Digital Chemical Engineering最新文献_第5页

Fault detection using multiscale recursive principal component analysis for chemical process systems 基于多尺度递归主成分分析的化工过程系统故障检测

IF 4.1 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-09-01 DOI: 10.1016/j.dche.2025.100264

Oktorifo Gardiola , Abdulhalim Shah Maulud , Muhammad Nawaz , Nabila Farhana Jamaludin

Process monitoring is essential for maintaining operational safety and product quality in chemical industries. Although conventional fault detection techniques are widely used, their static nature often leads to high false alarm rates (FAR) and missed detection rates (MDR) under dynamic conditions. To address these limitations, this study proposes a Multiscale Recursive Principal Component Analysis (MSRPCA)-based fault detection framework that combines multiscale signal decomposition with the adaptive capabilities of Recursive PCA (RPCA). The MSRPCA approach isolates process variations across different frequency bands while continuously updating the Principal Component Analysis (PCA) model using a moving window mechanism. This enables real-time adaptability and enhanced noise resistance. The proposed method is validated using the Tennessee Eastman Process (TEP), a widely used benchmark for chemical process monitoring under a range of fault types, including step, drift, and random variation disturbances. Fault detection performance is quantitatively assessed using FAR and MDR metrics across 20 distinct fault scenarios. The results demonstrate that MSRPCA consistently outperforms traditional techniques, significantly reducing false alarms while improving fault detection accuracy. For instance, in Fault 16, the MDR in the Hotelling’s T² (T²) chart decreased from 70.5 % (PCA) to 10.5 % (MSRPCA), while the FAR in the Squared Prediction Error (SPE) chart dropped from 21.3 % to 0 %. These findings underscore the robustness and effectiveness of MSRPCA for real-time fault detection in complex, time-varying, and noisy industrial environments.

在化工行业中，过程监控对于维护操作安全和产品质量至关重要。传统的故障检测技术虽然被广泛应用，但其静态特性往往导致在动态条件下的高虚警率和漏检率。为了解决这些局限性，本研究提出了一种基于多尺度递归主成分分析（MSRPCA）的故障检测框架，该框架将多尺度信号分解与递归主成分分析（RPCA）的自适应能力相结合。MSRPCA方法在使用移动窗口机制不断更新主成分分析（PCA）模型的同时，隔离了不同频带的过程变化。这实现了实时适应性和增强的抗噪声能力。采用田纳西伊士曼过程（TEP）验证了所提出的方法，TEP是一种广泛使用的化学过程监测基准，用于一系列故障类型，包括阶跃，漂移和随机变化干扰。在20个不同的故障场景中，使用FAR和MDR度量对故障检测性能进行定量评估。结果表明，MSRPCA持续优于传统技术，在提高故障检测精度的同时显著减少了误报。例如，在故障16中，Hotelling 's T2 （T2）图中的MDR从70.5% （PCA）下降到10.5% (MSRPCA)，而平方预测误差（SPE）图中的FAR从21.3%下降到0%。这些发现强调了MSRPCA在复杂、时变和嘈杂的工业环境中实时故障检测的鲁棒性和有效性。

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引用次数: 0

Editorial: Special issue on Emerging Stars in Digital Chemical Engineering 社论：数字化工新星特刊

IF 4.1 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-09-01 DOI: 10.1016/j.dche.2025.100247

Jin Xuan , Jinfeng Liu

引用次数: 0

Root cause identification of fault in hot-rolling process by causal plot 用因果图识别热轧过程故障的根本原因

IF 4.1 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-08-25 DOI: 10.1016/j.dche.2025.100263

