Computers & Chemical Engineering最新文献_第2页

Research on natural gas pipeline corrosion prediction by integrating extreme gradient boosting and generative adversarial network 结合极端梯度增强和生成对抗网络的天然气管道腐蚀预测研究

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

Computers & Chemical Engineering

Pub Date : 2025-12-30 DOI: 10.1016/j.compchemeng.2025.109547

Guoxi He , Jing Tian , Dezhi Tang , Fei Zhao , Shuhua Li , Chao Li , Kexi Liao , XiaoFei Chen , Wen Yang

Accurate prediction of corrosion rates is of great significance for ensuring pipeline integrity and operational safety. This study proposes a novel hybrid prediction model—GAN-QPSO-XGBoost—which integrates a Generative Adversarial Network (GAN), Quantum-behaved Particle Swarm Optimization (QPSO), and the XGBoost algorithm. This study used GAN to augment 100 field data sets with 50 high-quality synthetic samples, forming an enhanced dataset of 150. The Kolmogorov-Smirnov test showed p greater than 0.05 and MAPE around 5%, confirming the synthetic data’s statistical consistency and numerical reliability. QPSO, by introducing quantum behavior mechanisms, effectively overcomes the issues of local optima and premature convergence commonly found in traditional optimization algorithms, further optimizing the predictive performance of XGBoost.To comprehensively evaluate model performance, this study adopts multiple standard metrics for validation and introduces the SHAP (Shapley Additive exPlanations) method to enhance model interpretability. Experimental results demonstrate that the GAN-QPSO-XGBoost hybrid model significantly outperforms existing benchmark models in corrosion rate prediction, with the following evaluation metrics: R² = 0.922, MAPE = 1.24%, MAE = 0.036, MSE = 0.0018, and RMSE = 0.042, fully proving its excellent predictive accuracy and stability. SHAP analysis further reveals that temperature, liquid holdup, flow velocity, CO₂ partial pressure, gas-wall shear stress, and liquid-wall shear stress are the most significant factors influencing corrosion rate.In conclusion, the GAN-QPSO-XGBoost hybrid model not only significantly improves the accuracy and reliability of corrosion rate prediction but also provides a scientific basis and operational guidance for pipeline maintenance, safety assessment, and protection strategy formulation in practical engineering.

准确预测管道腐蚀速率对保证管道的完整性和运行安全具有重要意义。本研究提出了一种新的混合预测模型GAN-QPSO-XGBoost，该模型集成了生成对抗网络（GAN）、量子粒子群优化（QPSO）和XGBoost算法。本研究使用GAN用50个高质量的合成样本增强了100个现场数据集，形成了150个增强数据集。Kolmogorov-Smirnov检验显示p > 0.05， MAPE在5%左右，证实了合成数据的统计一致性和数值可靠性。QPSO通过引入量子行为机制，有效克服了传统优化算法存在的局部最优和过早收敛问题，进一步优化了XGBoost的预测性能。为了综合评价模型的性能，本研究采用多个标准指标进行验证，并引入SHAP （Shapley Additive exPlanations）方法来增强模型的可解释性。实验结果表明，GAN-QPSO-XGBoost混合模型在腐蚀速率预测方面明显优于现有的基准模型，其评价指标为：R²= 0.922,MAPE = 1.24%, MAE = 0.036, MSE = 0.0018, RMSE = 0.042，充分证明了其良好的预测精度和稳定性。进一步的SHAP分析表明，温度、液含率、流速、CO2分压、气壁剪切应力和液壁剪切应力是影响腐蚀速率最显著的因素。综上所述，GAN-QPSO-XGBoost混合模型不仅显著提高了腐蚀速率预测的准确性和可靠性，而且为实际工程中的管道维护、安全评估和保护策略制定提供了科学依据和操作指导。

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引用次数: 0

Manifold-aware stationary subspace and divergence analysis for nonstationary process monitoring 非平稳过程监测的流形感知平稳子空间及发散分析

