and technological system consisting of reactor, mixer, heat exchangers, separator, rectification columns, and pump is considered as a complex high-level system. Data was acquired in order to describe the functioning of the isopropylbenzene production unit. The main parameters of the process were calculated by simulation modeling using UniSim® Design software. A neural network model was developed and trained. The influence of various factors of the reaction process of alkylation, separation of reaction products, and evaluation of economic factors providing market interest of the industrial process was also considered. The adequacy of calculations was determined by statistics methods. A microcontroller prototype of the process was created.Results. A predictive neural network model and its creation algorithm for the process of benzene alkylation was developed. This model can be loaded into a microcontroller to allow for real-time determination of the economic efficiency of plant operation and automated optimization depending on the following factors: composition of incoming raw materials; the technological mode of the plant; the temperature mode of the process; and the pressure in the reactor.Conclusions. The model of a complex chemicotechnological system of cumene production, created and calibrated on the basis of long-term industrial data and the results of calculations of the output parameters, enables the parameters of the technological process of alkylation to be calculated (yield of reaction products, energy costs, conditional profit at the output of finished products). During the development of a hardware-software prototype, adapted to the operation of the real plant, the principles and stages of creating a digital twin of the operating systems of chemical technology industries were identified and formulated.
基于神经网络创建苯与丙烯烷基化过程数字孪生原型的原理
目标。以苯与丙烯的液相烷基化过程为例,确定创建运行技术单元数字孪生的原则,并确定数字孪生形成的阶段顺序,该顺序可用于优化石油和天然气化工生产。由反应器、混合器、热交换器、分离器、精馏塔和泵组成的化学和技术系统被视为一个复杂的高级系统。为了描述异丙苯生产装置的运行情况,我们获取了相关数据。通过使用 UniSim® Design 软件进行模拟建模,计算了工艺的主要参数。开发并训练了一个神经网络模型。还考虑了烷基化反应过程中各种因素的影响、反应产物的分离以及提供工业过程市场利益的经济因素的评估。通过统计方法确定了计算的充分性。结果。开发了苯烷基化工艺的预测神经网络模型及其创建算法。该模型可加载到微控制器中,以便实时确定工厂运行的经济效益,并根据以下因素自动进行优化:进料成分、工厂的技术模式、工艺的温度模式以及反应器中的压力。在长期工业数据和产出参数计算结果的基础上创建和校准的积层烯生产复杂化学技术系统模型,可以计算烷基化技术过程的参数(反应产物产量、能源成本、成品产出的条件利润)。在开发适应实际工厂运行的硬件-软件原型期间,确定并制定了创建化学技术工业操作系统数字孪生系统的原则和阶段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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