Koichi Fujiwara , Yoshiaki Uchida , Taketsugu Osaka

In the steel manufacturing industry, a hot-rolling process produces a thick steel plate from a slab as a batch operation; however, off-spec steel plates are sometimes produced when abnormalities occur during rolling operations. To improve the product yield, it is necessary to appropriately ascertain the root cause of a fault. Because the physicochemical behaviors of the slab during hot-rolling are complicated and yet to be fully understood, we adopted a data-driven approach to identify the cause of the fault in the hot-rolling process. We previously proposed a data-driven fault diagnosis method, referred to as a causal plot, that considers the causal relationships between process variables and monitoring indexes for process monitoring. In the causal plot, monitoring indexes were calculated using existing process monitoring methods, and the causal relationships between the process variables and the calculated monitoring indices were estimated. A linear non-Gaussian acyclic model (LiNGAM) can be adopted for causal inferences between the process variables and calculated monitoring indexes. In this study, we propose a new fault diagnosis method for a batch process, referred to as a b-causal plot, utilizing the causal plot and dynamic time warping (DTW). We analyzed real operation data when defective coils were produced in the hot-rolling process with the proposed b-causal plot and confirmed that the identified root cause was consistent with process engineers’ knowledge, which is typically a low-importance variable that operators do not constantly monitor in daily operation. Because the root cause identification of faults is crucial for maintaining product quality and efficiency in batch processes, the proposed b-causal plot contributes to improving productivity across industries, as demonstrated in this work.

在钢铁制造行业中，热轧工艺将板坯批量生产成厚钢板；然而，当轧制过程中出现异常时，有时会产生不符合规格的钢板。为了提高产品成品率，有必要适当地确定故障的根本原因。由于板坯在热轧过程中的物理化学行为复杂且尚未完全了解，我们采用数据驱动的方法来识别热轧过程中的故障原因。我们之前提出了一种数据驱动的故障诊断方法，称为因果图，该方法考虑过程变量与监控指标之间的因果关系，进行过程监控。在因果图中，利用现有的过程监测方法计算监测指标，并估计过程变量与计算出的监测指标之间的因果关系。采用线性非高斯无循环模型（LiNGAM）对过程变量与计算出的监测指标进行因果推理。在这项研究中，我们提出了一种新的故障诊断方法，称为b-因果图，利用因果图和动态时间规整（DTW）。我们利用提出的b因果图分析了热轧过程中产生缺陷线圈的实际操作数据，并确认确定的根本原因与工艺工程师的知识一致，这通常是操作员在日常操作中不经常监控的低重要性变量。由于故障的根本原因识别对于维持批处理过程中的产品质量和效率至关重要，因此所提出的b-因果图有助于提高各行业的生产率，正如本工作所示。

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引用次数: 0

Multi-criteria decision support for flexible dividing wall distillation columns 柔性分壁精馏塔多准则决策支持

IF 4.1 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-08-19 DOI: 10.1016/j.dche.2025.100258

David Mogalle , Patrick Otto Ludl , Tobias Seidel , Lea Trescher , Thomas Grützner , Michael Bortz

When designing a dividing wall column, some decisions regarding the layout of the column cannot be altered once the unit is built, whereas decisions regarding the column’s operation can, to some extent, be adjusted later. During the design phase, both layout and operation can be optimized to achieve an optimal column performance. However, such solutions are tailored to pre-specified process demands. If these demands change later, the physical layout can become suboptimal. Hence, we are interested in design decisions that keep the losses in performance minimal, leading to a column design that is flexible across different demands.

In this paper, we present a new methodology to measure flexibility. The approach is based on multi-criteria optimization, where Pareto fronts with fixed design variables and optimized operating variables are compared to an ideal Pareto front that optimizes both the layout and the operation simultaneously. The difference between two such fronts, representing the losses in performance of the fixed layout for a wide range of demands, is measured by a novel flexibility indicator. We apply our methodology to designing a dividing wall column separating an example mixture. A fast computation of the corresponding Pareto fronts is achieved by solving the arising optimization problems using a reduction method based on stage-to-stage calculations.

