Digital Chemical Engineering最新文献

{"title":"Comparison of autoencoder architectures for fault detection in industrial processes","authors":"Deris Eduardo Spina ,&nbsp;Luiz Felipe de O. Campos ,&nbsp;Wallthynay F. de Arruda ,&nbsp;Afrânio Melo ,&nbsp;Marcelo F. de S. Alves ,&nbsp;Gildeir Lima Rabello ,&nbsp;Thiago K. Anzai ,&nbsp;José Carlos Pinto","doi":"10.1016/j.dche.2024.100162","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100162","url":null,"abstract":"<div><p>Fault detection constitutes a fundamental task for predictive maintenance, requiring mathematical models that can be conveniently provided by data-driven techniques. Autoencoders are a particular type of unsupervised Artificial Neural Networks that can be suitable for fault detection applications. Diverse architectures might be used for autoencoders, resulting in different fault detection performances, which are usually compared by means of Fault Detection Rates for a fixed threshold of the False Alarm Rate, limiting the conclusions to particular cases. To improve the comparability, the present work uses the area under the receiver operating characteristic curve to compare autoencoder architectures for a range of false alarm rates using the Tennessee Eastman Process benchmark. Performances obtained for shallow and deep autoencoders were compared with those of the denoising and variational autoencoders for undercomplete and sparse structures. Overall, the results indicate better performances for sparse structures, especially for the variational autoencoder and the deep denoising autoencoder, with area under the curve of 98.35%.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"12 ","pages":"Article 100162"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000243/pdfft?md5=c55239f87adb594358ee7dd0b16c8e9d&pid=1-s2.0-S2772508124000243-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141322508","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":"Fault detection using machine learning based dynamic ICA-distributed CCA: Application to industrial chemical process","authors":"Husnain Ali ,&nbsp;Zheng Zhang ,&nbsp;Rizwan Safdar ,&nbsp;Muhammad Hammad Rasool ,&nbsp;Yuan Yao ,&nbsp;Le Yao ,&nbsp;Furong Gao","doi":"10.1016/j.dche.2024.100156","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100156","url":null,"abstract":"<div><p>Unexpected accidents and events in industrial chemical processes have resulted in a considerable number of causalities and property damage. Safety process management in industrial chemical processes is critical to avoid and ensure casualties and property damage. However, due to the immense scope and high complexity of current industrial chemical processes, the traditional safety process management approaches cannot address these challenges to attain adequate fault detection accuracy. To address this issue, an innovative machine learning-based distributed canonical correlation analysis-dynamic independent component analysis (DICA-DCCA) approach is needed to improve the fault detection effectiveness of complicated systems. The (DICA-DCCA) model could potentially detect anomalies and faults in industrial chemical data by utilizing three essential statistics:<span><math><msubsup><mi>I</mi><mi>d</mi><mn>2</mn></msubsup></math></span>,<span><math><msubsup><mi>I</mi><mi>e</mi><mn>2</mn></msubsup></math></span>and squared prediction error (<em>SPE</em>). The practical effectiveness of the proposed frameworks is evaluated and compared using a continuous stirred tank reactor (CSTR) framework as a standard benchmark study. The research findings present that the suggested (DICA-DCCA) approach is more resilient and effective in detecting abnormalities and faults than the ICA and DICA approaches with FDR 100 % and FAR 0 %. The implied research approach is robust, operational, and productive.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"11 ","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000188/pdfft?md5=b0300d9cf2db43a80d477768b4397cd4&pid=1-s2.0-S2772508124000188-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140906453","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":"Graph Neural Network for domain segmentation to predict regions of non-ideal mixing in two-dimensional baffle flow systems","authors":"John White,&nbsp;Jacob M. Miller,&nbsp;R. Eric Berson","doi":"10.1016/j.dche.2024.100155","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100155","url":null,"abstract":"<div><p>This paper presents a novel approach to address computational challenges in predicting flow features by employing a Graph Neural Network (GNN), which is proficient in predicting flow domain values. Traditional Computational Fluid Dynamics (CFD) simulations, although effective, often require substantial computational resources and time, limiting their applicability in time-sensitive scenarios and optimization studies necessitating extensive case studies. The main objective was to evaluate the feasibility of employing node classification on a graph generated from a 2D baffle flow system to segment the domain based on relative fluid age. A second objective was to compare the computational time required for CFD simulations with the inference time of the network to quantify the efficiency gains achieved by utilizing the network. Results demonstrate the potential of utilizing graph convolutional networks for domain segmentation to predict regions of holdup and bypass. The GNN achieved 97% and 92% accuracy in predicting recirculation regions in single and double baffle cases, particularly excelling in high Reynolds number scenarios. Importantly, the proposed GNN-based approach reduces computation time by over 2100%, showcasing significant efficiency gains. Results here highlight the promise of employing graph convolutional networks for flow feature prediction, offering substantial computational efficiency improvements over traditional CFD simulations.