Digital Chemical Engineering最新文献_第2页

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

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

{"title":"Solving bilevel problems under uncertainty with embedded neural networks: Incorporating scenario sets as inputs","authors":"Isabela Fons Moreno-Palancas, Rubén Ruiz Femenia, Raquel Salcedo Díaz, José A. Caballero","doi":"10.1016/j.dche.2025.100253","DOIUrl":"10.1016/j.dche.2025.100253","url":null,"abstract":"<div><div>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.</div><div>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.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100253"},"PeriodicalIF":4.1,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Hiroki Horiuchi, Yoshiyuki Yamashita

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

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

{"title":"Implementation of mixed reality for data visualization in liquid soap filling processes","authors":"Andrés Felipe Hurtado, Carlos Mario Paredes, Kelly Daniella Marín Montealegre, Juan Pablo González Molina, Juan Pablo Álzate Saiz","doi":"10.1016/j.dche.2025.100254","DOIUrl":"10.1016/j.dche.2025.100254","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100254"},"PeriodicalIF":4.1,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Next-generation thermal spray coatings for military use: Innovations, challenges, and applications (bibliometric review 2015–2025)","authors":"Agus Nugroho , Sarbani Daud , Prabowo Puranto , Rizalman Mamat , Zhang Bo , Mohd Fairusham Ghazali","doi":"10.1016/j.dche.2025.100259","DOIUrl":"10.1016/j.dche.2025.100259","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100259"},"PeriodicalIF":4.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neighborhood preservation-based trajectory clustering for analyzing temporal behavior of dynamic systems 基于邻域保存的轨迹聚类分析动态系统的时间行为

IF 4.1

Digital Chemical Engineering Pub Date : 2025-07-28 DOI: 10.1016/j.dche.2025.100251

Bálint Levente Tarcsay, János Abonyi, Sándor Németh

{"title":"Neighborhood preservation-based trajectory clustering for analyzing temporal behavior of dynamic systems","authors":"Bálint Levente Tarcsay, János Abonyi, Sándor Németh","doi":"10.1016/j.dche.2025.100251","DOIUrl":"10.1016/j.dche.2025.100251","url":null,"abstract":"<div><div>This work presents clustering algorithms for identifying movement patterns in trajectories, with a focus on applications in chemical engineering. The exponential growth of dynamic system data necessitates algorithms that account for both local and global trajectory trends. Existing methods often overlook these aspects. We propose two DBSCAN-based variants that cluster trajectories from dynamic systems using agglomeration criteria reflecting the temporal evolution of object neighborhoods in phase space. The first algorithm groups objects with similar movement patterns over a defined observation period, while the second clusters objects with consistent neighborhood similarity over extended periods. These approaches enable the identification of localized neighborhood preservation and trajectory similarity, alongside global trends. We demonstrate the method by clustering particle trajectories generated via computational fluid dynamics, revealing characteristic flow regions within a tank equipped with static mixers. This highlights the methods’ utility for analyzing and optimizing dynamic processes in chemical engineering.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100251"},"PeriodicalIF":4.1,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid neural networks for improved chemical process modeling: Bridging data-driven insights with physical consistency 用于改进化学过程建模的混合神经网络：桥接数据驱动的见解与物理一致性

IF 4.1

Digital Chemical Engineering Pub Date : 2025-07-25 DOI: 10.1016/j.dche.2025.100256