当设计分隔墙柱时，有关柱的布局的一些决定一旦建成就不能改变，而有关柱的操作的决定可以在一定程度上稍后调整。在设计阶段，布局和操作都可以优化，以实现最佳的列性能。然而，这样的解决方案是针对预先指定的过程需求量身定制的。如果这些需求后来发生了变化，那么物理布局可能会变得不理想。因此，我们感兴趣的是使性能损失最小化的设计决策，从而使列设计能够灵活地满足不同的需求。在本文中，我们提出了一种新的方法来衡量灵活性。该方法基于多标准优化，将具有固定设计变量和优化操作变量的帕累托前沿与同时优化布局和操作的理想帕累托前沿进行比较。两个这样的前沿之间的差异，代表了固定布局在广泛需求范围内的性能损失，是通过一个新的灵活性指标来衡量的。我们应用我们的方法来设计分离样品混合物的分壁柱。通过使用基于逐级计算的约简方法解决出现的优化问题，实现了相应Pareto前沿的快速计算。

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引用次数: 0

Neural network implementation of model predictive control with stability guarantees 神经网络实现具有稳定性保证的模型预测控制

IF 4.1 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-08-18 DOI: 10.1016/j.dche.2025.100262

Arthur Khodaverdian , Dhruv Gohil , Panagiotis D. Christofides

This work explores the use of supervised learning on data generated by a model predictive controller (MPC) to train a neural network (NN). The goal is to create an approximate control policy that can replace the MPC, offering reduced computational complexity while maintaining stability guarantees. Through the use of Lyapunov-based stability constraints, an MPC can be designed to guarantee stability. Once designed, this MPC can be used to generate a dataset of various state-space points and their resulting immediate optimal control actions. With the MPC dataset representing an optimal control policy, an NN is trained to function as a direct substitute for the MPC. The resulting approximate control policy can then be applied in real-time to the process, with stability guarantees being enforced through post-inference validation. If, for a given set of sensor readings, the NN yields control actions that violate the Lyapunov stability constraints used in the MPC, the control action is discarded and replaced with stabilizing control from a fallback stabilizing controller. This control architecture is applied to a benchmark chemical reactor model. Using this model, a comprehensive study of the stability, performance, robustness, and computational burden of the approach is carried out.

这项工作探索了对模型预测控制器（MPC）生成的数据使用监督学习来训练神经网络（NN）。目标是创建一个可以取代MPC的近似控制策略，在保持稳定性保证的同时降低计算复杂度。通过使用基于lyapunov的稳定性约束，可以设计出保证稳定性的MPC。一旦设计好，这个MPC可以用来生成各种状态空间点的数据集，以及它们产生的即时最优控制行为。使用MPC数据集表示最优控制策略，训练神经网络作为MPC的直接替代品。由此产生的近似控制策略可以实时应用于过程，并通过推理后验证强制执行稳定性保证。如果对于给定的一组传感器读数，神经网络产生的控制动作违反了MPC中使用的李雅普诺夫稳定性约束，则控制动作被丢弃，并由一个回降稳定控制器的稳定控制取代。将该控制体系结构应用于一个基准化学反应器模型。利用该模型，对该方法的稳定性、性能、鲁棒性和计算量进行了全面的研究。

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引用次数: 0

AutoRL framework for bioprocess control: Optimizing reward function, architecture, and hyperparameters 生物过程控制的AutoRL框架：优化奖励函数、结构和超参数

IF 4.1 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-08-14 DOI: 10.1016/j.dche.2025.100261

D.A. Goulart , R.D. Pereira , F.V. Silva

This study proposes a structured AutoRL framework for the development of deep reinforcement learning (DRL) controllers in chemical process systems. Focusing on the control of a 3

\times

3 MIMO yeast fermentation bioreactor, the methodology jointly optimizes three key internal components of the DRL agent: the reward function, the neural network architecture, and the hyperparameters of the algorithm. A parameterizable logistic reward formulation is introduced to encode control objectives, such as steady-state accuracy, minimalization of actuation effort, and control smoothness, into a flexible and tunable structure. A dual loop optimization strategy combines grid search and Bayesian optimization to systematically explore and refine the agent’s design space. The resulting controller achieved average steady-state errors of 0.009 °C for reactor temperature and 0.19 g/L for ethanol concentration, while maintaining smooth and stable behavior under diverse operational scenarios. By formalizing reward design and integrating it with hyperparameter and architecture optimization, this work delivers a AutoRL methodology for DRL-based control, reducing reliance on expert heuristics and enhancing reproducibility in complex bioprocess applications.

