Computers & Chemical Engineering最新文献_第6页

Study on explosion characteristics of premixed gas influenced by magnetic field based on product analysis 基于产物分析的磁场对预混气体爆炸特性影响研究

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-11-29 DOI: 10.1016/j.compchemeng.2025.109516

Shoutao Hu , Zhiwei Zhang , Hao Gong , Zhong Guan , Meiqi Liu , Nana Wu

In order to investigate the effects and mechanisms of magnetic fields on the explosion characteristics of premixed gases, explosion experiments were conducted under both magnetic field and no-magnetic field conditions using CH₄-air and C₃H₈-air premixed combustible gases. Explosion product analysis revealed variations in component types and concentrations under both conditions. Additionally, simulation studies related to product formation were carried out. The experimental results showed that the magnetic fields reduced maximum explosion pressure and flame velocity for both mixtures, while the detection of alkane products significantly increased. Numerical simulations revealed that ·H, ·O, ·OH, and ·CH₃ play key roles in the generation of explosion products in CH₄-air and C₃H₈-air premixed gases. The magnetic field caused changes in multiple free radical chain reaction pathways during the explosions of methane and propane. By simulating key elementary reactions using Materials Studio, it was found that in the methane explosion process, the reactions C₂H₂+·H, ·C₃H₅+·H, and ·C₂H₅+·CH₃ exhibit the lowest energy gaps, with the minimum value being 0.043855 eV. In the case of propane explosion, the energy gaps of critical elementary reactions increase in the order of ·C₂H₃+·CH₃, C₂H₄+·O, C₂H₂+·O, ·CH₃+·H, and ·CH₃+·HCO, with the lowest being 0.012061 eV. These low energy barriers indicate that little energy is needed to affect key reactions during methane and propane explosions. The applied magnetic field reduces collisions between free radicals, thereby inhibiting the consumption of reaction intermediates and ultimately increasing the concentration of explosion products.

为了研究磁场对预混气体爆炸特性的影响及其机理，采用ch4 -空气和c3h8 -空气预混可燃气体进行了有磁场和无磁场条件下的爆炸实验。爆炸产物分析揭示了两种条件下成分类型和浓度的变化。此外，还进行了与产品形成相关的模拟研究。实验结果表明，磁场降低了两种混合物的最大爆炸压力和火焰速度，而烷烃产物的检出率显著提高。数值模拟结果表明，在ch4 -空气和c3h8 -空气预混气体中，·H、·O、·OH和·CH3对爆炸产物的生成起关键作用。在甲烷和丙烷爆炸过程中，磁场引起了多种自由基链反应途径的变化。通过Materials Studio模拟关键元素反应，发现在甲烷爆炸过程中，C2H2+·H、·C3H5+·H、·C2H5+·CH3反应的能隙最小，最小值为0.043855 eV。丙烷爆炸时，临界元素反应的能隙大小依次为·C2H3+·CH3、C2H4+·O、C2H2+·O、·CH3+·H、·CH3+·HCO，最低为0.012061 eV。这些低能垒表明，在甲烷和丙烷爆炸过程中，只需很少的能量就能影响关键反应。外加磁场减少了自由基之间的碰撞，从而抑制了反应中间体的消耗，最终提高了爆炸产物的浓度。

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引用次数: 0

A stability-oriented stochastic optimization strategy for refinery scheduling during unit shutdowns 面向稳定的炼油厂停运调度随机优化策略

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-11-28 DOI: 10.1016/j.compchemeng.2025.109502

Ziting Liang , Zhi Li , Xin Dai , Yue Cao , Feng Qian

Planned unit shutdowns are critical to refinery operations. Careful scheduling is required to balance maintenance needs, safety inspections, and regulatory compliance. At the same time, it is essential to ensure production stability. Traditional refinery scheduling models primarily focus on economic objectives. The operational challenges introduced by shutdown conditions, such as variations in unit flow rates and inventory instability, are often overlooked in these models. Existing approaches predominantly utilize deterministic optimization frameworks. The frameworks fail to adequately address the uncertainties in process yields which arise from variations in feedstock properties and operational conditions. Also, Frequent unit transitions and inventory fluctuations is not considered in those frameworks. In order to overcome these limitations, a novel optimization strategy which explicitly incorporates stability considerations into shutdown scheduling is proposed in this paper. A new metric based on discrete switching event counts is introduced, which quantifies and limits the variability of unit flow rate. This metric helps reduce unnecessary adjustments and operational disruptions, as evidenced by few unit flow rate transitions observed in the optimized scheduling. Additionally, a two-stage stochastic optimization model is developed to handle unit yield uncertainties. The model improves the robustness of schedules by mitigating the impacts of uncertainty on key operational variables. The proposed method is validated using real industrial case studies. The scheduling results demonstrate that the proposed method has better performance on improving operational stability during refinery shutdowns.

