Digital Chemical Engineering最新文献

{"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 , Julius Suherman , Chao Zhang , Henrik Wang , Sthitie Bom , James F. Davis , Panagiotis D. Christofides","doi":"10.1016/j.dche.2025.100242","DOIUrl":"10.1016/j.dche.2025.100242","url":null,"abstract":"<div><div>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":"2025-06-01","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":"Revolutionizing perfume creation: PTD's innovative approach","authors":"Asma Iqbal,&nbsp;Mohammad Amil Bhat,&nbsp;Qazi Muneeb,&nbsp;Muazam Javid","doi":"10.1016/j.dche.2025.100223","DOIUrl":"10.1016/j.dche.2025.100223","url":null,"abstract":"<div><div>The Perfumery Ternary Diagram (PTD) is a powerful tool in perfumery for analyzing perfume mixtures comprising three fragrant components and a solvent base. It combines ternary diagrams with perfume pyramids to swiftly evaluate odor characteristics and composition in the headspace across various concentrations, bypassing time-consuming experimental processes. Using a diffusion model to simulate evaporation, this study utilizes PTDs to track changes in the liquid and gas-liquid interface. Using Python, we calculated the OVs of each component at 25 °C, based on molecular weight, saturated vapor pressure, and odor threshold. The data was processed and visualized in MATLAB, producing PTDs that highlighted the component with the highest OV at any given composition. Furthermore, initially as the mole fraction continues to rise, the percentage decrease in odor value is approximately 11.1 %, indicating a diminishing rate of change. The distribution of odor values is elaborated in the MATLAB diagrams that give a comprehensive representation of how the odor value varies with different compositions. The PTDs were effective in representing the critical role of individual components, making them valuable tools for perfumers and researchers. The PTD analysis revealed that limonene (top note) demonstrated the highest odor value (OV) at concentrations above 60 % within the mixture, while vanillin (base note) maintained stability at lower concentrations, supporting its role as a fixative. These findings validate PTDs as predictive tools, accurately reflecting odor value variations across different fragrance compositions. This study investigates whether Perfumery Ternary Diagrams (PTDs) can reliably predict odor value distributions within perfume mixtures, thus providing a practical and efficient tool for optimizing fragrance compositions.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100223"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polynomial Neural Networks for improved AI transparency: An analysis of their inherent explainability (operational rationale) capabilities","authors":"Donovan Chaffart ,&nbsp;Yue Yuan","doi":"10.1016/j.dche.2025.100230","DOIUrl":"10.1016/j.dche.2025.100230","url":null,"abstract":"<div><div>The demand for reliable Artificial Intelligence (AI) models within critical domains such as Chemical Engineering has garnered significant attention towards the use and development of transparent AI methodologies. Nevertheless, the field of AI transparency has received an uneven level of attention, such that crucial aspects like <em>explainability</em> (i.e., the transparency of the AI's operational rationales) have remained understudied. To address this challenge, this study investigates the inherent <em>explainability</em> capabilities of Polynomial Neural Networks (PNNs) for applications within Chemical Engineering. PNNs, which implement higher-order polynomials in lieu of linear expressions within their hidden layer neurons, are inherently nonlinear, and thus do not require an activation function to accurately capture the behavior of a system. Accordingly, these neural networks provide continuous, closed-form algebraic expressions that can be used to ascertain the contributions of individual features in the AI architecture towards the network operational behavior. In order to study this behavior, the PNN method was adopted in this work to capture the relationships of noiseless and noisy data derived according to simple mathematical expressions. The PNN polynomials were then extracted and examined to highlight the insights they provide regarding the system operational rationales. The PNN method was furthermore applied to capture the behavior of a circulating fluidized bed reactor to fully showcase the <em>explainative</em> capability of this method within a Chemical Engineering application. These studies highlight the intrinsic <em>explainability</em> capabilities of PNNs and demonstrated their potential for reliable AI implementations for applications in Chemical Engineering.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100230"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing cybersecurity of nonlinear processes via a two-layer control architecture","authors":"Arthur Khodaverdian ,&nbsp;Dhruv Gohil ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2025.100233","DOIUrl":"10.1016/j.dche.2025.100233","url":null,"abstract":"<div><div>This work proposes a novel two-layer multi-key control architecture to enhance the resilience of nonlinear chemical processes to cyberattacks. The architecture consists of an upper-layer nonlinear controller and a lower-layer of encrypted linear controllers. The nonlinear controllers process unencrypted sensor data to determine optimal control actions, which are then used to estimate the closed-loop state trajectory using a first-principle model of the plant. This trajectory is sampled and mapped to a valid subset before encryption, which can lead to minor inaccuracies. The resulting encrypted state-space data samples are used as set-points for the lower-layer controllers, which can be implemented using encrypted signals, allowing for obfuscation of the computation and transmission of the applied control inputs, thereby enhancing cybersecurity. This study further improves security by taking advantage of the Single-Input-Single-Output nature of some linear control methods to allocate a unique encryption key to each linear controller and its respective sensor data. Two nonlinear chemical process applications, including a benchmark chemical reactor example and one application modeled through the use of Aspen Dynamics, are used to demonstrate the application of the proposed two-layer architecture.