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"11 ","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000176/pdfft?md5=d5dbc6855fe5fbd5542ea1f3d85dd370&pid=1-s2.0-S2772508124000176-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140880686","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":"Estimation-based model predictive control of an electrically-heated steam methane reforming process","authors":"Xiaodong Cui ,&nbsp;Berkay Çıtmacı ,&nbsp;Dominic Peters ,&nbsp;Fahim Abdullah ,&nbsp;Yifei Wang ,&nbsp;Esther Hsu ,&nbsp;Parth Chheda ,&nbsp;Carlos G. Morales-Guio ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2024.100153","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100153","url":null,"abstract":"<div><p>The surge in demand for hydrogen (H₂) across diverse sectors, including clean energy transportation and chemical synthesis, underscores the need for a thorough investigation into H₂ production dynamics and the development of effective controllers for industrial applications. This paper focuses on an electrically heated steam methane reforming (SMR) process for H₂ production, offering advantages such as enhanced environmental sustainability, compactness, efficiency, and controllability compared to conventional reforming methods. Electric heating of the entire system allows for adjustments in current to control reactor temperature, thereby impacting hydrogen production rates. However, accurately modeling hydrogen production dynamics presents a formidable challenge, as complex models with high precision are computationally unsuitable for real-time control integration. Considering these factors, an accurate and efficient first-principles-based lumped-parameter model is developed to provide a dependable estimation of hydrogen production in an electrically-heated steam methane reformer. This model is validated experimentally and then utilized in a model predictive controller (MPC). To obtain the necessary state estimate information for the MPC, an extended Luenberger observer (ELO) method is employed to estimate state variables from limited, infrequent and delayed measurements of gas-phase reactor outlet stream and frequent measurements of the reactor temperature. Simulation comparisons with a proportional-integral (PI) controller reveal a much faster response in achieving the desired H₂ production rate under the estimation-based MPC. Additionally, the simulations demonstrate the robustness of the controller to process variability such as a decrease in catalyst activation energy, commonly encountered in the SMR process, highlighting its effectiveness in maintaining stable operation under varying process conditions.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"11 ","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000152/pdfft?md5=6613b027fd25b32d9e69624e7d9a9ed8&pid=1-s2.0-S2772508124000152-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140649207","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":"Modeling and evaluation of the permeate volume in membrane desalination processes using machine-learning techniques","authors":"Vinod Kumar S ,&nbsp;Mukil S ,&nbsp;Naveen P ,&nbsp;Senthil Rathi B","doi":"10.1016/j.dche.2024.100154","DOIUrl":"10.1016/j.dche.2024.100154","url":null,"abstract":"<div><p>Machine learning methodologies are gaining significant recognition as an effective approach for tackling and modelling challenges related to membranes. This study delves into the utilization of machine learning algorithms to forecast the quality of reverse osmosis (RO) water. Specifically, we conduct a comparative analysis of four popular algorithms: decision tree, random forest, support vector machine (SVM), and K-nearest neighbours (KNN). Our dataset comprises essential water quality evaluation features such as temperature, pH, and conductivity. Using these features, we train and test our models, evaluating their performance with metrics like accuracy and root-mean-squared error (RMSE). The outcomes indicate that all four algorithms perform admirably in predicting RO water quality, achieving accuracy scores ranging from 80 % to 95 %. Notably, KNN stands out with the highest accuracy score of 95 %, establishing it as the most effective algorithm for this task. Besides its performance, KNN's simplicity of implementation and interpretability make it a pragmatic choice for real-world applications. This study serves as compelling evidence of the potential of machine learning algorithms for forecasting RO water quality, particularly highlighting KNN's effectiveness in this context. To further enhance the accuracy of RO water quality prediction, future research could explore the inclusion of other features or alternative algorithms.