计划中的装置关闭对炼油厂的运营至关重要。需要仔细的调度来平衡维护需求、安全检查和法规遵从性。同时，保证生产的稳定性至关重要。传统的炼油厂调度模型主要关注经济目标。在这些模型中，通常忽略了关井条件带来的作业挑战，例如单位流量的变化和库存的不稳定性。现有的方法主要利用确定性优化框架。该框架未能充分解决由原料性质和操作条件变化引起的工艺产量的不确定性。此外，在这些框架中没有考虑到频繁的单位转换和库存波动。为了克服这些限制，本文提出了一种新的优化策略，明确地将稳定性考虑纳入到停机调度中。引入了一种基于离散开关事件计数的新度量，量化和限制了单位流量的可变性。该指标有助于减少不必要的调整和操作中断，正如在优化调度中观察到的很少的单位流量变化所证明的那样。此外，还建立了一个两阶段随机优化模型来处理机组产量的不确定性。该模型通过减轻不确定性对关键操作变量的影响，提高了调度的鲁棒性。通过实际工业案例验证了该方法的有效性。调度结果表明，该方法在提高炼油厂停运时的运行稳定性方面具有较好的效果。

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引用次数: 0

A fairness-guided supply chain framework for polyester production from biomass 生物质聚酯生产的公平导向供应链框架

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-11-28 DOI: 10.1016/j.compchemeng.2025.109504

Bo-Xun Wang, Victor M. Zavala

Supply chains are complex systems involving many assets and stakeholders that need to be carefully orchestrated to conduct a variety of functions (e.g., meeting demands, ensuring robust operations, and cut down costs). Traditional optimization models that maximize total profit often lead to unfair profit distribution among stakeholders, which could discourage stakeholder collaboration and impede sustainable business development (e.g., activate new market/economies). In this work, we propose a fairness-guided optimization framework for supply chain design. To motivate our developments, we consider the production of a valuable polyester known as polybutylene adipate terephthalate (PBAT) from biomass in the State of Wisconsin. Specifically, we aim to design a supply chain (a bioeconomy) that harnesses biomass from different counties (stakeholders) and that processes this in chemical facilities to produce PBAT. We study the total cost and greenhouse gas (GHG) emissions in the system with the optimal allocation of supplies and facilities installed. We introduce different optimization formulations that maximize the total profit and that maximize a fairness measure of the profit distribution. Our results demonstrate that the fairness-driven formulation can achieve a more equitable distribution of profit but at the expense of a lower total profit, highlighting the inherent trade-off between fairness and economic efficiency. This study aims to provide insights for decision-makers that aim to balance economy development, sustainability, and fairness when designing supply chains.

供应链是复杂的系统，涉及许多资产和利益相关者，需要精心安排以执行各种功能（例如，满足需求，确保稳健运营，并降低成本）。追求总利润最大化的传统优化模型往往导致利益相关者之间的利润分配不公平，这可能会阻碍利益相关者的合作，阻碍可持续的业务发展（例如，激活新的市场/经济）。在这项工作中，我们提出了一个公平导向的供应链设计优化框架。为了推动我们的发展，我们考虑从威斯康星州的生物质中生产一种有价值的聚酯，称为聚己二酸丁二酯（PBAT）。具体来说，我们的目标是设计一个供应链（生物经济），利用来自不同国家（利益相关者）的生物质，并在化学设施中处理这些生物质以生产PBAT。我们研究了在供应和设施配置最优的情况下，系统的总成本和温室气体排放。我们介绍了不同的优化公式，最大化总利润和最大化利润分配的公平措施。我们的研究结果表明，公平驱动的公式可以实现更公平的利润分配，但以降低总利润为代价，突出了公平与经济效率之间的内在权衡。本研究旨在为决策者在设计供应链时兼顾经济发展、可持续性和公平性提供参考。