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100233"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time process safety and systems decision-making toward safe and smart chemical manufacturing","authors":"Austin Braniff ,&nbsp;Sahithi Srijana Akundi ,&nbsp;Yuanxing Liu ,&nbsp;Beatriz Dantas ,&nbsp;Shayan S. Niknezhad ,&nbsp;Faisal Khan ,&nbsp;Efstratios N. Pistikopoulos ,&nbsp;Yuhe Tian","doi":"10.1016/j.dche.2025.100227","DOIUrl":"10.1016/j.dche.2025.100227","url":null,"abstract":"<div><div>The ongoing digital transformation has created new opportunities for chemical manufacturing with increasing plant interconnectivity and data accessibility. This paper reviews state-of-the-art research developments which offer the potential for real-time process safety and systems decision-making in the digital era. An overview is first presented on online process safety management approaches, including dynamic risk analysis and fault diagnosis/prognosis. Advanced operability and control methods are then discussed to achieve safely optimal operations under uncertainty (e.g., flexibility analysis, safety-aware control, fault-tolerant control). We highlight the connections between systems-based operation and process safety management to achieve operational excellence while proactively reducing potential safety losses. We also review the developments and showcases of digital twins paving the way to actual cyber–physical integration. Outstanding challenges and opportunities are identified such as safe data-driven control, integrated operability, safety and control, cyber–physical demonstration, etc. Toward this direction, we present our ongoing developments of the REal-Time Risk-based Optimization (RETRO) framework for safe and smart process operations.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100227"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review","authors":"Wenlong Wang ,&nbsp;Zhe Wu ,&nbsp;Dominic Peters ,&nbsp;Berkay Citmaci ,&nbsp;Carlos G. Morales-Guio ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2025.100237","DOIUrl":"10.1016/j.dche.2025.100237","url":null,"abstract":"<div><div>Electrochemical reactors play a critical role in various industrial sectors, including energy storage, chemical production, and environmental engineering. The complexity of these systems – arising from coupled electrochemical reactions with mass, heat and charge transport phenomena – poses significant challenges in modeling, analysis, and control. Machine learning (ML) has emerged as a promising tool for addressing these challenges by providing data-driven solutions to complex process modeling, optimization, and advanced control. This tutorial review explores the state-of-the-art applications of ML in electrochemical reactor systems, including ML-based modeling techniques and ML-based advanced control strategies, followed by the discussions of practical challenges and their solutions. An electrochemical carbon dioxide (CO₂) reduction reactor is used as a demonstration example to show the effectiveness of various modeling and control methods. In addition, an integrated data infrastructure platform is presented for the digitalization and control of the electrochemical CO₂ reduction reactor. By identifying current gaps and future opportunities, this article provides a roadmap for leveraging ML tools to improve the analysis and operation of electrochemical reactors.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100237"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887941","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":"Green hydrogen extraction from natural gas transmission grids using hybrid membrane and PSA processes optimized via bayesian techniques","authors":"Homa Hamedi,&nbsp;Torsten Brinkmann","doi":"10.1016/j.dche.2025.100234","DOIUrl":"10.1016/j.dche.2025.100234","url":null,"abstract":"<div><div>Green hydrogen (H₂) is a leading enabler for the decarbonization of hard-to-abate industries where electrification is either uneconomical or infeasible. Establishing an adequate and cost-effective infrastructure for hydrogen distribution remains one of the primary barriers to its widespread adoption. A promising short-term solution to this challenge involves H₂ storage and co-transportation via existing gas grids. For H₂ extraction from distribution gas grids, standalone pressure swing adsorption systems are considered the most viable option, whereas a hybrid process is suggested in the literature for transmission gas networks. This article presents a comprehensive techno-economic model for the proposed hybrid process, developed using an integrated platform based on Aspen Adsorption and Aspen Custom Modeler. The system consists of a single-stage hollow fiber Matrimid membrane module, followed by a 4-bed adsorption process operating in 8 sequential steps to meet H₂ market purity requirements with an acceptable recovery rate. Since the performances of these two separation modules, as an integrated system, significantly influence each other, the study identifies a unique opportunity to minimize separation costs through process optimization. To reduce computational time, a cyclic steady-state approach was employed to simulate the PSA process. Bayesian optimization, facilitated by the integration of Python with Aspen Adsorption, was used to efficiently identify the optimal solution with a minimal number of objective function evaluations. The levelized cost of H₂ separation (99.0 % purity at 10 bar) from natural gas containing 10 % H₂ at pressures of 35 bar and 60 bar is estimated to be 2.7310 and, $2.5116/kg-H₂, respectively. These estimates correspond to a scenario with 10 identical trains, each handling a feed flowrate of 200 kmol/hr. Increasing the number of trains keeps the cost contribution of PSA constant; however, the total cost decreases as the compression fixed cost is distributed across more trains.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100234"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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":"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}