Digital Chemical Engineering最新文献

{"title":"Accelerated modeling and design of a mixed refrigerant cryogenic process using a data-driven approach","authors":"Hosein Alimardani ,&nbsp;Mehrdad Asgari ,&nbsp;Roohangiz Shivaee-Gariz ,&nbsp;Javad Tamnanloo","doi":"10.1016/j.dche.2024.100143","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100143","url":null,"abstract":"<div><p>Cryogenic processes with mixed refrigerants are prevalent in energy-intensive chemical industries, enhancing energy efficiency while reducing costs and unit size. However, the curse of dimensionality and process design constraints pose significant hurdles for effective screening and optimization. To tackle this, we developed a neural network model for natural gas liquefaction prediction. Trained on an extensive Aspen HYSYS database, our ML model accurately simulates LNG processes, with an impressive <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> test value of 99.63, operating almost ten million times faster than HYSYS. It effectively addresses vital process design constraints, including liquid slugging and temperature cross, crucial for optimization. By integrating the ML model with genetic and Nelder–Mead algorithms, we achieve an 8.9% reduction in total exergy, outperforming Aspen HYSYS within the same time frame. Our study underscores ML’s significance in modeling energy-intensive chemical processes, providing insights into the exergy profile and enabling feature importance analysis.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100143"},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277250812400005X/pdfft?md5=cb4dcb0cfd121a5b865f2a5c7ff25e37&pid=1-s2.0-S277250812400005X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139693908","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":"Integrating transfer learning within data-driven soft sensor design to accelerate product quality control","authors":"Sam Kay ,&nbsp;Harry Kay ,&nbsp;Max Mowbray ,&nbsp;Amanda Lane ,&nbsp;Cesar Mendoza ,&nbsp;Philip Martin ,&nbsp;Dongda Zhang","doi":"10.1016/j.dche.2024.100142","DOIUrl":"10.1016/j.dche.2024.100142","url":null,"abstract":"<div><p>The measurement of batch quality indicators in real time operation is plagued with many challenges, hence soft sensing has become a promising solution within industrial research. However, small data has traditionally been a severe problem, hindering the ability to create accurate, reliable soft sensors, especially within industrial research and development for new product formulations. Nevertheless, it is often the case that modelling knowledge is available for a related system. In order to exploit this, we have developed a generalisable transfer learning methodology which takes advantage of previous modelling efforts to accelerate and improve the construction of models for new systems. Specifically, we adapted a recently developed advanced data-driven soft sensing methodology made for an existing process formulation and integrated a feature-based transfer learning approach to facilitate the modelling of two new industrial process systems, each of which containing notable differences to the original. The performance of the transfer soft sensors was tested rigorously and compared to a benchmark approach under different data availability conditions. It was shown that, the proposed transfer mechanism yielded high accuracy, and is robust to small data scenarios, indicating its potential for use in soft sensing of novel systems.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100142"},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000048/pdfft?md5=c15ad978e84f38981439079c12f32afa&pid=1-s2.0-S2772508124000048-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139633564","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":"Design of microfluidic chromatographs through reinforcement learning","authors":"Mohammad Shahab ,&nbsp;Raghunathan Rengaswamy","doi":"10.1016/j.dche.2024.100141","DOIUrl":"10.1016/j.dche.2024.100141","url":null,"abstract":"<div><p>Chromatography is one of the most valuable techniques chemists possess at their disposal, conducive to everything from developing vaccines, food, beverage, and drug testing to catching criminals. The diverse applications allow it to be used for analytical and preparative purposes. On the other hand, droplet microfluidics has significantly evolved from simple droplet generators to complex and integrated tasks through specially designed networks. Microfluidics finds itself at the center of various Lab-on-Chip studies, enabling single-cell analysis, biochemical synthesis, etc. We demonstrate a microfluidic chromatograph machine that can produce an ordered droplet arrangement for a large number of drops. The droplets are sent into the device using a novel methodology where the conventional droplet train is made into smaller batches. The study describes the use of droplet batch methodology and compares it with the traditional droplet train approach. Using this platform, different droplet sequences are sent through the chromatograph, which preferably allows some droplets to exit first while others take a longer time to flow across the chromatograph based on the droplet properties and device design. The droplet sequences contain various drops; however, the type of drops in these sequences is limited to 2. The chromatograph can handle any number of drops in a single machine is enough for handling diverse droplet sequences. The stability of the microfluidic chromatography is also studied by varying the droplet properties and the droplet batch size.