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引用次数: 0

Online redesign of dynamic experiments for high-throughput bioprocess development using deep reinforcement learning 利用深度强化学习在线重新设计高通量生物工艺开发的动态实验

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-11-27 DOI: 10.1016/j.compchemeng.2025.109500

Martin F. Luna , Federico M. Mione , Ernesto C. Martinez , M. Nicolas Cruz Bournazou

For efficiency and reproducibility, modern biotech laboratories increasingly rely on robotic platforms to perform complex, dynamic experiments to generate informative data for bioprocess development and knowledge discovery. Furthering the goal of self-driving biolabs imposes the need of automating cognitive demanding tasks such as redesigning online an experiment to maximize information gain in the face of different sources of uncertainty including microorganism behavior, hardware failure, and noisy measurements. In this work, a reinforcement learning (RL) based formulation of the online experimental redesign problem for dynamic experiments is proposed. Simulation-based learning of a redesign policy for parallel cultivations in a high-throughput platform is discussed to provide implementation details regarding the RL agent design (perceptions and actions) and the reward function used. The simulated environment and a training workflow for sequential information control are integrated with the Proximal Policy Optimization (PPO) algorithm to learn how to modify ’on the fly’ an offline design based solely on previous observations and actions in a given experiment. Results obtained demonstrate the feasibility of using deep RL to guarantee the quality of the generated data and increase the level of automation that can be used on high-throughput platforms.

为了提高效率和重现性，现代生物技术实验室越来越依赖机器人平台来执行复杂的动态实验，为生物工艺开发和知识发现生成信息数据。为了进一步实现自动驾驶生物实验室的目标，需要自动化认知要求任务，例如在面对不同的不确定性来源（包括微生物行为、硬件故障和噪声测量）时，重新设计在线实验以最大化信息增益。在这项工作中，提出了一种基于强化学习（RL）的动态实验在线再设计问题的表述。本文讨论了在高吞吐量平台中并行培养的重新设计策略的基于模拟的学习，以提供有关RL代理设计（感知和行动）和所使用的奖励函数的实现细节。模拟环境和序列信息控制的训练工作流程与近端策略优化（PPO）算法相结合，以学习如何仅基于给定实验中的先前观察和行为修改“飞行”离线设计。结果表明，使用深度强化学习可以保证生成数据的质量，提高自动化水平，可用于高通量平台。

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引用次数: 0

A survey and tutorial of reinforcement learning methods in Process Systems Engineering 过程系统工程中强化学习方法的综述与教程

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-11-27 DOI: 10.1016/j.compchemeng.2025.109515

Maximilian Bloor , Max Mowbray , Ehecatl Antonio del Rio Chanona , Calvin Tsay

Sequential decision making under uncertainty is central to many Process Systems Engineering (PSE) challenges, where traditional methods often face limitations related to controlling and optimizing complex and stochastic systems. Reinforcement Learning (RL) offers a data-driven approach to derive control policies for such challenges. This paper presents a survey and tutorial on RL methods, tailored for the PSE community. We deliver a tutorial on RL, covering fundamental concepts and key algorithmic families including value-based, policy-based and actor-critic methods. Subsequently, we survey existing applications of these RL techniques across various PSE domains, such as in fed-batch and continuous process control, process optimization, and supply chains. We conclude with PSE-focused discussion of specialized techniques and emerging directions. By synthesizing the current state of RL algorithm development and implications for PSE this work identifies successes, challenges, trends, and outlines avenues for future research at the interface of these fields.

