Digital Chemical Engineering最新文献

{"title":"Optimization-based framework for kernel parameter identification in multi-material population balance models","authors":"Haoran Ji,&nbsp;Lena Fuhrmann,&nbsp;Juan Fernando Meza Gonzalez,&nbsp;Frank Rhein","doi":"10.1016/j.dche.2025.100272","DOIUrl":"10.1016/j.dche.2025.100272","url":null,"abstract":"<div><div>This study presents a robust, parallelized optimization framework for kernel parameter identification that is adaptable to any population balance equation (PBE) formulation and process type. The framework addresses the challenge of incomplete 2D particle size distribution (PSD) measurements in multi-material systems by combining a reduced 2D PSD with complementary 1D datasets. The framework was validated by using noisy synthetic PSD data and evaluating both the error in PSD and kernel values across eight kernel parameters. Hyperparameter and sensitivity analyses provided configuration recommendations and insights into the influence of individual parameters, thus guiding kernel model selection. Incorporating prior knowledge of one kernel parameter (e.g., through multi-scale simulations) mitigated non-unique solutions and enhanced noise tolerance, ultimately improving the framework’s robustness and reliability. A case study based on experimental data from a dispersion process demonstrated the framework’s flexibility and practical relevance.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100272"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416788","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}

{"title":"Root cause identification of fault in hot-rolling process by causal plot","authors":"Koichi Fujiwara ,&nbsp;Yoshiaki Uchida ,&nbsp;Taketsugu Osaka","doi":"10.1016/j.dche.2025.100263","DOIUrl":"10.1016/j.dche.2025.100263","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100263"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107110","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}

{"title":"Data fusion of spectroscopic data for enhancing machine learning model performance","authors":"Pál Péter Hanzelik ,&nbsp;Szilveszter Gergely ,&nbsp;János Abonyi ,&nbsp;Alex Kummer","doi":"10.1016/j.dche.2025.100271","DOIUrl":"10.1016/j.dche.2025.100271","url":null,"abstract":"<div><div>Developing accurate industrial prediction models for complex industrial and geological applications remains a significant challenge, particularly when relying on limited and disparate spectroscopic data. Traditional data fusion methods often fall short in effectively integrating complementary information across different spectral sources, limiting predictive performance. Complex-level ensemble fusion (CLF) is presented as a two-layer chemometric algorithm that jointly selects variables from concatenated mid-infrared (MIR) and Raman spectra with a genetic algorithm, projects them with partial least squares and stacks the latent variables into an XGBoost regressor, thereby capturing feature- and model-level complementarities in a single workflow. When benchmarked against single-source models and classical low-, mid-, and high-level data-fusion schemes, the CLF technique consistently demonstrated significantly improved predictive accuracy. Evaluated on paired Mid-Infrared (MIR) and Raman datasets from industrial lubricant additives and RRUFF minerals, CLF robustly outperformed established methodologies by effectively leveraging complementary spectral information. Mid-level fusion yielded no improvement, underscoring the need for supervised integration. These results constitute the first evidence that a stacked, complex-level scheme can surpass all established fusion levels on real-world spectroscopic regressions comprising fewer than one hundred samples and provide a transferable recipe for building more accurate and resilient soft sensors in quality-control and geochemical applications.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100271"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416790","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}

{"title":"MIDDoE: An MBDoE Python package for model identification, discrimination, and calibration","authors":"Z. Tabrizi ,&nbsp;E. Barbera ,&nbsp;W.R. Leal da Silva ,&nbsp;F. Bezzo","doi":"10.1016/j.dche.2025.100276","DOIUrl":"10.1016/j.dche.2025.100276","url":null,"abstract":"<div><div>Mathematical modelling plays a critical role in the design, optimisation, and control of dynamic systems in the process industry. While mechanistic models offer strong explanatory and predictive power, their effectiveness depends on informed model selection and precise parameter calibration. Model-based design of experiments (MBDoE) provides a framework for addressing these challenges by designing experiments that accelerate model discrimination and parameter precision tasks. However, its practical application is frequently constrained by fragmented digital tools that lack integration and make MBDoE implementation a task for expert users. To address that – thus supporting the widespread use of MBDoE – <em>MIDDoE</em>, a modular and user-friendly <em>Python</em>-based framework centred on MBDoE is introduced. <em>MIDDoE</em> supports both model discrimination and parameter precision design strategies, incorporating physical constraints and non-convex design spaces. To provide a comprehensive MBDoE digital tool, the framework integrates numerical techniques such as Global Sensitivity Analysis, Estimability Analysis, parameter estimation, uncertainty analysis, and model validation. Its architecture decouples simulation from analysis, enabling compatibility with both built-in and external simulators, which allows <em>MIDDoE</em> to be applied across different systems. <em>MIDDoE</em> practical application is demonstrated through two case studies in bioprocess and pharmaceutical systems for model discrimination and parameter precision tasks.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100276"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623996","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}