本研究提出了一个结构化的AutoRL框架，用于开发化学过程系统中的深度强化学习（DRL）控制器。该方法以3x3 MIMO酵母发酵生物反应器的控制为重点，对DRL agent的三个关键内部组件：奖励函数、神经网络架构和算法的超参数进行了联合优化。引入了一个参数化的逻辑奖励公式，将控制目标（如稳态精度、驱动努力最小化和控制平滑度）编码为一个灵活可调的结构。采用网格搜索和贝叶斯优化相结合的双环优化策略，系统地探索和细化智能体的设计空间。该控制器在反应器温度和乙醇浓度的平均稳态误差分别为0.009°C和0.19 g/L，同时在各种操作场景下保持平稳稳定的行为。通过将奖励设计形式化并将其与超参数和架构优化相结合，本研究为基于drl的控制提供了一种AutoRL方法，减少了对专家启发式的依赖，并提高了复杂生物过程应用的可重复性。

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引用次数: 0

Solving bilevel problems under uncertainty with embedded neural networks: Incorporating scenario sets as inputs 用嵌入式神经网络解决不确定性下的双层问题：将场景集作为输入

IF 4.1 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-08-13 DOI: 10.1016/j.dche.2025.100253

Isabela Fons Moreno-Palancas, Rubén Ruiz Femenia, Raquel Salcedo Díaz, José A. Caballero

Bilevel optimization is a sub-field of optimization widely valued both in academia and business due to its suitability to identify the best solutions for hierarchical decision-making processes. The predominant approach to solving bilevel problems involves reformulating them as single-level equivalents that can be solved with commercial solvers. However, traditional reformulation techniques are often constrained by the complexity of the lower-level problem, particularly when the number of variables or constraints is large, or uncertain parameters are present.

Given the intrinsic presence of uncertainty in most real-world applications of bilevel optimization, this work proposes a metamodeling approach that approximates the lower level using a neural network. Although this strategy has been satisfactorily applied to deterministic bilevel models, we extend its use to stochastic bilevel problems by training a neural network that learns over a set of realizations of the uncertain parameters. Our methodology is tested on the short-term scheduling of a batch chemical process, a context where classical reformulation approaches become unmanageable due to the presence of differential equations. The results indicate that our approach successfully achieves a single-level reformulation that is computationally tractable and can be solved efficiently even in complex bilevel settings, provided that the lower-level remains manageable and the main complexity arises from its integration into the upper level.

双层优化是优化的一个分支领域，由于其适合于确定分层决策过程的最佳解决方案，因此在学术界和商界都受到广泛的重视。解决两层问题的主要方法是将它们重新表述为可以用商业求解器解决的单层等价问题。然而，传统的重新表述技术往往受到较低层次问题的复杂性的限制，特别是当变量或约束的数量很大或存在不确定参数时。考虑到在大多数现实世界的双层优化应用中存在固有的不确定性，本研究提出了一种元建模方法，该方法使用神经网络近似于较低层次。虽然该策略已令人满意地应用于确定性双层模型，但我们通过训练一个神经网络来学习一组不确定参数的实现，将其应用于随机双层问题。我们的方法在批量化学过程的短期调度上进行了测试，在这种情况下，由于微分方程的存在，经典的重新制定方法变得难以管理。结果表明，我们的方法成功地实现了单级重构，这种重构在计算上是可处理的，即使在复杂的双层设置中也可以有效地解决，前提是较低的层次仍然是可管理的，并且主要的复杂性来自于它与上层的集成。

