Digital Chemical Engineering最新文献

{"title":"Data driven prediction of hydrochar yields from biomass hydrothermal carbonization using extreme gradient boosting algorithm with principal component analysis","authors":"Tossapon Katongtung ,&nbsp;Nattawut Khuenkaeo ,&nbsp;Yuttana Mona ,&nbsp;Pana Suttakul ,&nbsp;James C. Moran ,&nbsp;Korrakot Y. Tippayawong ,&nbsp;Nakorn Tippayawong","doi":"10.1016/j.dche.2025.100283","DOIUrl":"10.1016/j.dche.2025.100283","url":null,"abstract":"<div><div>Dimensionality reduction plays a critical role in efficiently managing large and complex datasets in machine learning (ML) applications. This study presents an innovative integration of principal component analysis (PCA) and extreme gradient boosting (XGB) to model the hydrothermal carbonization (HTC) process. PCA effectively reduced the feature space from 18 to 9 principal components with minimal impact on model accuracy (R² decreased slightly from 0.8900 to 0.8480), significantly simplifying the model complexity. To enhance interpretability, one- and two-dimensional partial dependence plots (PDP) were employed, revealing key features and their interactions influencing HTC outcomes. This combined approach not only improves predictive performance but also provides meaningful insights into the underlying process variables, addressing common challenges of ML opacity. While the model demonstrates strong predictive capability, further experimental validation and extension to diverse biomass types are recommended to confirm practical applicability and enhance versatility. The proposed methodology offers a robust, interpretable, and computationally efficient framework for optimizing HTC and can guide future research involving high-dimensional datasets.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100283"},"PeriodicalIF":4.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791149","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":"Transfer learning of data-driven crystallisation processes via constrained Neural Ordinary Differential Equations","authors":"Daniele Pessina ,&nbsp;Tony Tian ,&nbsp;Oliver Watson ,&nbsp;Jerry Y. Heng ,&nbsp;Maria M. Papathanasiou","doi":"10.1016/j.dche.2026.100292","DOIUrl":"10.1016/j.dche.2026.100292","url":null,"abstract":"<div><div>Modelling complex crystallisation processes remains challenging due to limited experimental datasets, high measurement noise, and the need for generalisability across varying operating conditions. Neural Ordinary Differential Equations (NODEs) and transfer learning (TL) offer promising tools to overcome these limitations by providing data-efficient, flexible, and transferable modelling frameworks. This work investigates the use of NODEs to model protein crystallisation dynamics under data-scarce conditions. A NODE trained on a data-rich source system successfully captures solute consumption and particle size dynamics, but when applied to data-sparse target systems, scratch-trained NODEs exhibit limited generalisation and unphysical behaviours. To address this, several TL strategies are evaluated, including layer freezing, parameter deviation penalisation, and system-embedding within the neural architecture. Results show that layer freezing and deviation penalty consistently improve knowledge transfer, while system-embedding offers robustness in noisy or undersampled datasets. In addition, physics-informed NODEs, constrained to enforce monotonic concentration decay and crystal growth, demonstrate greater stability under high noise and sparse measurement regimes, ensuring physically consistent predictions. Overall, the combination of constrained NODEs with appropriate TL strategies provides a robust framework for accurate, transferable modelling of crystallisation systems in low-data regimes.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100292"},"PeriodicalIF":4.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385204","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":"All you need is noise — from feature selection to explainable industrial AI","authors":"Mattia Vallerio ,&nbsp;Antonio del Rio Chanona ,&nbsp;Francisco J. Navarro-Brull","doi":"10.1016/j.dche.2026.100290","DOIUrl":"10.1016/j.dche.2026.100290","url":null,"abstract":"<div><div>Modern chemical plants record thousands of sensor tags, yet only a small fraction meaningfully influence yield, quality, or throughput. Identifying those key drivers is often more difficult than building the predictive model itself. In this work, we show that appending one or more <em>Synthetic Noise Features</em> (SNFs), non-informative random variables known <em>a priori</em>, provide a simple reference for judging variable relevance. We show the impact of this model agnostic step across three workflows. In supervised learning, noise features establish an automatic cutoff for the feature importance, guide model regularization and signal when the dataset itself lacks predictive information. In unsupervised learning, they provide an unbiased threshold preventing spurious anomalies and latent dimensions. Finally, we demonstrate the applicability of this approach to small datasets typical of experimental work and Design of Experiments (DoE), including <em>Definitive Screening</em>, <em>Response Surface</em>, and <em>space-filling</em> designs, as well as <em>active learning</em> using <em>Bayesian optimization</em>. By turning <em>nothing but noise</em> into a quantitative benchmark, SNFs offer an immediately deployable safeguard against overfitting and misplaced experimental effort in data-driven chemical engineering.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100290"},"PeriodicalIF":4.