Journal of advanced manufacturing and processing最新文献

{"title":"An integral monitoring concept for data-driven detection and localization of incipient leakages by fusion of process and environment data","authors":"Kristian Kasten,&nbsp;Caroline Charlotte Zhu,&nbsp;Joachim Birk,&nbsp;Steven X. Ding","doi":"10.1002/amp2.10133","DOIUrl":"10.1002/amp2.10133","url":null,"abstract":"<p>The risk of leakages in process industry is environmentally critical and potentially hazardous. Many technologies and schemes for process monitoring are theoretically developed and applied in an industrial context. Nevertheless, most approaches still focus on individual monitoring of a process and its environment. The major challenge is the lack of <i>a priori</i> knowledge about the leakage. This paper introduces a new approach combining monitoring of the environment and its embedded process. The application on an industrial use-case in a real plant environment illustrates the success of this combined monitoring approach as well as a decision support to localize an incipient leakage.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10133","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42307159","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":"Techno-economic, environmental, and social measurement of clean energy technology supply chains","authors":"Jill A. Engel-Cox,&nbsp;Hope M. Wikoff,&nbsp;Samantha B. Reese","doi":"10.1002/amp2.10131","DOIUrl":"https://doi.org/10.1002/amp2.10131","url":null,"abstract":"<p>In addition to the criteria of reliability and cost, clean energy technologies, such as wind, solar, and batteries, need to strive to a higher standard of environmental and societal benefit along their entire supply chain. This means additional performance metrics for these technologies should be considered, such as embodied energy, embodied carbon, recycled content and recyclability, environmental impact of material sourcing, impact on land and ecosystems, materials recovery at end of life, and production through quality nonexploitive jobs with community benefit. Many commercial and emerging energy technologies have not yet been explicitly evaluated based on these environmental and social performance metrics, which presents multiple opportunities for researchers and analysts. In this paper, we review the importance and current limitations of techno-economic and life-cycle assessment models for research design and manufacturing decisions. We explore emerging manufacturing modeling options that could improve environmental and social performance and how they could be used to help guide research. Even with the deployment of low-carbon energy-generation technologies, the future of a successful clean energy transition requires collaboration between researchers, advanced manufacturers, independent standards and tracking organizations, local communities, and national governments, to ensure the financial, environmental, and social sustainability of the entire supply and manufacturing process of energy technologies.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72148008","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":"Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on-site deployment case study","authors":"Richard P. Donovan,&nbsp;Yoon G. Kim,&nbsp;Anthony Manzo,&nbsp;Yutian Ren,&nbsp;Shijie Bian,&nbsp;Tongzi Wu,&nbsp;Shweta Purawat,&nbsp;Henry Helvajian,&nbsp;Marilee Wheaton,&nbsp;Bingbing Li,&nbsp;Guann-Pyng Li","doi":"10.1002/amp2.10130","DOIUrl":"10.1002/amp2.10130","url":null,"abstract":"<p>In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real-world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42264601","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":"Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design","authors":"Yoon G. Kim,&nbsp;Richard P. Donovan,&nbsp;Yutian Ren,&nbsp;Shijie Bian,&nbsp;Tongzi Wu,&nbsp;Shweta Purawat,&nbsp;Anthony J. Manzo,&nbsp;Ilkay Altintas,&nbsp;Bingbing Li,&nbsp;Guann-Pyng Li","doi":"10.1002/amp2.10129","DOIUrl":"10.1002/amp2.10129","url":null,"abstract":"<p>The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet-of-things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low-cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon-based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44071796","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":"First principles of smart manufacturing","authors":"Conrad Leiva","doi":"10.1002/amp2.10123","DOIUrl":"10.1002/amp2.10123","url":null,"abstract":"<p>The democratizing of innovation and technology happens when tools and expertise are made affordable and accessible at scale to most manufacturers. To realize the democratization of smart manufacturing innovation, it is not only necessary to democratize the technology, but also necessary to provide wide access to the knowledge required to implement the strategies and leverage the solutions and insights in a more digitally enabled manufacturing ecosystem. We are at a point in the journey where it is time to converge on a concrete set of guiding principles and a framework that organizes expectations and requirements for the implementation of smart manufacturing. This paper summarizes the practices that define smart manufacturing as developed by the industry leaders, early adopters, and expert practitioners working in the ecosystem at CESMII, the smart manufacturing institute. In smart manufacturing, organizations, people, and technology work in synergy via processes and technology-based solutions that follow these seven First Principles: Flat &amp; real-time, open &amp; interoperable, proactive &amp; semi-autonomous, sustainable &amp; energy efficient, secure, scalable, orchestrated &amp; resilient. All the design principles must be considered to fully realize the vision for smart manufacturing. This paper will further explain how the seven principles work collectively to achieve new levels of connectivity, intelligence, and automation in the manufacturing ecosystem.