Roudgar Saffari Peyman, Salarian Hesamoddin, Lohrasbi Ali, Salehi Gholamreza, Khoshgoftar Manesh Mohammad Hasan
Department of Mechanical Engineering, Adiban Institute of Higher Education, Garmsar 35819-69855, Iran.
Department of Mechanical Engineering, Nour Branch, Islamic Azad University, Nour 15858-54289, Iran.
ACS Omega. 2022 Apr 2;7(15):12493-12508. doi: 10.1021/acsomega.1c04345. eCollection 2022 Apr 19.
With the global production of 150 million tons in 2016, ethylene is one of the most significant building blocks in today's chemical industry. Most ethylene is now produced in cracking furnaces by thermal cracking of fossil feedstocks with steam. This process consumes around 8% of the main energy used in the petrochemical industry, making it the single most energy-intensive process in the chemical industry. This paper studies a tubular thermal cracking reactor fed by propane and the molecular mechanism of the reaction within the reactor. After developing the reaction model, the existing issues, such as the reaction, flow, momentum, and energy, were resolved by applying heat to the outer tube wall. After solving the entropy generation equations, the entropy generation ratio of the sources was evaluated. The temperature of the tube/reactor was tuned following the reference results, and processes were replicated for different states. The verification of the modeling and simulation results was compared with the industrial case. The Genetic Programming (GP) machine learning approach was employed to generate objective functions based on key decision variables to reduce the computational time of the optimization algorithm. For the first time, this study has proposed a systematic approach for optimizing a thermal cracking reactor based on a combination of Genetic Programming (GP), Water Cycle Algorithm (WCA), and Genetic Algorithm (GA). In this regard, multiobjective optimization was performed based on the maximization of the products and entropy generation with the generation of GP objective functions. The key decision variables in this study included inlet gas temperature, inlet gas pressure, air mass flow rate, and wall temperature. The results showed that the weighted percentage of products after optimization increased to 61.13% and the entropy production rate of the system decreased to 899.80 J/s, displaying an improvement of 0.85 and 16.51% compared with the base case, respectively, with the multiobjective GA algorithm. In addition, by applying the multiobjective WCA, the weighted percentage of products increased to 61.81%. The entropy production rate of the system decreased to 882.72 J/s. So, an improvement of 1.97% in weights of products and an improvement of 18.77% in entropy generation have been achieved compared with the base case.
2016年全球乙烯产量为1.5亿吨，乙烯是当今化学工业中最重要的基础原料之一。目前，大多数乙烯是通过化石原料与蒸汽在裂解炉中进行热裂解生产的。该过程消耗了石化行业约8%的主要能源，使其成为化学工业中最耗能的单一过程。本文研究了以丙烷为原料的管式热裂解反应器及其内部反应的分子机理。建立反应模型后，通过对外管壁加热解决了反应、流动、动量和能量等现有问题。求解熵产生方程后，评估了源项的熵产生率。根据参考结果调整管/反应器的温度，并针对不同状态重复过程。将建模和模拟结果与工业案例进行了验证比较。采用遗传规划（GP）机器学习方法，基于关键决策变量生成目标函数，以减少优化算法的计算时间。本研究首次提出了一种基于遗传规划（GP）、水循环算法（WCA）和遗传算法（GA）相结合的热裂解反应器优化系统方法。在此方面，基于产物最大化和熵产生，利用GP目标函数生成进行了多目标优化。本研究中的关键决策变量包括进气温度、进气压力、空气质量流量和壁温。结果表明，采用多目标遗传算法优化后，产物的加权百分比提高到61.13%，系统的熵产生率降低到899.80 J/s，分别比基准情况提高了0.85%和16.51%。此外，应用多目标WCA后，产物的加权百分比提高到61.81%，系统的熵产生率降低到882.72 J/s。因此，与基准情况相比，产物权重提高了1.97%，熵产生提高了18.77%。