Jana Mousa, Stéphane Negny, Rachid Ouaret

{"title":"Hybrid neural networks for improved chemical process modeling: Bridging data-driven insights with physical consistency","authors":"Jana Mousa, Stéphane Negny, Rachid Ouaret","doi":"10.1016/j.dche.2025.100256","DOIUrl":"10.1016/j.dche.2025.100256","url":null,"abstract":"<div><div>The increasing reliance on neural networks (NN) in chemical process modeling highlights their capability for accurate predictions, yet their standalone application often struggles to adhere to fundamental physical laws such as equilibrium constraints and mass balance. Addressing this limitation, hybrid methods that integrate data-driven insights with physical consistency have gained prominence. This study systematically explores the integration of NNs with nonlinear data reconciliation (NDR) across multiple testing dimensions, including a Gibbs reactor, data robustness evaluations, and reactor-distillation system integration. Hybrid methodologies such as NN + NDR, NN + KKT (Karush-Kuhn-Tucker), and KKT + PINN (Physics-Informed Neural Networks with KKT conditions) are comparatively assessed. The proposed NN + NDR framework demonstrates superior performance in minimizing errors and enforcing physical laws, with minimal computational overhead. This work emphasizes the scalability, robustness, and transformative potential of modular hybrid strategies in advancing reliable, physically consistent chemical process modeling.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100256"},"PeriodicalIF":4.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An overview of chemical process operation-optimization under complex operating conditions 复杂操作条件下化工过程操作优化综述

IF 3

Digital Chemical Engineering Pub Date : 2025-07-23 DOI: 10.1016/j.dche.2025.100249

Yuanyuan Zou, Xu Ma, Yaru Yang, Shaoyuan Li

引用次数: 0

Decarbonizing the chemical industry through digital technologies 通过数字技术使化学工业脱碳

IF 3

Digital Chemical Engineering Pub Date : 2025-06-18 DOI: 10.1016/j.dche.2025.100250

Kathleen B. Aviso

引用次数: 0

A circular exploration of cryoprotective agents for stem cells using computer-aided molecular design approaches 利用计算机辅助分子设计方法对干细胞冷冻保护剂进行循环探索

IF 3

Digital Chemical Engineering Pub Date : 2025-06-11 DOI: 10.1016/j.dche.2025.100248

Rei Tamaki , Yusuke Hayashi , Yuki Uno , Masahiro Kino-oka , Hirokazu Sugiyama

引用次数: 0

Machine learning for asphaltene polarizability: Evaluating molecular descriptors 沥青质极化的机器学习：评估分子描述符

IF 3

Digital Chemical Engineering Pub Date : 2025-06-01 DOI: 10.1016/j.dche.2025.100244

Arun K. Sharma, Owen McMillan, Selsela Arsala, Supreet Gandhok, Rylend Young

{"title":"Machine learning for asphaltene polarizability: Evaluating molecular descriptors","authors":"Arun K. Sharma, Owen McMillan, Selsela Arsala, Supreet Gandhok, Rylend Young","doi":"10.1016/j.dche.2025.100244","DOIUrl":"10.1016/j.dche.2025.100244","url":null,"abstract":"<div><div>Asphaltenes are complex polycyclic organic molecules in crude oil that readily aggregate and precipitate under varying thermodynamic conditions. Their structural heterogeneity influences key physicochemical properties, including solubility, stability, and reactivity. Molecular polarizability, a crucial property governing intermolecular interactions and electronic behavior, remains challenging to predict due to this structural diversity. This study employs machine learning models to predict isotropic polarizability using two sets of molecular descriptors: WHIM and GETAWAY. A dataset of 255 asphaltene structures was analyzed using stratified sampling, generating 10 independent training (80 %) and testing (20 %) splits. The Wolfram Language’s Predict function evaluated multiple machine learning algorithms—including Random Forest, Decision Tree, Gradient Boosted Trees, Nearest Neighbors, Linear Regression, Gaussian Process, and Neural Network—through an automated model selection process, serving as an AutoML framework. Linear regression was the best-performing model in 9 out of 10 splits for GETAWAY descriptors. GETAWAY-based models achieved an average mean absolute deviation of 0.0920 ± 0.0030 and standard deviation of 0.113 ± 0.004, significantly outperforming WHIM-based models (MAD = 0.173 ± 0.007, STD = 0.224 ± 0.008) with paired <em>t</em>-tests confirming statistical significance (<em>p</em> < 0.001). While R² values were reported, their interpretability was limited by heterogeneity and narrow property ranges in some test sets. These findings demonstrate the effectiveness of AutoML-guided approaches for predicting molecular properties and identify GETAWAY descriptors as a robust, efficient basis for polarizability prediction. Accurate prediction of polarizability is essential for modeling intermolecular forces and improving force field design in petroleum and materials chemistry, issues that are central to industrial and chemical applications.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100244"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0