不确定条件下的顺序决策是许多过程系统工程（PSE）挑战的核心，传统方法在控制和优化复杂和随机系统方面经常面临限制。强化学习（RL）提供了一种数据驱动的方法来推导针对此类挑战的控制策略。本文介绍了针对PSE社区量身定制的RL方法的调查和教程。我们提供关于强化学习的教程，涵盖基本概念和关键算法家族，包括基于价值的，基于政策的和行动者批评的方法。随后，我们调查了这些RL技术在各种PSE领域的现有应用，例如进料批和连续过程控制、过程优化和供应链。最后，我们以pse为重点讨论了专业技术和新兴方向。通过综合RL算法开发的现状和对PSE的影响，本工作确定了成功、挑战、趋势，并概述了这些领域界面的未来研究途径。

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引用次数: 0

Q-learning agent-based level control of surge tanks for improved continuous manufacturing of monoclonal antibodies 基于q学习智能体的调压罐液位控制，用于改进单克隆抗体的连续生产

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-11-27 DOI: 10.1016/j.compchemeng.2025.109514

Naveen G Jesubalan , Saxena Nikita , Shantanu Banerjee , Navnath Deore , Keshari Gupta , Anurag S Rathore

During the continuous production of monoclonal antibodies, the optimal management of surge tanks is crucial for process scheduling and mitigating disruptions caused by potential process variations, equipment failures, or calibration errors. This study presents a Q-learning-based reinforcement learning (RL) agent designed to regulate surge tank levels, offering a more sophisticated alternative to the conventional ON/OFF control strategy. To ensure an accurate representation of real-world conditions, the RL training environment was modeled with all process limitations and scheduling dynamics in place. The Q-learning agent was trained using surge tank heights as observational inputs, with a reward policy designed to accommodate a wide range of scenarios, from steady-state conditions to process deviations. A replay buffer function was incorporated to enhance the model's robustness.

The agent underwent extensive training with 100,000 episodes and was evaluated using 100 random episodes for kernel density estimation (KDE)and 10 random episodes for in-depth analysis. In addition, a comparative analysis was conducted between the RL-based controller and a rule-based controller. This assessment aimed to highlight the reliability and robustness of the proposed control strategy. During the agent construction phase, each episode lasted 70 min, accounting for single process cycle. After training and testing, the agent was deployed in real-time and integrated with the PLC and other sensors using Python middleware. The reliability of the built agent was further tested through two unforeseen events introduced during the campaign. The results demonstrate the resilience and effectiveness of the proposed approach towards maintaining surge tank stability, thereby underscoring the robustness it offers and its suitability for implementation in commercial manufacturing.

在单克隆抗体的连续生产过程中，调压罐的优化管理对于工艺调度和减轻由潜在的工艺变化、设备故障或校准错误引起的中断至关重要。本研究提出了一种基于q学习的强化学习（RL）代理，旨在调节调压箱液位，为传统的ON/OFF控制策略提供更复杂的替代方案。为了确保真实世界条件的准确表示，RL训练环境的建模包含了所有的过程限制和调度动态。Q-learning代理使用调压罐高度作为观察输入进行训练，其奖励策略旨在适应从稳态条件到过程偏差的各种场景。为了增强模型的鲁棒性，引入了重放缓冲函数。代理接受了100,000集的广泛训练，并使用100个随机集进行核密度估计（KDE）和10个随机集进行深度分析。此外，还对基于rl的控制器和基于规则的控制器进行了对比分析。该评估旨在强调所提出的控制策略的可靠性和鲁棒性。代建阶段，每集持续70分钟，占单工艺周期。经过培训和测试，该代理被实时部署，并使用Python中间件与PLC和其他传感器集成。通过在战役中引入的两个不可预见的事件，进一步测试了构建剂的可靠性。结果证明了所提出的方法在维持调压箱稳定性方面的弹性和有效性，从而强调了它提供的鲁棒性及其在商业制造中实施的适用性。

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引用次数: 0

Parameter estimation in dynamic multiphase liquid–liquid equilibrium systems 动态多相液-液平衡系统的参数估计

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-11-25 DOI: 10.1016/j.compchemeng.2025.109485

Volodymyr Kozachynskyi , Dario Staubach , Erik Esche , Lorenz T. Biegler , Jens-Uwe Repke

Modeling dynamic systems with a variable number of liquid phases is a challenging task, especially in scenarios where the model is designed for optimization tasks such as parameter estimation. Although there exist methods to model the appearance and disappearance of liquid phases in dynamic systems, they usually require integer variables. In this work, the smoothed continuous approach (SCA) is developed for use with a large number of solvers, since it relies only on continuous variables. To demonstrate the applicability of the new method, the SCA is then applied to model the batch esterification of acetic acid with 1-propanol to water and propyl acetate, and to estimate the reaction parameters. Since the mixture may separate into two liquid phases during the course of the reaction, the parameters are estimated with information on the liquid compositions of both separated liquid phases, which improves the accuracy of the parameter estimates and opens new possibilities for optimal experimental design.

