Digital Chemical Engineering最新文献

{"title":"Decarbonizing the chemical industry through digital technologies","authors":"Kathleen B. Aviso","doi":"10.1016/j.dche.2025.100250","DOIUrl":"10.1016/j.dche.2025.100250","url":null,"abstract":"<div><div>There are several challenges to decarbonizing the chemical industry as it utilizes significant amounts of fossil fuels as feedstock and as source of energy. As a result, the industry contributes about 5 % to global CO₂ emissions. Various strategies and technologies which include the use of alternative feedstock, electrification, and negative emissions technologies are available to aid in the industry’s decarbonization. These strategies can be implemented at different stages of the chemical production life cycle. The adoption of digital technologies has reported improvements in the economic, environmental, and societal performance of manufacturing industries. This review intends to investigate how available digital technologies can be utilized to accelerate the decarbonization of the chemical industry.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100250"},"PeriodicalIF":3.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329715","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":"A circular exploration of cryoprotective agents for stem cells using computer-aided molecular design approaches","authors":"Rei Tamaki ,&nbsp;Yusuke Hayashi ,&nbsp;Yuki Uno ,&nbsp;Masahiro Kino-oka ,&nbsp;Hirokazu Sugiyama","doi":"10.1016/j.dche.2025.100248","DOIUrl":"10.1016/j.dche.2025.100248","url":null,"abstract":"<div><div>This work presents a circular exploration of cryoprotective agents (CPAs) for stem cells using computer-aided molecular design approaches that can comprehensively consider compounds. An exploration cycle was developed that consists of the following five steps: setting conditions, computational evaluation, experimental evaluation, verification experiments, and discussions with experts in biotechnology. It aims to discover promising CPA candidate compounds by incorporating domain knowledge through discussions with the experts. The developed cycle can be applied to fields where the required physical properties have not been clearly known. As a result, 1-methylimidazole and pyridazine were selected as promising CPA candidate compounds, which were both heterocyclic amines. Hence, heterocyclic amines could be a stepping-stone toward the future development of CPAs for stem cells. By repeatedly using the exploration cycle, CPA candidate compounds with better cryoprotective effects could be discovered.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100248"},"PeriodicalIF":3.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280750","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":"Machine learning for asphaltene polarizability: Evaluating molecular descriptors","authors":"Arun K. Sharma,&nbsp;Owen McMillan,&nbsp;Selsela Arsala,&nbsp;Supreet Gandhok,&nbsp;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> &lt; 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}

{"title":"Utilization of aspen DMC3 in process control of crude distillation unit (CDU)","authors":"Bol Ram,&nbsp;Z Ahmad,&nbsp;N Md Nor","doi":"10.1016/j.dche.2025.100245","DOIUrl":"10.1016/j.dche.2025.100245","url":null,"abstract":"<div><div>Crude oil remains a vital non-renewable resource that supports numerous industries in the current era of industrial advancement. Consequently, petroleum refineries face increasing challenges, including stringent environmental regulations, fluctuating feedstock quality, rising demand, safety requirements, and the need for cost optimization. These challenges, coupled with the inherent complexity of the Crude Distillation Unit (CDU), demand advanced control strategies to ensure stable and efficient operation. This study investigates the application of Dynamic Matrix Control (DMC), a subset of Model Predictive Control (MPC), using Aspen DMC3 for CDU process control—a novel implementation not previously explored. The methodology involves three main stages: validation of a CDU simulation based on real data from the Basrah refinery, generation of dynamic response data through MATLAB integrated with Aspen Dynamics, and the development of a DMC controller using Aspen DMC3. The performance of the DMC controller is compared against conventional Proportional-Integral-Derivative (PID) controllers implemented in Aspen Dynamics using key indicators such as settling time, offset error, maximum deviation, and response smoothness. Results demonstrate that the DMC controller provides superior control performance, with faster settling times, zero offset, minimal deviations, and smoother responses. Additionally, Aspen DMC3′s AI-assisted capabilities enable streamlined controller configuration and real-time optimization through server connectivity, highlighting its potential for robust and efficient CDU operation.