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

Computers & Chemical Engineering

Pub Date : 2025-12-30 DOI: 10.1016/j.compchemeng.2025.109546

Xue Xu , Wei Zhao , Dong Lv , Yuanjian Fu , Chaomin Luo , Chengyi Xia

Due to load changes, unit aging, or other causes, industrial processes are in general time variant condition and characterized by nonstationarity, challenging conventional monitoring methods. A manifold-aware stationary subspace and divergence analysis (MSSDA) is proposed for monitoring nonstationary processes, which aims at capturing the underlying low-dimensional representations of data from geometric and statistical perspectives. Specifically, an across-epoch similarity term induced by Gromov-Wasserstein distance is developed to align the manifold structures across different epochs such that MSSDA faithfully explores the intrinsic geometric characteristics of data. An adaptive neighbor strategy is designed to learn the neighborhood relationship among data and tailor appropriate neighbors for each sample with conditions of data density. Afterwards, a maximizing-minimizing divergence analysis is also investigated to match the intra-epoch and inter-epoch statistical information. In this way, the learned reduced-dimensional representations of data provide an in-depth analysis into the operation process, enhancing the monitoring capabilities. To demonstrate its effectiveness, the MSSDA approach is applied to two complicated industrial processes including a wastewater treatment process and a real-world fluid catalytic cracking process.

由于负荷变化、机组老化或其他原因，工业过程通常处于时变状态，并具有非平稳性，这对传统的监测方法提出了挑战。提出了一种流形感知平稳子空间和散度分析（MSSDA），用于监测非平稳过程，旨在从几何和统计角度捕获数据的潜在低维表示。具体来说，利用Gromov-Wasserstein距离诱导的跨历元相似性项来对齐不同历元间的流形结构，使MSSDA忠实地探索数据的内在几何特征。设计了一种自适应邻居策略，学习数据之间的邻居关系，并根据数据密度的条件为每个样本定制合适的邻居。然后，研究了最大-最小散度分析，以匹配历元内和历元间的统计信息。通过这种方式，学习到的数据降维表示提供了对操作过程的深入分析，增强了监控能力。为了证明其有效性，将MSSDA方法应用于两个复杂的工业过程，包括废水处理过程和现实世界的流体催化裂化过程。

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引用次数: 0

What’s new in biomass supply chain optimization? current trends and insights 生物质供应链优化有什么新进展？当前趋势和见解

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

Computers & Chemical Engineering

Pub Date : 2025-12-29 DOI: 10.1016/j.compchemeng.2025.109545

Jeremy Pantet , Ludovic Montastruc , Pierre Thiriet

Biomass has emerged as a pivotal new resource that could alleviate dependence on fossil resources and support the ecological transition by benefiting local communities. There has been an expanding literature on the subject for the past two decades. The focus of this literature is primarily on the organization and optimization of the biomass supply chain (BSC), which is the key component in providing profitable and sustainable valorized goods from biomass. The aim of this paper is to evaluate the present state of known research gaps, identify research gaps in BSC design by including economic considerations, and propose new research orientations on the subject that rely on more multidisciplinary approaches. We found three main understudied gaps. The majority of papers still only consider the strategic and tactical decision levels, excluding the operational decision level. Therefore, there are still opportunities to improve the currently accepted BSC design. The demand, as part of the supply chain, appears to be understudied. In the reviewed literature, the demand is treated as a parameter, and is perfectly met by the production, without consideration for pricing, surplus, or shortage. The other gap found is that most of the models considered in this review describe a BSC in autarky, and few take into account importations either of additional biomass or of bioproduct in their studied case, or the potential exportation of surplus. Consequently, closing these gaps in biomass supply design and optimization would facilitate the integration of BSC modeling into broader economic models.

生物质已成为一种关键的新资源，可以减轻对化石资源的依赖，并通过造福当地社区来支持生态转型。在过去的二十年里，关于这个问题的文献越来越多。本文献的重点主要放在生物质供应链（BSC）的组织和优化上，这是从生物质中提供有利可图和可持续的增值商品的关键组成部分。本文的目的是评估已知研究差距的现状，通过考虑经济因素确定平衡记分卡设计中的研究差距，并提出依赖更多多学科方法的新研究方向。我们发现了三个主要的未被充分研究的空白。大多数论文仍然只考虑战略和战术决策层面，而不考虑作战决策层面。因此，目前公认的平衡记分卡设计仍有改进的机会。作为供应链一部分的需求似乎没有得到充分研究。在文献综述中，需求被视为一个参数，生产完全满足需求，而不考虑价格、过剩或短缺。发现的另一个差距是，本综述中考虑的大多数模型都描述了自给自足的平衡计分卡，很少考虑到在其研究案例中额外生物质或生物产品的进口，或剩余的潜在出口。因此，缩小生物质供应设计和优化方面的这些差距将有助于将平衡计分卡模型整合到更广泛的经济模型中。