具有可变液相数量的动态系统建模是一项具有挑战性的任务，特别是在模型设计用于参数估计等优化任务的情况下。虽然已有方法来模拟动态系统中液相的出现和消失，但它们通常需要整数变量。在这项工作中，平滑连续方法（SCA）是为使用大量求解器而开发的，因为它只依赖于连续变量。为了证明新方法的适用性，应用SCA模拟了醋酸与1-丙醇批量酯化成水和乙酸丙酯的过程，并对反应参数进行了估计。由于混合物在反应过程中可能会分离成两种液相，因此根据分离的两种液相的液体成分信息来估计参数，提高了参数估计的准确性，为优化实验设计开辟了新的可能性。

引用次数: 0

A machine learning-fueled modelfluid for flowsheet optimization 用于流程优化的机器学习驱动的模型流体

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-11-24 DOI: 10.1016/j.compchemeng.2025.109486

Martin Bubel, Tobias Seidel, Michael Bortz

Process optimization in chemical engineering may be hindered by the limited availability of reliable thermodynamic data for fluid mixtures. Remarkable progress is being made in predicting thermodynamic mixture properties by machine learning techniques. The vast information provided by these prediction methods enables new possibilities in process optimization. This work introduces a novel modelfluid representation that is designed to seamlessly integrate these ML-predicted data directly into flowsheet optimization. Tailored for distillation, our approach is built on physically interpretable and continuous features derived from core vapor liquid equilibrium phenomena. This ensures compatibility with existing simulation tools and gradient-based optimization. We demonstrate the power and accuracy of this ML-fueled modelfluid by applying it to the problem of entrainer selection for an azeotropic separation. The results show that our framework successfully identifies optimal, thermodynamically consistent entrainers with high fidelity compared to conventional models. Ultimately, this work provides a practical pathway to incorporate large-scale property prediction into efficient process design and optimization, overcoming the limitations of both traditional thermodynamic models and complex molecular-based equations of state.

化工过程的优化可能会受到流体混合物可靠热力学数据可用性有限的阻碍。机器学习技术在预测混合热力学性质方面取得了显著进展。这些预测方法提供的大量信息为工艺优化提供了新的可能性。这项工作引入了一种新的模型流体表示，旨在将这些机器学习预测的数据直接无缝集成到流程优化中。专为蒸馏，我们的方法是建立在物理上可解释和连续的特征，源自核心汽液平衡现象。这确保了与现有仿真工具和基于梯度的优化的兼容性。我们通过将该模型应用于共沸分离的夹带剂选择问题，证明了该模型流体的功能和准确性。结果表明，与传统模型相比，我们的框架成功地识别了最佳的、热力学一致的、高保真的夹带剂。最终，这项工作为将大规模性质预测纳入有效的工艺设计和优化提供了一条实用途径，克服了传统热力学模型和复杂的基于分子的状态方程的局限性。

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引用次数: 0

A Petri net-based approach for modeling and analyzing the vulnerability of storage tanks under simultaneous fire and explosion hazards 基于Petri网的储罐火灾爆炸脆弱性建模分析方法

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-11-24 DOI: 10.1016/j.compchemeng.2025.109501

Zahra Khodabakhsh , Khadijeh Mostafaee Dolatabad , Matin Aleahmad , Leila Omidi

Fire and explosion accidents pose significant risks in the chemical and process industries. This study develops an integrated modeling framework to assess the vulnerability of oil storage tanks exposed to simultaneous fire and explosion hazards. The methodology integrates fault tree analysis for identifying contributing factors to simultaneous fire and explosion hazards, a hybrid Fuzzy Cognitive Maps-Bayesian Networks (FCM-BN) approach to quantify probabilistic relationships, graph theory metrics for criticality analysis, and Petri net simulations to model domino effect propagation. The FCM-BN model identifies three key contributors to domino effects, including domino effects from adjacent equipment, unprotected electrical equipment, and hot work and maintenance activities near leaks or flammable vapors. Graph theory analysis identifies Tank No. 4 as the most critical unit based on centrality metrics ("betweenness" and "closeness"), while Petri net simulations show that adjacent tanks, particularly Tanks No. 8 and No. 5, are highly vulnerable to explosion impacts. The framework provides both theoretical insights into domino effect mechanisms and practical tools for risk management, enabling targeted safety interventions and optimal resource allocation in storage facilities. These findings establish a foundation for preventing domino accidents through evidence-based vulnerability assessment and dynamic propagation modeling.