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100245"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144223561","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":"Capturing variability in material property predictions for plastics recycling via machine learning","authors":"Marcin Pietrasik ,&nbsp;Anna Wilbik ,&nbsp;Yannick Damoiseaux ,&nbsp;Tessa Derks ,&nbsp;Emery Karambiri ,&nbsp;Shirley de Koster ,&nbsp;Daniel van der Velde ,&nbsp;Kim Ragaert ,&nbsp;Sin Yong Teng","doi":"10.1016/j.dche.2025.100239","DOIUrl":"10.1016/j.dche.2025.100239","url":null,"abstract":"<div><div>Plastic mechanical recycling is the conventional technological step towards circularity. In such aspects, complex mixtures of polyolefin blends are often fed into mechanical recycling systems, resulting in moulded products with uncertain quality. To add to the difficulty of heterogeneous feedstocks, the testing of mechanical properties for plastic products often results in stochastic measurements, making connections from material prediction to systems understanding challenging. This research is aimed at providing a framework capable of generalizing stochastic plastic recycling knowledge via interval-based machine learning for the prediction of properties formulation for unrecycled plastics. The framework is made up of two components: a regressor for point estimation and an interval predictor for generating prediction intervals. We compare several competing methods for each of these components through empirical evaluation on a real-world dataset. The results demonstrate the usefulness of interval-based machine learning in the application of stochastic engineering problems such as plastic mechanical recycling, highlighting such approaches towards better model interpretation and (un)certainty prediction regions.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100239"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203735","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":"A degradation-related slow feature analysis for equipment health indicator extraction and remaining useful life prediction","authors":"Qilin Qu ,&nbsp;Linhui Wang ,&nbsp;I.-Yen Wu ,&nbsp;David Shan-Hill Wong ,&nbsp;Ying Zheng ,&nbsp;Yuan Yao","doi":"10.1016/j.dche.2025.100243","DOIUrl":"10.1016/j.dche.2025.100243","url":null,"abstract":"<div><div>Predicting the Remaining Useful Life (RUL) of equipments has recently become a crucial technology for assessing operational safety and assisting maintenance decision-making. Numerous studies have demonstrated that a low-dimensional Health Indicator (HI) can be constructed from multidimensional sensor readings related to degradation, and the prediction of RUL can be based on similarities of HI. However, existing approaches for HI construction ignore neither the slow and monotonic nature of a degradation feature nor correlations between HI and RUL. To address this issue, this paper proposes a degradation-related slow feature analysis (DRSFA) method for extracting HIs and applying them in RUL prediction. Specifically, an objective function and its corresponding closed-form solution are proposed, aiming at extracting a health indicator from multidimensional degradation parameters to represent the slow degradation trend of an equipment and is correlated with its RUL. In DRSFA, HIs of each segment of lifecycle data is extracted separately rather than by a unified model, thereby enhancing its scalability as new data become available. As an HI extractor, DRSFA can serve as a plug-and-play module for RUL prediction based on similarity matching. Finally, experiments conducted on the CMAPSS dataset for aero-engine RUL assessment from NASA validate that the proposed method effectively balances RUL prediction accuracy, interpretability, and scalability.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100243"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211867","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":"A computational investigation of high-flux, plate-and-frame membrane modules for industrial carbon capture","authors":"Cheick Dosso ,&nbsp;Hector A. Pedrozo ,&nbsp;Thien Tran ,&nbsp;Lingxiang Zhu ,&nbsp;Victor Kusuma ,&nbsp;David Hopkinson ,&nbsp;Lorenz T. Biegler ,&nbsp;Grigorios Panagakos","doi":"10.1016/j.dche.2025.100246","DOIUrl":"10.1016/j.dche.2025.100246","url":null,"abstract":"<div><div>In this work, we study the application of membrane-based separation systems for carbon capture, considering plate-and-frame membrane modules. The successful deployment of membrane CO₂ capture systems relies on high-performing membranes, as well as on effective membrane modules that can fully exploit the developed membranes. A plate-and-frame membrane module is especially attractive for CO₂ capture from industrial flue gas, due to its reduced pressure drop compared to its counterparts such as spiral wound modules and hollow fiber modules. To design