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引用次数: 0

Surrogate-based multi-objective optimisation via tree regression 基于代理的树回归多目标优化

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

Computers & Chemical Engineering

Pub Date : 2025-12-26 DOI: 10.1016/j.compchemeng.2025.109538

Artemis Tsochatzidi , Georgios I. Liapis , Francesca Cenci , Magdalini Aroniada , Lazaros G. Papageorgiou

Modern industries rely on advanced modelling techniques to enhance process efficiency, yet the computational complexity of these models often limits their direct use in optimisation. To tackle this issue, surrogate-based approaches for optimising manufacturing flowsheets can be used. In this work, we introduce a multi-objective tree regression approach for surrogate-based optimisation, integrating a multi-target tree regression model to approximate complex process dynamics. The proposed approach can be extended and formulated as a strategic decision-making problem, to reveal optimal trade-offs between conflicting objectives such as yield, process mass intensity, and purity in Pharmaceutical Manufacturing. By combining Pareto-fronts with game-theoretic and/or compromise solutions, the methodology offers a systematic way to define the limits of the feasible space and identify optimal operational strategies in the absence of decision making preferences. The proposed approach enhances interpretability, computational efficiency, and practical applicability, offering a powerful tool for decision-making in pharmaceutical manufacturing and beyond.

现代工业依靠先进的建模技术来提高流程效率，然而这些模型的计算复杂性往往限制了它们在优化中的直接使用。为了解决这个问题，可以使用基于代理的方法来优化制造流程。在这项工作中，我们引入了一种多目标树回归方法，用于基于代理的优化，集成了一个多目标树回归模型来近似复杂的过程动力学。所提出的方法可以扩展并制定为战略决策问题，以揭示在药物制造中产量，工艺质量强度和纯度等冲突目标之间的最佳权衡。通过将帕累托前沿与博弈论和/或妥协解决方案相结合，该方法提供了一种系统的方法来定义可行空间的极限，并在没有决策偏好的情况下确定最佳操作策略。所提出的方法增强了可解释性、计算效率和实用性，为制药等行业的决策提供了强大的工具。

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引用次数: 0

Stationarity fusion with SVM: A stationary combined features support vector machine approach for blast furnace iron-making process fault diagnosis 支持向量机与平稳融合：一种用于高炉炼铁过程故障诊断的平稳组合特征支持向量机方法

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

Computers & Chemical Engineering

Pub Date : 2025-12-24 DOI: 10.1016/j.compchemeng.2025.109543

Yang Cao , Chunjie Yang , Siwei Lou , Yuelin Yang

Blast furnace iron-making process (BFIP), constituting the core of modern steel production, presents formidable diagnostic challenges due to its inherent nonlinear dynamics and pronounced nonstationary characteristics. Addressing these challenges, we introduce the Stationary Combined Features Support Vector Machine (SCF-SVM) - a novel hybrid diagnostic framework that synergistically combines: a Differential Dynamic Feature (DDF) extraction module that precisely decouples the process’s complex temporal dynamics, and a Stationary Support Vector Machine (SSVM) classifier specifically engineered to handle nonstationary process behavior. This innovation establishes a new paradigm for BFIP condition monitoring, where the DDF component effectively captures transitional process states while the SSVM ensures robust classification under nonstationary conditions. Comprehensive validation using real-world BFIP operational data demonstrates the framework’s significant advancements over existing methods, achieving a 3.0% reduction in false alarms and a remarkable 9.5% enhancement in detection accuracy. Furthermore, we implement a dual-mode diagnostic system featuring seamless offline training-to-online deployment capability, confirming both the methodological superiority and practical viability for industrial applications.

高炉炼铁过程是现代钢铁生产的核心，由于其固有的非线性动力学和显著的非平稳特性，对高炉炼铁过程的诊断提出了巨大的挑战。为了应对这些挑战，我们引入了平稳组合特征支持向量机（SCF-SVM）——一种新型混合诊断框架，它协同结合了：精确解耦过程复杂时间动态的差分动态特征（DDF）提取模块，以及专门设计用于处理非平稳过程行为的平稳支持向量机（SSVM）分类器。这一创新为BFIP状态监测建立了一个新的范例，其中DDF组件有效地捕获过渡过程状态，而SSVM确保在非平稳条件下进行鲁棒分类。使用实际BFIP操作数据进行的综合验证表明，该框架比现有方法有了显著的进步，误报率降低了3.0%，检测精度提高了9.5%。此外，我们实现了一个双模式诊断系统，具有无缝的离线培训到在线部署能力，证实了方法上的优越性和工业应用的实际可行性。