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引用次数: 0

Temporal PFD-guided graph convolutional networks: a novel approach to process modeling 时间pfd引导图卷积网络：过程建模的新方法

IF 4.1 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-08-04 DOI: 10.1016/j.dche.2025.100260

Hiroki Horiuchi, Yoshiyuki Yamashita

The present study proposes a novel methodology to construct a regression model of process systems, namely, temporal PFD-guided graph convolutional networks (GCN). The approach integrates domain knowledge derived from process flow diagrams (PFDs) and controller configurations into a GCN framework, enabling enhanced state estimation in chemical processes. We introduce a process topology with temporal propagation derived from PFDs to construct robust graph structures for GCNs. The proposed method integrates causal relationships among process variables and their time-series dependencies, enhancing prediction accuracy and adaptability. A case study of a concentration estimation on the Tennessee Eastman Process (TEP) demonstrates the effectiveness of the PFD-guided GCN. The results indicate significant improvements in prediction accuracy compared to 1D-CNN, especially under abnormal operating conditions and when limited training data is available. This approach provides a practical and generalizable solution for process state estimation and soft sensor applications in dynamic and data-sparse industrial environments.

本研究提出了一种新的方法来构建过程系统的回归模型，即时间pfd引导图卷积网络（GCN）。该方法将来自过程流程图（pfd）和控制器配置的领域知识集成到GCN框架中，从而增强了化学过程的状态估计。我们引入了一种由pfd衍生而来的具有时间传播的过程拓扑结构来构建GCNs的鲁棒图结构。该方法集成了过程变量之间的因果关系及其时间序列依赖关系，提高了预测精度和适应性。对田纳西伊士曼过程（TEP）的浓度估计进行了实例研究，证明了pfd引导的GCN的有效性。结果表明，与1D-CNN相比，预测精度有了显著提高，特别是在异常操作条件下和可用的训练数据有限的情况下。该方法为动态和数据稀疏工业环境下的过程状态估计和软测量应用提供了一种实用的、可推广的解决方案。

引用次数: 0

Implementation of mixed reality for data visualization in liquid soap filling processes 液体肥皂灌装过程中数据可视化的混合现实实现

IF 4.1 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-07-31 DOI: 10.1016/j.dche.2025.100254

Andrés Felipe Hurtado, Carlos Mario Paredes, Kelly Daniella Marín Montealegre, Juan Pablo González Molina, Juan Pablo Álzate Saiz

Visualizing data in industrial processes represents a critical component of Industry 4.0, offering opportunities for better decision-making, real-time monitoring, and process optimization. This article presents an architecture design that enables data capture from control technologies and integrates into a Mixed Reality (MR) system for data visualization in the context of liquid soap filling processes. The system integrates real-time process data from a programmable logic controller (PLC) into MR technology, thereby creating an immersive platform that serves to enhance understanding and interaction with operational metrics. This was structured into five distinct phases. Initially, an analysis of the filling line was performed to determine the data sources and user requirements. Subsequently, immersive technologies that would facilitate hands-free interaction, spatial mapping, and integration of digital data with the physical environment were evaluated. The third phase entailed the implementation of the PLC-MR data integration via a custom API. The fourth phase involved iterative refinements, informed by hands-on feedback from prototype trials. Finally, a usability evaluation was conducted to ensure the effectiveness and user-friendliness of the developed solution. A validation with seventeen operators of the industrial filling system confirmed that the system provides a clear and intuitive view of the filling process. When the interaction time of the proposed platform was evaluated, it was found that it was improved compared to the traditional method of visualization through the HMI. This position the interface as a viable reference for future industrial MR applications. The results of this study underscore the potential of MR as a transformative tool for industrial data visualization.