better plate-and-frame modules, we investigate their basic unit - a single membrane stack - through a combination of computational modeling and experimental investigations. The modeling approach is based on computational fluid dynamics (CFD) to represent the multiphysics problem, including the fluid flow and diffusion processes within the membrane stack. We use experimental data collected under different operating conditions to validate the CFD model. Numerical results suggest good agreement between experiments and model outputs for CO₂ recovery, CO₂ mole fractions in the retentate and permeate, and stage-cut. The CFD model is able to predict accurately the flow behavior, providing valuable insights into the effects of fluid dynamics on the mass transfer of CO₂. CFD models achieve high accuracy by capturing complex permeate-side flow patterns exhibiting a 4.5 % maximum relative error compared to experiments. Results suggest that deviations of 1D models, assuming ideal flow patterns, from the CFD increase as separation properties improve with material advancements, and can be as high as 21 % for some cases. We also carry out a sensitivity analysis to identify the effect of key parameters on the CO₂ recovery and the CO₂ purity of the outlet streams.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100246"},"PeriodicalIF":3.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280749","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":"Industrial multi-machine data aggregation, AI-ready data preparation, and machine learning for virtual metrology in semiconductor wafer and slider production","authors":"Feiyang Ou ,&nbsp;Julius Suherman ,&nbsp;Chao Zhang ,&nbsp;Henrik Wang ,&nbsp;Sthitie Bom ,&nbsp;James F. Davis ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2025.100242","DOIUrl":"10.1016/j.dche.2025.100242","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Smart Manufacturing (SM), which is short for “Smart (Predictive, Preventive, Proactive) zero incident, zero emissions Manufacturing,” describes manufacturing’s digital transformation in which factories, supply chains and ecosystems are integrated, interoperable, and interconnected. Smart Manufacturing is rooted in AI, Machine Learned (ML), and Data Synchronized (DS) modeling to tap into invaluable operating data. By making data actionable at larger scales, SM opens new ways to increase productivity, precision, and process performance. Smart Manufacturing applied to front-end wafer manufacturing in the semiconductor industry offers significant opportunity to increase production throughput and ensure precision by increasing staff and operational productivity. Front-end wafer manufacturing involves multi tool operations for complex material processing that requires a high degree of precision and extensive product qualification. There is a high degree of commonality with semiconductor manufacturing tools, for example etching, that are well instrumented. Companies are already collecting large amounts of operational data from these tools that can be aggregated and leveraged for virtual metrology and other control, diagnostic, and management solutions. AI/ML/DS modeling involves monitoring the state of an operation in real-time to continuously learn and improve on human centered, automated, and autonomous actions. This operational data are embedded in invaluable machine, process, product, and material behaviors as interaction complexities, linearities/non-linearities, and dimensional effects. Because of machine commonalities, data can be selected to draw out operational value across machines. Today’s data science offers considerable capability for qualifying, assessing alignment and contribution, aggregating, and engineering data for more robust modeling. We refer to this as a Data-first strategy to process, engineer and model with AI-Ready data. In this paper, we address AI-Ready data for a virtual metrology solution focused on etching measurement PASS/FAIL classification and milling depth prediction regression tasks using operational data from production machine tools. If the quality of the product can be predicted, the productivity of the metrology process can be increased, which in turn increases the productivity of the overall operation. In a previous paper, we considered how to aggregate data from different etch tools in the same processes at different factories within Seagate Technology and proposed a method for data aggregation and demonstrated its value (Ou et al., 2024). The present paper considers how to process and engineer datasets from two different etch tool processes: wafer and slider production. The data processing approaches when used systematically with appropriate ML algorithms demonstrate the potential for reducing metrological interventions in semiconductor manufacturing. Advanced machine learning techniques are used to