Abstract: The technology of producing chemicals directly from crude oil involves the direct catalytic cracking of crude oil into chemical raw materials.This innovative process bypasses traditional atmospheric and vacuum distillation units and hydrogenation units,directly reducing both equipment investment and energy consumption.Consequently,this leads to lower production costs and brings significant economic benefits.The direct conversion method not only streamlines the production process but also minimizes the need for complex infrastructure,making it a more efficient alternative to conventional methods.With the continuous advancement of the national dual carbon goals-aiming to peak carbon dioxide emissions and achieve carbon neutrality-accelerating the development of technology for direct catalytic cracking of crude oil to produce chemicals is of paramount importance.This technology holds the potential to significantly reduce process energy consumption and contribute to carbon emission reduction efforts.By optimizing the cracking process,it is possible to achieve higher yields of desirable chemicals while minimizing the formation of by-products like coke,which are less valuable and contribute to increased emissions.As research on the modeling of technology for producing chemicals directly from crude oil deepens,establishing intelligent models to guide process production becomes increasingly crucial.These models can optimize process operations by fine-tuning parameters in real-time,thereby achieving a balance between economic benefits and environmental sustainability.Intelligent models leverage data-driven insights to predict outcomes and adjust variables dynamically,ensuring that the production process remains efficient and aligned with both economic and environmental targets.This study established a robust process simulation model in Aspen HYSYS based on industrial trial data from direct catalytic cracking of crude oil.Through detailed case analysis,single-factor analysis was conducted on four critical process parameters:preheating temperature,reaction temperature,regeneration temperature,and catalyst equilibrium activity.Each of these parameters played a vital role in determining the efficiency and yield of the cracking process.By systematically varyied these factors,researchers can identify optimal conditions that maximize the production of key chemicals such as ethylene and propylene while minimizing unwanted by-products like coke.To enhance the predictive capabilities of the model,a neural network was implemented using Python programming.This neural network model was trained on a comprehensive dataset derived from the process simulations.The model's ability to predict product distribution under different operating conditions was rigorously tested and validated.Furthermore,a multi-objective optimization algorithm,NSGA-II,was integrated into the deep learning framework.This algorithm focuses on maximizing the yield of low-carbon olefins while minimizing coke production,providing a balanced approach to optimizing the overall process.Compared to traditional optimization methods,the established surrogate model offers higher computational efficiency and faster optimization solution times.It enables the decoupling of multiple operational variables,allowing for more precise control over the process.This real-time optimization capability is particularly beneficial in dynamic production environments where conditions can change rapidly.The optimization results demonstrated notable improvements:coke yield decreased by 0.23%,while the yields of ethylene and propylene increased by 1%.In conclusion,the intelligent agent model developed in this study not only enhances solution efficiency and prediction accuracy but also provides valuable insights for guiding process production.Its application could lead to more sustainable and cost-effective chemical manufacturing processes,aligning with both economic and environmental objectives.

Key words: catalytic cracking of crude oil, process simulation, multi-objective optimization, neural network

摘要： 原油直接制化学品技术涉及将原油通过催化裂解直接转化为化学原料.这一创新工艺绕过了传统的常压蒸馏装置、真空蒸馏装置和加氢装置,直接降低了设备投资和能耗.因此,这不仅降低了生产成本,还带来了显著的经济效益.直接转化方法不仅简化了生产流程,还减少了对复杂基础设施的需求,使其成为传统方法的一种更高效的替代方案.随着国家双碳目标——即二氧化碳排放达峰和碳中和——的不断推进,加速开发原油直接催化裂解制化学品的技术变得至关重要.这项技术有望显著降低过程能耗,并为减少碳排放做出贡献.通过优化裂解过程,可以实现更高产率的目标化学品,同时最小化焦炭等副产品的形成,这些副产品价值较低且会增加排放.随着对原油直接制化学品技术的建模研究不断深入,建立智能模型以指导生产过程变得越来越重要.这些模型可以通过实时微调参数来优化操作,从而实现经济效益和环境可持续性之间的平衡.智能模型利用数据驱动的见解预测结果,并动态调整变量,确保生产过程保持高效并符合经济和环境目标.本研究在Aspen HYSYS中建立了一个基于工业试验数据的稳健过程模拟模型,对 4 个关键工艺参数进行详细案例分析和单因素分析:预热温度、反应温度、再生温度和催化剂平衡活性.每个参数在决定裂解过程的效率和产率方面都起着至关重要的作用.通过系统地变化这些因素,研究人员可以确定最佳条件,以最大限度地提高关键化学品(如乙烯和丙烯)的生产,同时最小化不需要的副产品(如焦炭).为了增强模型的预测能力,使用Python编程实现了一个神经网络.这个神经网络模型是在从过程模拟中得出的综合数据集上进行训练的.该模型在不同操作条件下预测产品分布的能力经过了严格的测试和验证.此外,多目标优化算法NSGA-II被集成到深度学习框架中.该算法专注于最大化低碳烯烃的产量,同时最小化焦炭的产生,为整体过程优化提供了平衡的方法.与传统优化方法相比,所建立的代理模型具有更高的计算效率和更短的优化解决方案时间.它使多个操作变量的解耦成为可能,从而允许对过程进行更精确的控制.这种实时优化能力在动态生产环境中特别有益,其中条件可能会迅速变化.优化结果显示了显著的改进:焦炭产量减少了 0.23%,而乙烯和丙烯的产量增加了 1%.总之,本研究中开发的智能代理模型不仅提高了解决方案效率和预测准确性,还为指导生产过程提供了宝贵的见解.其应用可能带来更可持续和经济有效的化学品制造过程,符合经济和环境目标.

关键词: 原油催化裂解, 流程模拟, 多目标优化, 神经网络

CLC Number: 

• TQ032.4 TQ021.8