{"title":"HYBpy: A web-based framework for hybrid modeling of biological systems","authors":"José Pedreira ,&nbsp;José Pinto ,&nbsp;Daniel Gonçalves ,&nbsp;Pedro Barahona ,&nbsp;Rui Oliveira ,&nbsp;Rafael S. Costa","doi":"10.1016/j.dche.2025.100278","DOIUrl":"10.1016/j.dche.2025.100278","url":null,"abstract":"<div><div>Hybrid modeling is gaining prominence in various industrial sectors because it offers a flexible balance between mechanistic and data-driven modeling. However, the adoption of such hybrid modeling techniques has been rather limited. Only few expert researchers using in-house tools have technical background and skills to develop such hybrid models worldwide. Additionally, freely available and user-friendly software tools for developing hybrid models in bioprocesses and biological systems are lacking.</div><div>To address these gaps, we developed HYBpy. HYBpy is a user-friendly web-based framework based on a generalized step-by-step pipeline for quick and easy generation/training of hybrid models compliant with current file formats. We demonstrated the HYBpy functionalities using two literature case studies in the biological engineering domain. HYBpy is expected to greatly facilitate the usage of hybrid modeling, making these approaches accessible for the nonexpert community.</div><div>Availability: HYBpy and two case examples can be accessed online at <span><span>www.hybpy.com</span><svg><path></path></svg></span>. Although HYBpy is offered as a web-based tool, it can also be installed locally as described in the GitHub repository instructions. The source code is hosted and publicly available on GitHub at <span><span>https://github.com/joko1712/HYBpy</span><svg><path></path></svg></span> under the GNU General Public License v3.0.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100278"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693678","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}

{"title":"Building a scalable digital infrastructure for a (bio)chemical engineering pilot plant: A case study from DTU","authors":"Jakob Kjøbsted Huusom ,&nbsp;Mark N. Jones ,&nbsp;Julian Kager ,&nbsp;Kim Dam-Johansen ,&nbsp;Jochen A.H. Dreyer","doi":"10.1016/j.dche.2025.100269","DOIUrl":"10.1016/j.dche.2025.100269","url":null,"abstract":"<div><div>The digitalization of pilot-scale chemical engineering facilities offers significant potential for enabling e.g. data-driven research, process modeling, closed loop process control, and digital twin development, but the implementation of robust and maintainable infrastructure remains a practical challenge. This case study presents the digitalization of the Pilot Plant at DTU Chemical Engineering, with a focus on building a scalable and reproducible architecture for real-time data access, structured data storage, and unified system control.</div><div>A key feature of the infrastructure is the use of standardized OPC UA gateways to establish encrypted connections to a diverse set of legacy and modern unit operations. While the supervisory control and data acquisition (SCADA) system communicates directly with the OPC UA gateways, the data streams are also structured using an intermediate data broker. Here, each tag is organized by unit operation and type (e.g., sensors, controls, configurations) aligned with the underlying database schema. The broker then publishes all real-time data via MQTT. Containerized Python applications deployed on a dedicated server subscribe to the MQTT data streams and whenever experiments are active, write the real-time data to an SQL database. The system is fully extensible: new units or sensors can be added without modifying the database schema or Python code.</div><div>Unified operation and metadata collection are enabled through a web-based SCADA system, while version-controlled CI/CD pipelines ensure reproducible deployment of all services on the server. This workflow avoids manual modifications to the server and simplifies long-term maintenance. The use of open communication protocols minimizes dependency on proprietary services and ensures that individual components can be replaced or extended without vendor lock-in.</div><div>The resulting infrastructure provides both real-time and historical access to high-quality experimental data, supporting applications ranging from digital twin development and process optimization to machine learning. It serves as an educational resource used annually by approximately 150–200 students across five courses, in addition to student and Ph.D. projects. The SCADA system is routinely applied during pilot-scale unit operation exercises, while advanced courses make use of live data access and interaction with the SQL database. Beyond education, the infrastructure has been adopted across multiple research centers: for example, it underpins recent work on hybrid modeling and digital twins for pilot-scale bubble column and distillation units, and its modular components (CI/CD pipelines, database, MQTT broker, data broker) are being reused in other digitalization initiatives. These developments highlight both the scalability of the approach and its value as a transferable reference for academic and industrial pilot plants.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100269"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321012","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}

{"title":"Editorial: Special issue on pioneering digital chemical engineering","authors":"Jin Xuan ,&nbsp;Jinfeng Liu","doi":"10.1016/j.dche.2025.100255","DOIUrl":"10.1016/j.dche.2025.100255","url":null,"abstract":"","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100255"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059855","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}

{"title":"Neighborhood preservation-based trajectory clustering for analyzing temporal behavior of dynamic systems","authors":"Bálint Levente Tarcsay,&nbsp;János Abonyi,&nbsp;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":"2025-09-01","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}

{"title":"Multi-criteria decision support for flexible dividing wall distillation columns","authors":"David Mogalle ,&nbsp;Patrick Otto Ludl ,&nbsp;Tobias Seidel ,&nbsp;Lea Trescher ,&nbsp;Thomas Grützner ,&nbsp;Michael Bortz","doi":"10.1016/j.dche.2025.100258","DOIUrl":"10.1016/j.dche.2025.100258","url":null,"abstract":"<div><div>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.</div><div>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.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100258"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907779","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}

{"title":"An overview of chemical process operation-optimization under complex operating conditions","authors":"Yuanyuan Zou,&nbsp;Xu Ma,&nbsp;Yaru Yang,&nbsp;Shaoyuan Li","doi":"10.1016/j.dche.2025.100249","DOIUrl":"10.1016/j.dche.2025.100249","url":null,"abstract":"<div><div>With the increasing complexity of production requirements and the constant change of operating conditions, the optimization of process control systems (PCSs) has become an important issue in chemical industry production. Motivated by this urgent need, an overview of advanced real-time optimization, model predictive control, and data-driven operation-optimization approaches is presented. In particular, our discussions highlight approaches that focus on typical problems such as dynamic and steady-state economic performance improvement, robust constraint satisfaction, stable and offset-free operation, and multi-mode operation, which should be addressed foremostly under complex operating conditions. The aim of this paper is to provide a better understanding of the methods and their parameter-tuning routines, which can be a reference for the readers to align the suitable techniques with the PCSs, according to the practical operation-optimization requirements in chemical processes.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100249"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702516","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}