t","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100242"},"PeriodicalIF":3.0,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147463","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":"Automation and control of an experimental protonic membrane steam methane reforming system","authors":"Dominic Peters ,&nbsp;Xiaodong Cui ,&nbsp;Yifei Wang ,&nbsp;Christopher G. Donahue ,&nbsp;Jake Stanley ,&nbsp;Carlos G. Morales-Guio ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2025.100240","DOIUrl":"10.1016/j.dche.2025.100240","url":null,"abstract":"<div><div>Nickel dispersion on doped barium-zirconate ceramics is a state-of-the-art material formulation used to fabricate proton conducting membranes that can reform methane at lower operational temperatures (600 to 800 °<span><math><mi>C</mi></math></span>). Although steady-state operational data have been reported for these ion-conducting ceramic reformers, transient datasets are uncommon and not readily available. Moreover, the automation of protonic membrane reformers is a major technical challenge for the commercialization of modular thermo-electrochemical hydrogen generators with highly nonlinear process dynamics. Here, a multi-input multi-output feedback control scheme has been designed from a relative gain array analysis of three process variables for an experimental 500 W (thermal and electrochemical power consumption) protonic membrane reforming system. Specifically, the proposed control architecture automatically calculates hydrogen separation rate setpoints while safely and effectively reaching hydrogen production rate setpoints and desired steam-to-carbon ratios. The control architecture also drives the system to 99.6% methane conversion at a current density of 0.564 ± 0.0125 A<span><math><mi>⋅</mi></math></span>cm⁻² at 788 °<span><math><mi>C</mi></math></span>. Internal temperature fluctuations are mostly constrained to <span><math><mo>±</mo></math></span> 6.00 °<span><math><mi>C</mi></math></span> <span><math><mi>⋅</mi></math></span>min⁻¹, which improves catalyst longevity when operating at hydrogen recovery rates exceeding 50%. Chief among these findings is an experimental demonstration of a control scenario that alters the hydrogen production rate setpoint every 150 min without sacrificing system-wide controllability. Integrator windup scenarios and counterproductive control actions are also avoided through rational controller design and proper controller tuning exercises. Industrial-scale applications of protonic membrane reformers may therefore be automated to control up to three process variables and have up to three additional control degrees of freedom for process intensification and optimization, making for well-governed, autonomous hydrogen generation units.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100240"},"PeriodicalIF":3.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084476","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":"CFD-based optimization of dynamic cyclones with variable vortex length using GMDH artificial neural network","authors":"Hamed Safikhani ,&nbsp;Somayeh Davoodabadi Farahani ,&nbsp;Lakhbir Singh Brar ,&nbsp;Faroogh Esmaeili","doi":"10.1016/j.dche.2025.100241","DOIUrl":"10.1016/j.dche.2025.100241","url":null,"abstract":"<div><div>Dynamic cyclone separators with adjustable vortex length are widely used in industrial applications such as particle collection and air pollution control. However, optimizing their performance remains a challenge due to complex fluid–particle interactions. This research introduces a three-step multi-objective optimization framework for dynamic cyclones with adjustable vortex length. Initially, computational fluid dynamics (CFD) simulations are utilized to examine airflow behavior in different cyclone designs. The Reynolds-averaged Navier-Stokes (RANS) equations, combined with the Reynolds stress turbulence model, are employed to model turbulence. The Eulerian-Lagrangian approach is used to track particle motion, while the Discrete Random Walk technique simulates velocity variations. In the second phase, data obtained from the numerical simulations is used to construct objective function models, focusing on minimizing pressure drop and maximizing collection efficiency. These models are developed using artificial neural networks based on the Group Method of Data Handling (GMDH). The final step involves optimizing the cyclone designs through the non-dominated sorting genetic algorithm (NSGA). The Pareto front is generated and analyzed, offering valuable insights into cyclone design improvements. The findings highlight that an optimized design for dynamic cyclones with variable vortex length can only be achieved through a systematic multi-objective optimization approach.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100241"},"PeriodicalIF":3.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904429","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}