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"11 ","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000164/pdfft?md5=7d7576b1e6be7fb47bf18504030eb571&pid=1-s2.0-S2772508124000164-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140782008","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":"The importance of process intensification in undergraduate chemical engineering education","authors":"Zong Yang Kong ,&nbsp;Eduardo Sánchez-Ramírez ,&nbsp;Jia Yi Sim ,&nbsp;Jaka Sunarso ,&nbsp;Juan Gabriel Segovia-Hernández","doi":"10.1016/j.dche.2024.100152","DOIUrl":"10.1016/j.dche.2024.100152","url":null,"abstract":"<div><p>This perspective article highlights our opinions on the imperative of incorporating Process Intensification (PI) into undergraduate chemical engineering education, recognizing its pivotal role in preparing future engineers for contemporary industrial challenges. The trajectory of PI, from historical milestones to its significance in advancing the United Nations’ Sustainable Development Goals (SDGs), reflects its intrinsic alignment with sustainability, resource efficiency, and environmental stewardship. Despite its critical relevance, the absence of dedicated PI courses in numerous undergraduate chemical engineering programs presents an opportunity for educational enhancement. An exploration of global PI-related courses reveals the potential of educational platforms to fill this void. To address this gap, we advocate for the introduction of a standalone PI course as a minor elective, minimizing disruptions to established curricula while acknowledging the scarcity of PI expertise. The challenges associated with PI integration encompass faculty workload, specialized expertise, curriculum content standardization, and industry alignment. Surmounting these challenges necessitates collaborative efforts among academia, industry stakeholders, and policymakers, emphasizing the manifold benefits of PI, faculty development initiatives, and the establishment of continuous improvement mechanisms. The incorporation of PI into curricula signifies a transformative approach, cultivating a cadre of innovative engineers poised to meet the demands of the evolving industrial landscape.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"11 ","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000140/pdfft?md5=e5a9fa940af190b7644b1883fa862288&pid=1-s2.0-S2772508124000140-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140794080","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":"Artificial intelligence – Human intelligence conflict and its impact on process system safety","authors":"Rajeevan Arunthavanathan ,&nbsp;Zaman Sajid ,&nbsp;Faisal Khan ,&nbsp;Efstratios Pistikopoulos","doi":"10.1016/j.dche.2024.100151","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100151","url":null,"abstract":"<div><p>In the Industry 4.0 revolution, industries are advancing their operations by leveraging Artificial Intelligence (AI). AI-based systems enhance industries by automating repetitive tasks and improving overall efficiency. However, from a safety perspective, operating a system using AI without human interaction raises concerns regarding its reliability. Recent developments have made it imperative to establish a collaborative system between humans and AI, known as Intelligent Augmentation (IA). Industry 5.0 focuses on developing IA-based systems that facilitate collaboration between humans and AI. However, potential conflicts between humans and AI in controlling process plant operations pose a significant challenge in IA systems. Human-AI conflict in IA-based system operation can arise due to differences in observation, interpretation, and control action. Observation conflict may arise when humans and AI disagree with the observed data or information. Interpretation conflicts may occur due to differences in decision-making based on observed data, influenced by the learning ability of human intelligence (HI) and AI. Control action conflicts may arise when AI-driven control action differs from the human operator action. Conflicts between humans and AI may introduce additional risks to the IA-based system operation. Therefore, it is crucial to understand the concept of human-AI conflict and perform a detailed risk analysis before implementing a collaborative system. This paper aims to investigate the following: 1. Human and AI operations in process systems and the possible conflicts during the collaboration. 2. Formulate the concept of observation, interpretation, and action conflict in an IA-based system. 3. Provide a case study to identify the potential risk of human-AI conflict.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"11 ","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000139/pdfft?md5=717b713a0304b1ad376553ead2d81709&pid=1-s2.0-S2772508124000139-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140548183","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":"OpenCrystalData: An open-access particle image database to facilitate learning, experimentation, and development of image analysis models for crystallization processes.","authors":"Yash Barhate ,&nbsp;Christopher Boyle ,&nbsp;Hossein Salami ,&nbsp;Wei-Lee Wu ,&nbsp;Nina Taherimakhsousi ,&nbsp;Charlie Rabinowitz ,&nbsp;Andreas Bommarius ,&nbsp;Javier Cardona ,&nbsp;Zoltan K. Nagy ,&nbsp;Ronald Rousseau ,&nbsp;Martha Grover","doi":"10.1016/j.dche.2024.100150","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100150","url":null,"abstract":"<div><p>Imaging and image-based process analytical technologies (PAT) have revolutionized the design, development, and operation of crystallization processes, providing greater process understanding through the characterization of particle size, shape and crystallization mechanisms in real-time. The performance of corresponding PAT models, including machine learning/artificial intelligence (ML/AI)-based approaches, is highly reliant on the data quality used for training or validation. However, acquiring high quality data is often time consuming and a major roadblock in developing image analysis models for crystallization processes.