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385201","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":"Robust reinforcement learning for nonlinear process control with stability guarantees","authors":"Xiaodong Cui ,&nbsp;Arthur Khodaverdian ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2026.100291","DOIUrl":"10.1016/j.dche.2026.100291","url":null,"abstract":"<div><div>Reinforcement learning (RL) offers a promising route to fast, nonlinear feedback control for complex process systems; however, its deployment is hindered by the lack of formal stability guarantees and sensitivity to model-plant mismatch under constraints. This paper proposes a stability and robustness-oriented RL-based control framework for nonlinear constrained processes by explicitly integrating Lyapunov-based decision rules into the RL closed loop and importing an offset-free approach from model predictive control (MPC). The RL policy is treated as a performance-seeking candidate controller that is supervised by a Lyapunov-certified fallback controller: at each sampling instant, the learned candidate input is evaluated against a Lyapunov condition, and any violating proposal is rejected in favor of a stabilizing backup input, yielding Lyapunov-certified practical stability under sample-and-hold implementation. To mitigate steady-state offsets and enhance robustness to disturbances and mismatches, the state available to the learning agent is augmented with online-estimated uncertainty/disturbance variables in the spirit of offset-free MPC, enabling the policy/value function to condition its decisions on the magnitude of uncertainty rather than overfitting nominal dynamics. The proposed architecture is demonstrated using two representative RL methods—an HJB-based value-critic approach and a TD3-based actor-critic approach—both deployed under the same Lyapunov-supervisory switching logic. Simulation studies on nonlinear chemical process control problems show that the proposed RL-based control framework preserves the low online computational cost while enforcing Lyapunov stability and improving robustness under disturbances, thereby advancing RL toward reliable process control deployment.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100291"},"PeriodicalIF":4.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385203","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":"Smart control of heavy metal adsorption onto LDC wastes for Industry 4.0 applications","authors":"Mohammad Gheibi ,&nbsp;Seyyed Roohollah Masoomi ,&nbsp;Mohammad Eftekhari ,&nbsp;Martin Palušák ,&nbsp;Daniele Silvestri ,&nbsp;Miroslav Černík ,&nbsp;Stanisław Wacławek","doi":"10.1016/j.dche.2025.100286","DOIUrl":"10.1016/j.dche.2025.100286","url":null,"abstract":"<div><div>Due to the complexity and the need for automation in adsorption systems, this study develops a decision-making model using 20 Machine Learning Algorithms (MLAs), Response Surface Methodology (RSM), and lab tests. Inputs include pH, metal type, concentration, adsorbent mass, and time; output is removal percentage for process control. The best-performing models (Tree Random Forest: TRF, lazy Instance-Based K: IBK, and Function Multilayer Perceptron: FMLP) achieve high accuracy for prediction of Removal Percentage (RP) of heavy metals with &gt;0.92 correlation coefficient and &gt;0.8 recall/precision indicators. As a novelty of this study, a decision-making model for the heavy metal adsorption process onto waste materials is developed for the first time using the Knowledge Flow (KF) platform in WEKA software, incorporating real-time data to improve process control and operational efficiency. The results demonstrated that the cation type and pH, with a P-value &lt; 0.001, are the most significant factors affecting the RP. To achieve maximum RP, the pH should be set to 5, and the adsorbent amount should be in a range of 8.67-10 g L^-¹, regardless of the initial concentration and type of ions (Pb²⁺, Mn²⁺, and Co²⁺). Then, evaluating MLAs showed that TRF, with a correlation coefficient exceeding 0.96, performs best for predicting RP, potentially reaching 0.99 at a split percentage around 80%. TRF uses real-time experimental data in water treatment systems to anticipate RP up to 98.75% and to activate alarms for RP below 80% using the KF principle.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100286"},"PeriodicalIF":4.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977798","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":"Advanced machine learning techniques for predicting solution gas-oil ratios in petroleum reservoirs: A comprehensive study and new empirical correlation","authors":"Mohammad Sepahvand ,&nbsp;Majid Mohammadi ,&nbsp;Ali Madani ,&nbsp;Mahin Schaffie","doi":"10.1016/j.dche.2026.100288","DOIUrl":"10.1016/j.dche.2026.100288","url":null,"abstract":"<div><div>The precise assessment of crude oil properties is crucial for various tasks in reservoir evaluation, including estimating reserves, enhancing oil recovery, predicting oil reservoir performance, designing pipelines and production equipment, and simulating reservoirs. Therefore, the reservoir fluid properties, especially the solution gas-oil ratio (<em>GOR</em> or <em>R_s</em>), which depends on bubble point pressure (<em>P_b</em>), reservoir temperature (<em>T_r</em>), gas specific gravity (<span><math><msub><mi>γ</mi><mi>g</mi></msub></math></span>) and API are of great importance. On the other side, due to the time-consuming and expensive nature of pressure-volume-temperature (PVT) laboratory models, accurate experimental correlations are significant in petroleum engineering. Therefore, a cheap, accurate, fast model is needed to estimate these parameters. In this study, to develop a comprehensive and reliable model, 1309 data points were collected from crude oil samples in different geographical locations. The development of a new correlation was presented with high accuracy. Also, intelligent models based on neural networks such as Multilayer perceptron neural networks (MLP), Radial basis function network (RBF), Extreme gradient boosting (XGBoost), Adaptive boosting (AdaBoost), and Gradient boosting with categorical features support (CatBoost), were used to predict <em>R_s</em>. To assess the performance of the models, statistical and graphical error analyses, sensitivity analyses, group errors, model trends, and a leverage approach were employed. The CatBoost model delivered the highest predictive accuracy, with an <em>AAPRE</em> of 8.13%, surpassing competitors like XGBoost (10.51%) and AdaBoost (24.78%). In addition, the new correlation developed achieved an <em>AAPRE</em> of 16.40%, offering a marked advancement over conventional empirical correlations that typically range from 17.24% to 64.88% in error. These findings demonstrate the superior precision and versatility of methods, underscoring its innovative value for estimating <em>R_s</em> across varied reservoir environments. Factorial sensitivity analysis showed that <em>P_b</em> had the greatest impact on <em>R_s</em> with 25.4%.