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000036/pdfft?md5=cfa177e155cab5c162a4e0b7e3220cf1&pid=1-s2.0-S2772508124000036-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139638475","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":"Multiscale computational fluid dynamics modeling of an area-selective atomic layer deposition process using a discrete feed method","authors":"Henrik Wang ,&nbsp;Matthew Tom ,&nbsp;Feiyang Ou ,&nbsp;Gerassimos Orkoulas ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2024.100140","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100140","url":null,"abstract":"<div><p>Area-selective atomic layer deposition (AS-ALD) is a beneficial procedure that facilitates self-alignment for transistor stacking by concentrating oxide growth on targeted areas of a substrate. However, AS-ALD is difficult to incorporate into semiconductor manufacturing industries due to difficulties such as minimal process data and a lack of insight into reactor design. To enable the industrial scale-up of AS-ALD, <em>in silico</em> modeling is necessary to characterize the process. Thus, this work proposes a multiscale computational fluid dynamics modeling framework that simultaneously describes the surface chemistry and ambient fluid behavior for an Al₂O₃/SiO₂ substrate. The multiscale model first involves <em>ab initio</em> molecular dynamics simulations to optimize molecular structures involved in the AS-ALD reactions. Next, a kinetic Monte Carlo simulation is performed to describe the stochastic surface chemistry behavior to determine the surface coverage, and deposition and byproduct rates. Lastly, computational fluid dynamics is performed to study the spatiotemporal behavior of the flow. The surface and flow field simulations are carried out in an integrated fashion. Various AS-ALD discrete feed reactor configurations with differing injection plate geometries were developed to investigate their impact on the processing time to achieve full surface coverage and film uniformity. Results indicate that the multi-inlet reactor model achieves minimal processing time while producing a high-quality film with the AS-ALD process.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000024/pdfft?md5=7393217bfc2d5d5b0070453869905996&pid=1-s2.0-S2772508124000024-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139549753","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":"Adaptive digital twins for energy-intensive industries and their local communities","authors":"Timothy Gordon Walmsley ,&nbsp;Panos Patros ,&nbsp;Wei Yu ,&nbsp;Brent R. Young ,&nbsp;Stephen Burroughs ,&nbsp;Mark Apperley ,&nbsp;James K. Carson ,&nbsp;Isuru A. Udugama ,&nbsp;Hattachai Aeowjaroenlap ,&nbsp;Martin J. Atkins ,&nbsp;Michael R. W. Walmsley","doi":"10.1016/j.dche.2024.100139","DOIUrl":"10.1016/j.dche.2024.100139","url":null,"abstract":"<div><p>Digital Twins (DTs) are high-fidelity virtual models that behave-like, look-like and connect-to a physical system. In this work, the physical systems are operations and processes from energy-intensive industrial plants and their local communities. The creation of DTs demands expertise not just in engineering, but also in computer science, data science, and artificial intelligence. Here, we introduce the Adaptive Digital Twins (ADT) concept, anchored in five attributes inspired by the self-adaptive systems field from software engineering. These attributes are self-learning, self-optimizing, self-evolving, self-monitoring, and self-protection. This new approach merges cutting-edge computing with pragmatic engineering needs. ADTs can enhance decision-making in both the design phase and real-time operation of industrial facilities and allow for versatile 'what-if' scenario simulations. Seven applications within the energy-intensive industries are described where ADTs could be transformative.