在化学和加工工业中，火灾和爆炸事故构成了重大风险。本研究开发了一个综合建模框架来评估同时暴露于火灾和爆炸危险的储油罐的脆弱性。该方法集成了故障树分析，用于识别同时引起火灾和爆炸危险的因素，模糊认知图-贝叶斯网络（FCM-BN）混合方法，用于量化概率关系，图论度量，用于临界分析，Petri网模拟，用于模拟多米诺效应的传播。FCM-BN模型确定了多米诺骨牌效应的三个关键因素，包括相邻设备的多米诺骨牌效应，未受保护的电气设备，以及泄漏或易燃蒸气附近的热工和维护活动。图论分析认为，基于中心性指标（“中间性”和“接近性”），4号罐是最关键的单元，而Petri网模拟显示，相邻的罐，特别是8号罐和5号罐，非常容易受到爆炸的影响。该框架提供了对多米诺骨牌效应机制的理论见解和风险管理的实用工具，实现了有针对性的安全干预和存储设施的最佳资源分配。这些发现为通过基于证据的脆弱性评估和动态传播建模来预防多米诺骨牌事故奠定了基础。

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引用次数: 0

MINLP-based integrated modeling and multi-period optimization of mass-energy coupled FCC-steam systems with carbon-cost-oriented economic objective 以碳成本为经济目标的基于minlp的质能耦合FCC-steam系统集成建模与多周期优化

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-11-24 DOI: 10.1016/j.compchemeng.2025.109503

Jian Long , Bishi Zhao , Kai Deng , Cheng Huang , Chen Fan

With increasing global pressure to decarbonize the energy and chemical industries, the oil refining sector is undergoing a critical transformation toward green and low-carbon development. As one of the core oil refining units, the fluid catalytic cracking (FCC) process is complex. Meanwhile, it has high energy consumption and large carbon emissions. Separate optimization leads to the loss of energy and quality synergy. To address the issue of simultaneous energy and quality losses resulting from the separate optimization of the FCC and steam systems, this study models and optimizes the multi-cycle energy and quality coupling of catalytic cracking process and steam system collaboration. Based on the deep coupling of the cracking reaction and the dynamic transmission characteristics of the steam pipeline network, a multi-time-scale coupling model is established to reveal the interaction mechanism between the device and the steam system. This work develops a mathematical framework based on mixed-integer linear optimization, which aims to enhance the overall economic performance of the integrated plant, integrating the topological constraints of the pipeline network, the variable operating conditions characteristics of the equipment, and the discrete start-stop logic. Through case verification and system decoupling comparative experiments, the revenue increase of the global optimization scheme with energy and quality coupling reached 41.2 %, proving that the proposed method can effectively improve energy efficiency in the optimization scheme under the actual refinery.

随着全球能源和化工行业脱碳压力的加大，炼油行业正在经历一场向绿色低碳发展的关键转型。流化催化裂化（FCC）作为核心炼油装置之一，工艺复杂。同时，它的能耗高，碳排放量大。单独优化导致能量和质量协同的损失。针对催化裂化过程中催化裂化过程与蒸汽系统分别优化导致的能量和质量同时损失的问题，本研究对催化裂化过程与蒸汽系统协同的多循环能量和质量耦合进行了建模和优化。基于裂化反应的深度耦合和蒸汽管网的动态传输特性，建立了多时间尺度耦合模型，揭示了装置与蒸汽系统的相互作用机理。本工作开发了一个基于混合整数线性优化的数学框架，旨在通过集成管网的拓扑约束、设备的可变运行条件特征和离散启停逻辑，提高综合工厂的整体经济性能。通过实例验证和系统解耦对比实验，能量与质量耦合的全局优化方案的收益增幅达到41.2%，证明所提方法能有效提高实际炼油厂优化方案的能效。

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引用次数: 0