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引用次数: 0

Safe deployment of offline reinforcement learning via input convex action correction 通过输入凸动作校正的离线强化学习的安全部署

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

Computers & Chemical Engineering

Pub Date : 2025-12-24 DOI: 10.1016/j.compchemeng.2025.109535

Alex Durkin , Jasper Stolte , Matthew Jones , Raghuraman Pitchumani , Bei Li , Christian Michler , Mehmet Mercangöz

Offline reinforcement learning (offline RL) offers a promising framework for developing control strategies in chemical process systems using historical data, without the risks or costs of online experimentation. This work investigates the application of offline RL to the safe and efficient control of an exothermic polymerisation continuous stirred-tank reactor. We introduce a Gymnasium-compatible simulation environment that captures the reactor’s nonlinear dynamics, including reaction kinetics, energy balances, and operational constraints. The environment supports three industrially relevant scenarios: startup, grade change down, and grade change up. It also includes reproducible offline datasets generated from proportional–integral controllers with randomised tunings, providing a benchmark for evaluating offline RL algorithms in realistic process control tasks.

We assess behaviour cloning and implicit Q-learning as baseline algorithms, highlighting the challenges offline agents face, including steady-state offsets and degraded performance near setpoints. To address these issues, we propose a novel deployment-time safety layer that performs gradient-based action correction using partially input convex neural networks (PICNNs) as learned cost models. The PICNN enables real-time, differentiable correction of policy actions by descending a convex, state-conditioned cost surface, without requiring retraining or environment interaction.

Experimental results show that offline RL, particularly when combined with convex action correction, can outperform traditional control approaches and maintain stability across all scenarios. These findings demonstrate the feasibility of integrating offline RL with interpretable and safety-aware corrections for high-stakes chemical process control, and lay the groundwork for more reliable data-driven automation in industrial systems.

离线强化学习（Offline reinforcement learning，离线RL）提供了一个很有前途的框架，可以利用历史数据开发化学过程系统的控制策略，而无需在线实验的风险或成本。本文研究了离线RL在放热聚合连续搅拌槽反应器安全有效控制中的应用。我们介绍了一个体育馆兼容的模拟环境，捕捉反应堆的非线性动力学，包括反应动力学，能量平衡和操作约束。该环境支持三种与工业相关的场景：启动、等级降级和等级升级。它还包括由随机调谐的比例积分控制器生成的可重复离线数据集，为在实际过程控制任务中评估离线RL算法提供基准。我们评估了行为克隆和隐式q -学习作为基线算法，突出了离线代理面临的挑战，包括稳态偏移和在设定值附近的性能下降。为了解决这些问题，我们提出了一种新的部署时间安全层，该层使用部分输入凸神经网络（PICNNs）作为学习成本模型执行基于梯度的动作校正。PICNN通过下降一个凸的、状态条件的成本面来实现实时的、可微分的策略行为校正，而不需要再训练或环境交互。实验结果表明，离线强化学习，特别是与凸动作校正相结合时，可以优于传统的控制方法，并在所有场景下保持稳定性。这些发现证明了将离线强化学习与可解释和安全意识校正集成在高风险化学过程控制中的可行性，并为工业系统中更可靠的数据驱动自动化奠定了基础。

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引用次数: 0

End-to-end reinforcement learning of Koopman models for eNMPC of an air separation unit 空分装置eNMPC的Koopman模型端到端强化学习

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

Computers & Chemical Engineering

Pub Date : 2025-12-24 DOI: 10.1016/j.compchemeng.2025.109540

Daniel Mayfrank , Kayra Dernek , Laura Lang , Alexander Mitsos , Manuel Dahmen

With our recently proposed method based on reinforcement learning (Mayfrank et al., 2024), Koopman surrogate models can be trained for optimal performance in specific (economic) nonlinear model predictive control ((e)NMPC) applications. So far, our method has exclusively been demonstrated on a small-scale case study. Herein, we show that our method scales well to a more challenging demand response case study built on a large-scale model of a single-product (nitrogen) air separation unit. Across all numerical experiments, we assume observability of only a few realistically measurable plant variables. Compared to a purely system identification-based Koopman eNMPC, which generates small economic savings but frequently violates constraints, our method delivers similar economic performance while avoiding constraint violations.