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000012/pdfft?md5=afa95153df0ac493e8b74986aab47748&pid=1-s2.0-S2772508124000012-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139393960","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":"Model predictive control of an electrically-heated steam methane reformer","authors":"Berkay Çıtmacı ,&nbsp;Xiaodong Cui ,&nbsp;Fahim Abdullah ,&nbsp;Derek Richard ,&nbsp;Dominic Peters ,&nbsp;Yifei Wang ,&nbsp;Esther Hsu ,&nbsp;Parth Chheda ,&nbsp;Carlos G. Morales-Guio ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2023.100138","DOIUrl":"https://doi.org/10.1016/j.dche.2023.100138","url":null,"abstract":"<div><p>Steam methane reforming (SMR) is one of the most widely used hydrogen (H₂) production processes. In addition to its extensive utilization in industrial sectors, hydrogen is expanding it share as a clean energy carrier, and more sustainable and efficient H₂ production methods are continuously being explored and developed. One method replaces conventional fossil fuel-based heating with electrical heating through the flow of electrons across the reformer. At UCLA, an experimental setup was built of an electrically heated steam methane reforming process. This paper describes the system components, explains the digitalization of the experimental setup and introduces methods for building a first-principles-based dynamic process model using parameters estimated via data-driven methods from process experimental data. The modeling approach uses a lumped parameter approximation and employs algebraic equations to solve for gas-phase variables. The reaction parameters are calculated from steady-state experimental data, and the temperature change is modeled with respect to change in electric current using a first-order dynamic model. The overall dynamic process model is then used in a computational model predictive control (MPC) scheme to drive the process to a new H₂ production set-point under unperturbed and steam flowrate disturbance cases. The performance and robustness of the proposed MPC scheme are compared to the ones of a classical proportional–integral (PI) controller and are demonstrated to be superior in terms of closed-loop response, robustness, and constraint handling.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277250812300056X/pdfft?md5=056a9bd7a7e5b135f7111e6adb9943c4&pid=1-s2.0-S277250812300056X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139100925","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":"Predictive models for upstream mammalian cell culture development - A review","authors":"Bhagya S. Yatipanthalawa ,&nbsp;Sally L. Gras","doi":"10.1016/j.dche.2023.100137","DOIUrl":"https://doi.org/10.1016/j.dche.2023.100137","url":null,"abstract":"<div><p>The production of therapeutic proteins in mammalian cell culture is an essential unit operation in biopharmaceutical manufacture that can benefit from the predictive insights of effective process models, leading to accelerated process development and improved process control. This review outlines and evaluates current approaches to predictive model development for mammalian cell culture and protein production. Classical mechanistic and data driven approaches are analysed, together with potential challenges in model development and application, including the experimental requirements for parameter estimation. Hybrid models, which may offer greater robustness, are then explored along with hybrid model architecture and the steps involved in model development. Successful examples from other cell fermentation processes are also considered, for application to the development, monitoring and control of mammalian processes.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508123000558/pdfft?md5=d0b39ad5197b66dda71f79bcfd165abe&pid=1-s2.0-S2772508123000558-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139107726","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":"A review and perspective on hybrid modeling methodologies","authors":"Artur M. Schweidtmann,&nbsp;Dongda Zhang,&nbsp;Moritz von Stosch","doi":"10.1016/j.dche.2023.100136","DOIUrl":"https://doi.org/10.1016/j.dche.2023.100136","url":null,"abstract":"<div><p>The term hybrid modeling refers to the combination of parametric models (typically derived from knowledge about the system) and nonparametric models (typically deduced from data). Despite more than 20 years of research, over 150 scientific publications (Agharafeie et al., 2023), and some recent industrial applications on this topic, the capabilities of hybrid models often seem underrated, misunderstood, and disregarded by other disciplines as “simply combining some models” or maybe it has gone unnoticed at all. In fact, hybrid modeling could become an enabling technology in various areas of research and industry, such as systems and synthetic biology, personalized medicine, material design, or the process industries. Thus, a systematic investigation of the hybrid model properties is warranted to scoop the full potential of machine learning, reduce experimental effort, and increase the domain in which models can predict reliably.