Computers & Chemical Engineering最新文献_第6页

A cloud-enabled digital twin platform for upstream processing in biotechnology: Integrating CFD, compartmentalization, and reaction kinetics 生物技术上游处理的云端数字孪生平台：集成CFD、分区和反应动力学

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

Computers & Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-04 DOI: 10.1016/j.compchemeng.2025.109488

Xiyan Li , Sebastian L. Jensen , Noah B. Christiansen , Elham Ramin , Johan le Nepvou de Carfort , Johannes Schmölder , Eric von Lieres , Krist V. Gernaey

Biotechnology process modeling has become an essential tool in both research and industrial settings, offering a cost-effective and efficient alternative to extensive physical experimentation. It enables the simulation and analysis of complex biological systems, supporting faster development cycles and better decision-making. To foster collaboration and knowledge sharing in this domain, we present an open source cloud-enabled platform for upstream biotechnology process modeling. The platform provides an integrated environment where users can combine computational fluid dynamics (CFD), compartmental models, and kinetic simulations within a unified and modular interface. Each component of the model operates independently, but can be seamlessly coupled through a standardized API. The system is designed to support both research and educational use cases, with an emphasis on accessibility, extensibility, and reproducibility. This paper outlines the platform architecture and implementation, highlights the technical challenges addressed in model integration, and discusses opportunities for future development. By lowering technical barriers and encouraging community-driven innovation, the platform aims to advance digital twin applications in biotechnology upstream processing.

生物技术过程建模已成为研究和工业环境中的重要工具，为广泛的物理实验提供了一种经济有效的替代方法。它能够模拟和分析复杂的生物系统，支持更快的开发周期和更好的决策。为了促进这一领域的协作和知识共享，我们提出了一个开源的云平台，用于上游生物技术过程建模。该平台提供了一个集成的环境，用户可以在一个统一的模块化界面中结合计算流体动力学（CFD）、隔室模型和动力学模拟。模型的每个组件都独立运行，但可以通过标准化的API无缝耦合。该系统旨在支持研究和教育用例，强调可访问性、可扩展性和可再现性。本文概述了平台的架构和实现，强调了模型集成中所面临的技术挑战，并讨论了未来发展的机会。通过降低技术壁垒和鼓励社区驱动的创新，该平台旨在推进数字孪生在生物技术上游加工中的应用。

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引用次数: 0

Harnessing machine learning for water energy food nexus sustainability: developing a surrogate to multi-objective optimization 利用机器学习实现水能食物关系的可持续性：开发多目标优化的替代方法

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

Computers & Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-12 DOI: 10.1016/j.compchemeng.2025.109526

Fatima Mansour , Michael Yehya , Ali Ayoub , Mahmoud Al-Hindi

Integrated management of water, energy, and food resources is critical for achieving sustainability under rising environmental and demographic pressures, yet existing approaches either lack computational efficiency for real-time decision support or fail to capture the full complexity of sectoral interdependencies. This study presents a Water Energy Food Nexus Machine Learning based surrogate Model (WEFN-MLM). The key innovation lies in training a Random Forest algorithm on comprehensive multi-objective optimization outputs from thousands of diverse scenarios, enabling the model to learn complex nonlinear interdependencies and resource trade-offs without requiring explicit mathematical formulation of system relationships. A high-resolution Multi-Objective Optimization WEFN model (MOO-WEFN) is used to generate the training dataset, incorporating constraints for resource availability, caloric requirements, and environmental thresholds. The trained model demonstrates high predictive accuracy, with most output variables achieving R² values above 0.90 and cosine similarity scores near 1.0. Normalized absolute error analysis reveals strong performance consistency across system-level metrics, with select deviations in sector-specific outputs, particularly those highly sensitive to scenario dynamics or underrepresented in the training space. Compared to traditional optimization, the surrogate model achieves up to a 300,000-fold reduction in computation time. The surrogate model is validated using a randomly generated test set of scenarios that enables direct comparison between surrogate predictions and optimization results. The results highlight the model’s effectiveness for high-resolution nexus analysis and scenario exploration, while also acknowledging trade-offs between speed and precision. Findings underscore the importance of diverse training scenarios, careful application boundaries, and integration with policy processes to support resilient resource planning.

在不断上升的环境和人口压力下，水、能源和粮食资源的综合管理对于实现可持续性至关重要，但现有方法要么缺乏实时决策支持的计算效率，要么无法捕捉部门相互依存关系的全部复杂性。本研究提出了一个基于水-能源-食品关系机器学习的代理模型（WEFN-MLM）。关键的创新在于对随机森林算法进行训练，使其能够在数千个不同场景的综合多目标优化输出上学习复杂的非线性相互依赖关系和资源权衡，而无需明确的系统关系数学公式。高分辨率多目标优化WEFN模型（MOO-WEFN）用于生成训练数据集，该模型结合了资源可用性、热量需求和环境阈值的约束。训练后的模型显示出较高的预测精度，大多数输出变量的R²值达到0.90以上，余弦相似度得分接近1.0。标准化绝对误差分析揭示了跨系统级指标的强大性能一致性，在特定部门的输出中有选择偏差，特别是那些对场景动态高度敏感或在训练空间中代表性不足的输出。与传统优化相比，代理模型的计算时间减少了30万倍。代理模型使用随机生成的场景测试集进行验证，可以直接比较代理预测和优化结果。结果突出了该模型在高分辨率关联分析和场景探索方面的有效性，同时也承认了速度和精度之间的权衡。研究结果强调了多样化培训方案、谨慎的应用边界以及与政策流程整合以支持弹性资源规划的重要性。