</p><p>To address the lack of diverse, high-quality, and publicly available particle image datasets, this paper presents an initiative to create an open-access crystallization-related image database: OpenCrystalData (OCD, at <span>www.kaggle.com/opencrystaldata/datasets</span><svg><path></path></svg>). The datasets consist of images from different crystallization systems with different particle sizes and shapes captured under various conditions. The initial release consists of four different datasets, addressing the estimation of particle size distribution using <em>in-situ</em> images for different categories of particles and detection of anomalous particles for process monitoring purposes. Images are collected using various instruments, followed by case-specific processing steps, such as ground-truth labeling and particle size characterization using offline microscopy. Datasets are released on the online collaborative platform Kaggle, along with specific guidelines for each dataset. These datasets are aimed to serve as a resource for researchers to enable learning, experimentation, development, and evaluation and comparison of different analytical approaches and algorithms. Another goal of this initiative is to encourage researchers to contribute new datasets focusing on various systems and problem statements. Ultimately, OpenCrystalData is intended to facilitate and inspire new developments in imaging-based PAT for crystallization processes, encouraging a shift from time-consuming offline analysis towards comprehensive real-time process insights that drive product quality.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"11 ","pages":"Article 100150"},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000127/pdfft?md5=68a1edf1f7c56a1d9eb1baf8911ef096&pid=1-s2.0-S2772508124000127-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140540552","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":"Traveling of multiple salesmen to dynamically changing locations for satisfying multiple goals","authors":"Anubha Agrawal,&nbsp;Manojkumar Ramteke","doi":"10.1016/j.dche.2024.100149","DOIUrl":"10.1016/j.dche.2024.100149","url":null,"abstract":"<div><p>Polymer grade scheduling, maritime surveillance, e-food delivery, e-commerce, and military tactics necessitate multiple agents (e.g., extruders, speed boats, salesmen) capable of visiting (or completing) dynamically changing locations (or tasks) in minimum time and distance. This study proposes a novel methodology based on clustering and local heuristic-based evolutionary algorithms to address the dynamic traveling salesman problem (TSP) and the dynamic multi-salesman problem with multiple objectives. The proposed algorithm is evaluated on 11 benchmark TSP problems and large-scale problems with up to 10,000 instances. The results show the superior performance of the proposed methodology called the dynamic two-stage evolutionary algorithm as compared to the dynamic hybrid local search evolutionary algorithm. Furthermore, the algorithm's applicability is illustrated through various scenarios involving up to four salesmen and three objectives with dynamically changing locations. To demonstrate real-world relevance, a maritime surveillance problem employing a helideck monitoring system is solved, wherein the objective is to minimize the patrolling route while visiting faulty vessels that threaten marine vessels. This study provides a general framework of TSP which finds application in several sectors, including planning and scheduling in chemical and manufacturing industries, the defense sector, and the e-commerce sector. Finally, the results showcase the effectiveness of the proposed methodology in solving the dynamic multiobjective, and multiple salesmen problem, which represents a more generalized version of the TSP.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"11 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000115/pdfft?md5=475e498f67dd75c5f125ba5c42e19411&pid=1-s2.0-S2772508124000115-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140401922","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":"Virtual reality-based bioreactor digital twin for operator training","authors":"Mahmudul Hassan ,&nbsp;Gary Montague ,&nbsp;Muhammad Zahid Iqbal ,&nbsp;Jack Fahey","doi":"10.1016/j.dche.2024.100147","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100147","url":null,"abstract":"<div><p>The use of immersive technologies and digital twins can enhance training and learning outcomes in various domains. These technologies can reduce the cost and risk of training and improve the retention and transfer of knowledge by providing feedback in real-time. In this paper, a novel virtual reality (VR) based Bioreactor simulation is developed that covers the set-up and operation of the process. It allows the trainee operator to experience infrequent events, and reports on the effectiveness of their response. An embedded complex simulation of the bioreaction effectively replicates the impact of operator decisions to mimic the real-world experience. The need to train and assess the skills acquired aligns with the requirements of manufacturing in a validated environment, where proof of operator capability is a prerequisite. It has been deployed at UK’s National Horizons Center(NHC) to train the trainees in biosciences.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"11 ","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000097/pdfft?md5=cfa32298f2740dc63cfe1690f6a4384f&pid=1-s2.0-S2772508124000097-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140341900","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}