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508123000546/pdfft?md5=e903c06645add17b5290e3b601ba61ee&pid=1-s2.0-S2772508123000546-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138770101","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":"A graph embedding based fault detection framework for process systems with multi-variate time-series datasets","authors":"Umang Goswami ,&nbsp;Jyoti Rani ,&nbsp;Hariprasad Kodamana ,&nbsp;Prakash Kumar Tamboli ,&nbsp;Parshotam Dholandas Vaswani","doi":"10.1016/j.dche.2023.100135","DOIUrl":"https://doi.org/10.1016/j.dche.2023.100135","url":null,"abstract":"<div><p>Due to the enormous potential of modelling, graph-based approaches have been used for various applications in the process industries. In this study, we propose a fault detection framework through graphs by utilising its attributes in the form of node embeddings. Shallow embedding methods are deployed to generate node embedding vectors. Shallow embedding methods are broadly classified into matrix factorisation and skip-gram-based methods. Node2vec and Deepwalk fall under skip-gram models, while GraphRep and HOPE constitute the Matrix factorisation methods. Node embedding values generated from these methods are then fed to the variational auto-encoder, which ranks the nodes in reconstruction loss value. The node embedding reconstruction loss values exceeding a particular threshold are considered outliers. The proposed work has been validated on NPCIL power-flux data and the benchmark Tennessee Eastman data. The results indicate that skip-gram models, especially Node2vec-VAE, outperformed the matrix factorisation methods for both the above-mentioned datasets.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508123000534/pdfft?md5=1251fb013b40db08915ec20c700f5e1d&pid=1-s2.0-S2772508123000534-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138577499","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":"Investigating an amplitude amplification-based optimization algorithm for model predictive control","authors":"Kip Nieman ,&nbsp;Helen Durand ,&nbsp;Saahil Patel ,&nbsp;Daniel Koch ,&nbsp;Paul M. Alsing","doi":"10.1016/j.dche.2023.100134","DOIUrl":"10.1016/j.dche.2023.100134","url":null,"abstract":"<div><p>The potential for greater algorithmic efficiency for some problems on quantum computers compared to classical computers is appealing in many fields including, for example, the process systems engineering field. While quantum algorithms have been studied for a variety of applications related to optimization, molecular modeling, and machine learning, there remain many applications in process systems engineering, including process control, where it is not clear how quantum computing algorithms would be beneficial. One idea for attempting to understand when a quantum algorithm might provide benefits for control is to start with algorithms that would be expected to benefit “similar” problems (e.g., optimization problems) and to see if controllers can be implemented within those algorithmic frameworks. Therefore, in this work, we study the use of a quantum computing algorithm related to Grover’s algorithm, which is an amplitude amplification strategy that can search an unordered list with improved efficiency compared to a classical algorithm for the task. It has been extended to perform a search for optimal paths over a graph. Given its potential utility for search and optimization, this is an example of an algorithm where we might wonder if it could be adjusted or used to provide speed-ups for large control problems if the controller could function within this algorithmic framework. This work provides the first steps toward attempting to address this question by investigating how optimization-based control problems would fit into this framework. A process described by <span><math><mrow><mover><mrow><mi>x</mi></mrow><mrow><mo>̇</mo></mrow></mover><mo>=</mo><mi>x</mi><mo>+</mo><mi>u</mi></mrow></math></span> is considered as a test case. The modified Grover’s algorithm requires the optimization problem to be mapped into quantum gates. We discuss ideas for attempting to represent an optimization-based controller known as model predictive control (MPC) in the modified Grover’s algorithm framework. We test how various parameters of the control and quantum algorithm designs, including fundamental parameters in MPC such as the number of sampling periods and length of the sampling periods, impact the success of using the quantum algorithm for the MPC. We provide analyses regarding why the results are what they are to give perspective on how quantum computing algorithms work and intersect with engineering problems.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100134"},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508123000522/pdfft?md5=1b4e6df9badcb829360ce4b7f801844b&pid=1-s2.0-S2772508123000522-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139300644","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}