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48168365","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":"Process prediction and detection of faults using probabilistic bidirectional recurrent neural networks on real plant data","authors":"Lucky E. Yerimah,&nbsp;Sambit Ghosh,&nbsp;Yajun Wang,&nbsp;Yanan Cao,&nbsp;Jesus Flores-Cerrillo,&nbsp;B. Wayne Bequette","doi":"10.1002/amp2.10124","DOIUrl":"10.1002/amp2.10124","url":null,"abstract":"<p>Attaining Industry 4.0 for manufacturing operations requires advanced monitoring systems and real-time data analytics of plant data, among other topics. We propose a probabilistic bidirectional recurrent network (PBRN) for industrial process monitoring for the early detection of faults. The model is based on a gated recurrent unit (GRU) neural network that allows the model to retain long-term dependencies between sensor data along a time horizon, hence learning the dynamic behavior of the process. To reduce the false-positive detection rate of the model, we compel the model to learn from a highly noisy sensor reading while outputting noise-free sensor outputs. The performance of the proposed model is compared with other data-driven statistical process monitoring schemes using real plant data from an industrial air separations unit (ASU) containing noisy sensor readings. We show that the model can learn from noisy data without reducing its performance. Using two different fault cases, we demonstrate the model's ability to carry out early fault detection with average false-positive rates of 2.9% and 4.9% for both fault cases. The missed detection rates are 0.1% and 0.2%, respectively.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48196297","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":"Continuous reactor network design for multi-product rigid polyols under uncertainty","authors":"Yunhan Wen,&nbsp;Lorenz T. Biegler,&nbsp;Maria P. Ochoa,&nbsp;John Weston,&nbsp;Nima Nikbin,&nbsp;Jeff Ferrio","doi":"10.1002/amp2.10122","DOIUrl":"10.1002/amp2.10122","url":null,"abstract":"<p>Differential-algebraic optimization models for rigid polyol production from our previous work are able to generate optimal reactor configurations along with the optimal operation recipe. However, their performance could degenerate when uncertainty comes into play. In this study, we apply a multi-scenario (MS) strategy to analyze the impact of kinetic uncertainty on reactor network design. Our approach first identifies scenarios that are worst-case violations of inequality constraints. Then, we solve the MS problem with current number of scenarios and obtain the optimal common (or first-stage) decision profiles (<i>q</i>). Next, we apply an iterative scenario sampling and updating strategy, where the uncertainty range is sampled for fixed <i>q</i> to discover constraint violations that determine the need for additional scenarios. This process continues until no further violations are detected. We adopt the MS method to three continuous reactor networks that were optimized in our previous work: CSTR in series, single differential sidestream reactor (DSR) and CSTR followed by a DSR. Among these, the DSR reactor is found to be the most economical and robust with respect to uncertainties.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47217936","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 smart manufacturing strategy for multiparametric model predictive control in air separation systems","authors":"Dustin Kenefake,&nbsp;Iosif Pappas,&nbsp;Styliani Avraamidou,&nbsp;Burcu Beykal,&nbsp;Hari S. Ganesh,&nbsp;Yanan Cao,&nbsp;Yajun Wang,&nbsp;Joannah Otashu,&nbsp;Simon Leyland,&nbsp;Jesus Flores-Cerrillo,&nbsp;Efstratios N. Pistikopoulos","doi":"10.1002/amp2.10120","DOIUrl":"10.1002/amp2.10120","url":null,"abstract":"<p>Recent trends in digitization and automation of information systems have led to the Industry 4.0 revolution in manufacturing systems. With the emergence of integrated “smart” systems that communicate through the cloud, collecting and manipulating the system data became a key yet, challenging component for developing optimal control strategies for these complex systems. In this work, we propose a strategy to address this problem with the case study on an air separation unit (ASU). Our approach involves developing an ASU's controllers via high-fidelity modeling, studies in data-driven reduced-order models, and providing implementable control policies for the high-fidelity model. Connecting the high-fidelity model to a smart manufacturing platform allows integration into other smart manufacturing tools and applications. Since the high-fidelity model is computationally challenging for online optimization tasks, such as model predictive control, surrogate models are generated that represent the high-fidelity model's behavior. The derived reduced-order models are then embedded into a model predictive control formulation for the optimal control of the whole process through multiparametric programming. A multiparametric approach based on solving a small portion of the multiparametric program is proposed to reduce the computational overhead. We then close the loop by deploying the developed controllers on the high-fidelity model for tuning with prospects of employing them on the real industrial plant.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46612869","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 signal processing-based multiple model Kalman filter (GSP-MMKF) tool for predictive analytics: An air separation unit process application","authors":"Sambit Ghosh,&nbsp;Lucky E. Yerimah,&nbsp;Yajun Wang,&nbsp;Yanan Cao,&nbsp;Jesus Flores-Cerrillo,&nbsp;B. Wayne Bequette","doi":"10.1002/amp2.10121","DOIUrl":"10.1002/amp2.10121","url":null,"abstract":"<p>The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real-time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network-scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor-network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman-filter type of state-space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43648350","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":"Stochastic parallel machine scheduling using reinforcement learning","authors":"Juxihong Julaiti,&nbsp;Seog-Chan Oh,&nbsp;Dyutimoy Das,&nbsp;Soundar Kumara","doi":"10.1002/amp2.10119","DOIUrl":"10.1002/amp2.10119","url":null,"abstract":"<p>In a high-mix and low-volume manufacturing facility, heterogeneous jobs introduce frequent reconfiguration of machines which increases the chance of unplanned machine breakdowns. As machines are often nonidentical and their performance degrades over time, it is critical to consider the heterogeneity and non-stationarity of the machines during scheduling. We propose a reinforcement learning-based framework with a novel sampling method to train the agent to schedule heterogeneous jobs on non-stationary unreliable parallel machines to minimize weighted tardiness. The results indicate that the new sampling approach expedites the learning process and the resulting policy significantly outperforms static dispatching rules.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10119","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49531942","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}