工业过程中的可视化数据是工业4.0的关键组成部分，为更好的决策、实时监控和流程优化提供了机会。本文提出了一种架构设计，可以从控制技术中捕获数据，并将其集成到混合现实（MR）系统中，以便在液体肥皂填充过程中实现数据可视化。该系统将来自可编程逻辑控制器（PLC）的实时过程数据集成到MR技术中，从而创建了一个沉浸式平台，用于增强对操作指标的理解和交互。这分为五个不同的阶段。最初，对灌装线进行了分析，以确定数据源和用户需求。随后，对沉浸式技术进行了评估，这些技术将促进免提交互、空间映射以及数字数据与物理环境的整合。第三阶段需要通过自定义API实现PLC-MR数据集成。第四阶段涉及迭代改进，由原型试验的实际反馈告知。最后，进行了可用性评估，以确保开发的解决方案的有效性和用户友好性。对17位工业灌装系统操作人员的验证证实，该系统提供了一个清晰直观的灌装过程视图。通过对该平台的交互时间进行评估，发现与传统的HMI可视化方法相比，该平台的交互时间得到了改善。这使得该接口成为未来工业MR应用的可行参考。这项研究的结果强调了MR作为工业数据可视化变革性工具的潜力。

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引用次数: 0

Next-generation thermal spray coatings for military use: Innovations, challenges, and applications (bibliometric review 2015–2025) 下一代军用热喷涂涂料：创新、挑战和应用（文献计量回顾2015-2025）

IF 4.1 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-07-29 DOI: 10.1016/j.dche.2025.100259

Agus Nugroho , Sarbani Daud , Prabowo Puranto , Rizalman Mamat , Zhang Bo , Mohd Fairusham Ghazali

This study presents a comprehensive bibliometric and thematic analysis of 743 research articles published between 2015 and 2025 on thermal spray coatings for military applications. Advanced bibliometric tools visualized co-authorship networks, keyword evolution, and citation clusters, mapping research trajectories and material-process innovations. The review highlights significant advancements aimed at improving wear resistance, corrosion protection, and thermal stability under extreme conditions. Research on nanostructured and multifunctional coatings has increased by over 45 % in the past five years, addressing needs for electromagnetic shielding, stealth, and biological functions. Publication trends closely correlate with global defense modernization and geopolitical tensions, emphasizing the strategic importance of these materials. While plasma spraying and high-velocity oxygen fuel (HVOF) dominate, emerging eco-friendly spray techniques and AI-assisted designs constitute fewer than 10 % of studies, indicating future research opportunities. Key gaps include real-time in-situ diagnostics and sustainability-focused coatings. This work provides strategic, actionable insights for defense-oriented surface engineering, facilitating the lab-to-field transition and guiding researchers, engineers, and strategists in advancing next-generation military-grade thermal spray coatings.

本研究对2015年至2025年间发表的743篇关于军事应用热喷涂涂料的研究论文进行了全面的文献计量和专题分析。先进的文献计量工具可视化合著者网络、关键词演变和引文集群、绘制研究轨迹和材料过程创新。该综述强调了在提高极端条件下的耐磨性、防腐蚀和热稳定性方面取得的重大进展。在过去的五年中，纳米结构和多功能涂层的研究增长了45%以上，解决了电磁屏蔽、隐身和生物功能的需求。出版趋势与全球国防现代化和地缘政治紧张局势密切相关，强调了这些材料的战略重要性。虽然等离子喷涂和高速氧燃料（HVOF）占据主导地位，但新兴的环保喷涂技术和人工智能辅助设计只占研究的不到10%，这表明未来的研究机会很大。主要差距包括实时现场诊断和以可持续发展为重点的涂层。这项工作为面向国防的表面工程提供了战略性的、可操作的见解，促进了实验室到战场的过渡，并指导研究人员、工程师和战略家推进下一代军用级热喷涂涂层。

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引用次数: 0