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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 : 2026-03-01 Epub 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

Cluster-based adaptive sampling methodology for systems modeling 基于聚类的系统建模自适应抽样方法

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

Computers & Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-12 DOI: 10.1016/j.compchemeng.2025.109527

Maaz Ahmad , Yin Jun , Marta Moreno-Benito , Senthil Kumarasamy , Harsha Nagesh Rao , Jason Mustakis , Iftekhar A Karimi

Modeling real-world (experimental) or simulated (computational) systems using data-driven surrogate models involves selecting a sampling technique to generate the input-output data for training and selecting a surrogate form. In this work, we present a novel sampling technique, Cluster-based Adaptive Sampling, that generates training data smartly and adaptively for developing surrogate models over a given input domain. CAS iteratively clusters sampled points, defines Voronoi tessellation of cluster centroids, and approximates the tessellations using simple hypercubes. It then searches locally and globally over the domain at each iteration to identify nonlinear and under-explored regions respectively, where it samples two new points using a distance-based metric. CAS is agnostic to surrogate form and terminates automatically based on a surrogate quality metric. We assessed CAS against two existing sampling techniques on 40 diverse test functions using six surrogate forms. CAS outperformed both techniques in developing more accurate surrogates for a given computational effort and required lower computational effort for a specified accuracy across most test functions and forms. We highlight the practical applicability of CAS in modeling two pharmaceutical processes and showcase its superior performance over the two techniques.

使用数据驱动的代理模型对真实世界（实验）或模拟（计算）系统进行建模涉及到选择一种采样技术来生成用于训练的输入输出数据和选择代理表单。在这项工作中，我们提出了一种新的采样技术，基于簇的自适应采样，它可以在给定的输入域上智能地自适应地生成训练数据，用于开发代理模型。CAS迭代聚类采样点，定义聚类质心的Voronoi镶嵌，并使用简单的超立方体近似镶嵌。然后，在每次迭代中，它在局部和全局搜索域，分别识别非线性和未开发的区域，在这些区域中，它使用基于距离的度量对两个新的点进行采样。CAS与代理表单无关，并根据代理质量度量自动终止。我们使用6个代理表单对40种不同测试功能的两种现有抽样技术进行了CAS评估。CAS在为给定的计算量开发更精确的代理方面优于这两种技术，并且在大多数测试函数和表单中需要更低的计算量来实现指定的精度。我们强调了CAS在两种制药过程建模中的实际适用性，并展示了其优于两种技术的性能。

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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 : 2026-03-01 Epub 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

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 : 2026-03-01 Epub 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

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 : 2026-03-01 Epub 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

Safe reinforcement learning via adaptive robust model predictive shielding 基于自适应鲁棒模型预测屏蔽的安全强化学习

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

Computers & Chemical Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-10 DOI: 10.1016/j.compchemeng.2025.109521

Hilde Gerold, Sergio Lucia

Ensuring constraint satisfaction during the deployment of reinforcement learning (RL) controllers remains a key challenge for safety-critical systems. Model predictive shielding addresses this by verifying proposed actions through predictive models and replacing unsafe ones with a backup policy, but existing approaches can be overly conservative, computationally demanding, and difficult to design for nonlinear systems with uncertainty.

We propose Adaptive Robust Model Predictive Shielding to overcome these limitations. First, we employ an approximate robust nonlinear model predictive controller as the backup policy, trained offline from multi-stage robust model predictive control data. This robust model predictive shielding approach retains safety under uncertainty while enabling real-time applicability. Second, we introduce an adaptive safety parameter in the RL observation space, allowing the agent to dynamically adjust its conservativeness. Our adaptive model predictive shielding method thus enhances safety and adapts to current uncertainty levels while avoiding excessive conservatism. When deployed with a safe backup policy, adaptive robust model predictive shielding retains safety under uncertainty and reduces unnecessary backup interventions. Simulation results for a nonlinear continuous stirred tank reactor with parametric uncertainty show that the proposed adaptive robust model predictive shielding approach reduces interventions of backup policies while still guaranteeing safety. This framework can be especially beneficial for safe RL of chemical processes where a combination of safety guarantees, high performance, and real-time feasibility is critical.

在部署强化学习（RL）控制器期间确保约束满足仍然是安全关键系统的关键挑战。模型预测屏蔽通过通过预测模型验证建议的操作，并用备用策略替换不安全的操作来解决这个问题，但是现有的方法可能过于保守，计算要求高，并且难以设计具有不确定性的非线性系统。我们提出了自适应鲁棒模型预测屏蔽来克服这些限制。首先，采用近似鲁棒非线性模型预测控制器作为备份策略，从多阶段鲁棒模型预测控制数据中进行离线训练。这种鲁棒模型预测屏蔽方法在不确定性下保持安全性，同时实现实时适用性。其次，我们在RL观测空间中引入自适应安全参数，允许智能体动态调整其保守性。因此，我们的自适应模型预测屏蔽方法提高了安全性，适应当前的不确定性水平，同时避免了过度的保守性。当与安全备份策略一起部署时，自适应鲁棒模型预测屏蔽在不确定情况下保持安全性，并减少不必要的备份干预。对具有参数不确定性的非线性连续搅拌槽式反应器的仿真结果表明，提出的自适应鲁棒模型预测屏蔽方法在保证安全性的同时减少了备用策略的干预。该框架对于化学过程的安全RL尤其有益，因为安全保证、高性能和实时可行性的结合至关重要。

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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 : 2026-03-01 Epub 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 : 2026-03-01 Epub 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