通过我们最近提出的基于强化学习的方法（Mayfrank等人，2024），可以在特定（经济）非线性模型预测控制（(e)NMPC）应用中训练Koopman代理模型以获得最佳性能。到目前为止，我们的方法只在一个小规模的案例研究中得到了证明。在此，我们表明，我们的方法可以很好地适用于建立在单产品（氮气）空气分离装置的大型模型上的更具挑战性的需求响应案例研究。在所有数值实验中，我们假设只有少数实际可测量的植物变量是可观测的。与纯粹基于系统识别的Koopman eNMPC相比，我们的方法在避免违反约束的同时提供了类似的经济性能，而Koopman eNMPC产生了少量的经济节省，但经常违反约束。

引用次数: 0

Determination of minimal target indices for cyanobacteria-based biorefineries and optimal design of the metabolic network 蓝藻生物精炼厂最小目标指标确定及代谢网络优化设计

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

Computers & Chemical Engineering

Pub Date : 2025-12-24 DOI: 10.1016/j.compchemeng.2025.109539

Romina Lasry Testa , Fernando D. Ramos , Matías Ramos , Vanina Estrada , Maria Soledad Diaz

In this work, we propose a mixed-integer nonlinear multiobjective optimization framework for the determination of minimal target indices for the sustainable design of an integrated cyanobacteria-based biorefinery and its heat exchanger network (HEN). The potential production of phycocyanin and zeaxanthin, poly (3-hydroxybutyrate) (PHB), fourth-generation bioethanol, biogas, hydrogen and diethyl ether is analyzed. The main objective is to determine the minimal target indices (productivity, yield, titer) for Synechocystis sp. that must be reached to achieve a sustainable biorefinery design, by imposing lower bounds on an economic parameter (Net Present Value) and a multi-criteria sustainability metric (Sustainability Net Present Value).

For in silico strains, a bilevel optimization framework identifies gene knockouts in a genome-scale metabolic model of Synechocystis sp. PCC 6803 that couple product synthesis to growth. The resulting strain-specific performance indices are compared with their minimum feasible targets obtained from the process-level optimization. This integrated approach extends previous studies by combining genome-scale metabolic modelling with techno-economic and sustainability analysis within a unified optimization framework. Numerical results indicate that the minimal target indices are largely surpassed by two of the in silico strains, the wild-type and the coupled ethanol-producing, as well as by the in vivo strains. The proposed framework provides a quantitative basis to assess the feasibility of cyanobacteria-based biorefineries and to guide future metabolic engineering and process design strategies.

在这项工作中，我们提出了一个混合整数非线性多目标优化框架，用于确定基于蓝藻的生物精炼厂及其热交换器网络（HEN）可持续设计的最小目标指标。分析了藻蓝蛋白和玉米黄质、聚3-羟基丁酸酯（PHB）、第四代生物乙醇、沼气、氢气和乙醚的生产潜力。主要目标是通过对经济参数（净现值）和多标准可持续性度量（可持续性净现值）施加下限，确定为实现可持续生物炼制设计必须达到的最小目标指数（生产率、产量、滴度）。对于硅酸菌株，一个双水平优化框架确定了聚囊藻（Synechocystis sp. PCC 6803）基因组尺度代谢模型中的基因敲除，该模型将产物合成与生长结合起来。将得到的各菌株性能指标与工艺级优化得到的最小可行目标进行了比较。这种综合方法通过在统一的优化框架内将基因组尺度代谢模型与技术经济和可持续性分析相结合，扩展了以前的研究。数值结果表明，野生型和耦合产乙醇菌株以及体内菌株在很大程度上超过了最小目标指数。该框架为蓝藻生物精炼厂的可行性评估提供了定量基础，并为未来的代谢工程和工艺设计策略提供了指导。

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引用次数: 0

Graph neural networks for soft sensors: Learning from process topology and operational data 用于软传感器的图形神经网络：从过程拓扑和操作数据中学习

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

Computers & Chemical Engineering

Pub Date : 2025-12-24 DOI: 10.1016/j.compchemeng.2025.109532

Maximilian F. Theisen, Gabrie M.H. Meesters, Artur M. Schweidtmann

Soft sensors estimate process variables that are difficult or impossible to measure directly by using mathematical models and available sensor data, e.g., product concentrations. Machine learning-based approaches have become popular for soft sensing tasks. These approaches offer automatic modeling using historical process data but lack basic process information, such as the process topology. This can lead to (1) modeling of correlations instead of causation between process measurements, (2) model deterioration in deployment due to unseen process scenarios, and (3) large data requirements. To overcome these shortcomings, we propose a novel ML modeling approach incorporating the process topology into soft sensor models for improved spatio-temporal modeling. For this, we propose process topology-aware graph neural networks. We combine process topology and sensor data by representing process data in a directed graph and leverage these process graphs to train graph neural networks. Our method demonstrates enhanced model robustness, reduced data requirements, and more intuitive data representations compared to standard black-box machine learning modeling approaches. Overall, this work introduces a new paradigm for soft sensing by directly embedding process information into the data, paving the way for more efficient and reliable digital twin applications.