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100288"},"PeriodicalIF":4.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077746","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":"Early-stage chemical process screening through hybrid modeling: Introduction and case study of a reaction–crystallization process","authors":"Diana Wiederschitz ,&nbsp;Edith-Alice Kovacs ,&nbsp;Botond Szilagyi","doi":"10.1016/j.dche.2025.100280","DOIUrl":"10.1016/j.dche.2025.100280","url":null,"abstract":"<div><div>Late-stage development of complex chemical processes presents significant challenges due to the high dimensionality and interactions of operating parameters. This complexity renders traditional factorial experimental designs impractical. Consequently, there is often a default reliance on suboptimal legacy technologies, which can lead to reduced overall performance and a larger environmental footprint. This work introduces a novel integrated methodology for combined process and product attribute screening specifically designed to overcome these limitations. The approach strategically integrates expert knowledge, high-fidelity first-principle modeling, and data mining techniques to accelerate the generation of critical process understanding. This supports the confident adoption of sustainable high-performance manufacturing routes. The sequential framework begins with expert knowledge to define promising technological pathways, which are then modeled using first-principle approaches, potentially enhanced by contemporary Artificial Intelligence (AI) techniques. Afterward, extensive parametric optimizations are performed, generating rich synthetic datasets. These data are then subjected to data mining algorithms for pattern recognition, identification of different clusters of the operational regime, and estimation of key product properties. The effectiveness of this methodology is demonstrated through a challenging case study that focuses on the crystallization of conglomerates, which combines deracemization and particle formation, steps traditionally performed sequentially with associated inefficiencies. Our analysis reveals that optimal operations form 12 distinct clusters within which the expected product properties can vary considerably. A key finding is that incorporating data from a strategically designed preliminary experiment enables the exclusion of difficult-to-measure material-specific parameters and enhances the cluster classification and product property estimation.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100280"},"PeriodicalIF":4.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738461","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":"Implementation of mixed reality for data visualization in liquid soap filling processes","authors":"Andrés Felipe Hurtado,&nbsp;Carlos Mario Paredes,&nbsp;Kelly Daniella Marín Montealegre,&nbsp;Juan Pablo González Molina,&nbsp;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":"2025-12-01","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}

{"title":"Soft-measuring method of iron ore sintering process using transient model","authors":"Yoshinari Hashimoto ,&nbsp;Satoki Yasuhara ,&nbsp;Yuji Iwami","doi":"10.1016/j.dche.2025.100268","DOIUrl":"10.1016/j.dche.2025.100268","url":null,"abstract":"<div><div>To achieve efficient sintering machine operation in the steel industry, we developed an online soft-measuring method that can visualize the temperature distribution in the sintering process using a two-dimensional (2D) transient model. Although various numerical simulation models of the sintering process have been proposed, the conventional models suffer from estimation errors caused by unmeasurable disturbances, such as the fluctuations in raw material characteristics, when these models are applied for online control in actual plants over a long period. In this study, to reduce the estimation errors, the model parameters were adjusted successively by moving horizon estimation (MHE), considering the effects of the disturbances. The validation results with actual plant data showed that the estimation errors of the burn rising point (BRP) and the exhaust gas compositions were reduced significantly by MHE. In particular, the root mean square error (RMSE) of the BRP estimation was only 1.48 m. In addition, a correlation was confirmed between the estimated high-temperature holding time of the material and the product yield. The developed soft-measuring method is beneficial for process automation to improve product yield.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100268"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267047","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":"Next-generation thermal spray coatings for military use: Innovations, challenges, and applications (bibliometric review 2015–2025)","authors":"Agus Nugroho ,&nbsp;Sarbani Daud ,&nbsp;Prabowo Puranto ,&nbsp;Rizalman Mamat ,&nbsp;Zhang Bo ,&nbsp;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":"2025-12-01","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}