软传感器通过使用数学模型和可用的传感器数据来估计难以或不可能直接测量的过程变量，例如产品浓度。基于机器学习的方法在软测量任务中已经很流行。这些方法使用历史过程数据提供自动建模，但缺乏基本的过程信息，例如过程拓扑。这可能导致(1)对过程度量之间的相关性而不是因果关系进行建模，(2)由于不可见的过程场景而导致部署中的模型恶化，以及(3)大数据需求。为了克服这些缺点，我们提出了一种新的机器学习建模方法，将过程拓扑纳入软传感器模型，以改进时空建模。为此，我们提出了过程拓扑感知图神经网络。我们通过在有向图中表示过程数据来结合过程拓扑和传感器数据，并利用这些过程图来训练图神经网络。与标准的黑箱机器学习建模方法相比，我们的方法展示了增强的模型鲁棒性、减少的数据需求以及更直观的数据表示。总的来说，这项工作通过将过程信息直接嵌入到数据中，为软测量引入了一种新的范例，为更有效和可靠的数字孪生应用铺平了道路。

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引用次数: 0

Improving the precision of crude oil prices using hybrid modeling methods 利用混合建模方法提高原油价格的精度

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

Computers & Chemical Engineering

Pub Date : 2025-12-24 DOI: 10.1016/j.compchemeng.2025.109541

Chunyu Du , Wenbo Ma

Energy security, investment choices, and risk management all depend on accurate crude oil price forecasting, which is still challenging because oil markets are nonlinear, volatile, and non-stationary. The current work posits a hybrid forecasting framework using the Multivariate Empirical Mode Decomposition-Barnacles Mating Optimizer- Interpretable Feature Temporal Self-Attention Transformer (MEMD-BMO-IFTT) model. The Interpretable Feature Temporal Self-Attention Transformer is used for implicitly capturing long-term temporal dependencies, the Barnacles Mating Optimizer is used for effective parameter optimization, and Multivariate Empirical Mode Decomposition is used for extracting multi-scale, informative features. The model was tested against prevalent algorithms such as Extreme Gradient Boosting with Random Forest, Extended Long Short-Term Memory, an IFTT, and MEMD-FA-IFTT based on June 2014 to October 2023 daily technical indicators, fundamental indicators, and trading volume data. It is observed that MEMD-BMO-IFTT is more efficient in the prediction process with a test coefficient of determination of 0.9937. While real-world backtesting demonstrated its usefulness by producing higher cumulative returns, lower drawdowns, and stronger risk-adjusted performance when compared to a buy-and-hold strategy, out-of-sample testing validated its generalization to unseen data. The MEMD-BMO-IFTT framework provides a reliable, understandable, and practically useful solution for forecasting crude oil prices. This novel hybrid model can provide a valuable tool for researchers and investors in forecasting other financial markets.

能源安全、投资选择和风险管理都依赖于准确的原油价格预测，但由于石油市场具有非线性、波动性和非平稳性，这仍然具有挑战性。目前的工作假设了一个混合预测框架，使用多元经验模式分解-藤壶配对优化器-可解释特征时间自注意转换器（MEMD-BMO-IFTT）模型。可解释特征时间自关注转换器用于隐式捕获长期时间依赖性，Barnacles匹配优化器用于有效参数优化，多元经验模式分解用于提取多尺度、信息特征。基于2014年6月至2023年10月的每日技术指标、基本指标和交易量数据，对该模型进行了基于随机森林的极端梯度增强、扩展长短期记忆、IFTT和MEMD-FA-IFTT等流行算法的测试。MEMD-BMO-IFTT在预测过程中更有效，其检验决定系数为0.9937。与“买入并持有”策略相比，实际回测证明了该策略的有效性，可以产生更高的累积回报、更低的回撤，以及更强的风险调整后的表现，而样本外测试则验证了其对未知数据的泛化。MEMD-BMO-IFTT框架为预测原油价格提供了可靠、易懂、实用的解决方案。这种新的混合模型可以为研究人员和投资者预测其他金融市场提供有价值的工具。

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引用次数: 0