1
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Poger D, Yen L, Braet F. Big data in contemporary electron microscopy: challenges and opportunities in data transfer, compute and management. Histochem Cell Biol 2023; 160:169-192. [PMID: 37052655 PMCID: PMC10492738 DOI: 10.1007/s00418-023-02191-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/21/2023] [Indexed: 04/14/2023]
Abstract
The second decade of the twenty-first century witnessed a new challenge in the handling of microscopy data. Big data, data deluge, large data, data compliance, data analytics, data integrity, data interoperability, data retention and data lifecycle are terms that have introduced themselves to the electron microscopy sciences. This is largely attributed to the booming development of new microscopy hardware tools. As a result, large digital image files with an average size of one terabyte within one single acquisition session is not uncommon nowadays, especially in the field of cryogenic electron microscopy. This brings along numerous challenges in data transfer, compute and management. In this review, we will discuss in detail the current state of international knowledge on big data in contemporary electron microscopy and how big data can be transferred, computed and managed efficiently and sustainably. Workflows, solutions, approaches and suggestions will be provided, with the example of the latest experiences in Australia. Finally, important principles such as data integrity, data lifetime and the FAIR and CARE principles will be considered.
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Affiliation(s)
- David Poger
- Microscopy Australia, The University of Sydney, Sydney, NSW, 2006, Australia.
| | - Lisa Yen
- Microscopy Australia, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Filip Braet
- Australian Centre for Microscopy and Microanalysis, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Medical Sciences (Molecular and Cellular Biomedicine), The University of Sydney, Sydney, NSW, 2006, Australia
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2
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Tseng YJ, Chuang PJ, Appell M. When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry. ACS OMEGA 2023; 8:15854-15864. [PMID: 37179635 PMCID: PMC10173424 DOI: 10.1021/acsomega.2c07722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/07/2023] [Indexed: 05/15/2023]
Abstract
Since the first food database was released over one hundred years ago, food databases have become more diversified, including food composition databases, food flavor databases, and food chemical compound databases. These databases provide detailed information about the nutritional compositions, flavor molecules, and chemical properties of various food compounds. As artificial intelligence (AI) is becoming popular in every field, AI methods can also be applied to food industry research and molecular chemistry. Machine learning and deep learning are valuable tools for analyzing big data sources such as food databases. Studies investigating food compositions, flavors, and chemical compounds with AI concepts and learning methods have emerged in the past few years. This review illustrates several well-known food databases, focusing on their primary contents, interfaces, and other essential features. We also introduce some of the most common machine learning and deep learning methods. Furthermore, a few studies related to food databases are given as examples, demonstrating their applications in food pairing, food-drug interactions, and molecular modeling. Based on the results of these applications, it is expected that the combination of food databases and AI will play an essential role in food science and food chemistry.
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Affiliation(s)
- Yufeng Jane Tseng
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei 10617, Taiwan
- Y.J.T.:
tel, +886.2.3366.4888#529; fax, +886.2.23628167; email,
| | - Pei-Jiun Chuang
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei 10617, Taiwan
| | - Michael Appell
- USDA,
Agricultural Research Service, National Center for Agricultural Utilization
Research, Mycotoxin Prevention
and Applied Microbiology Research Unit, 1815 N. University, Peoria, Illinois. 61604, United States
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3
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Di Caprio U, Wu M, Vermeire F, Van Gerven T, Hellinckx P, Waldherr S, Kayahan E, Leblebici ME. Predicting overall mass transfer coefficients of CO2 capture into monoethanolamine in spray columns with hybrid machine learning. J CO2 UTIL 2023. [DOI: 10.1016/j.jcou.2023.102452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
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4
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Wang N, Zhang Y, Wang W, Ye Z, Chen H, Hu G, Ouyang D. How can machine learning and multiscale modeling benefit ocular drug development? Adv Drug Deliv Rev 2023; 196:114772. [PMID: 36906232 DOI: 10.1016/j.addr.2023.114772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/06/2023] [Accepted: 03/05/2023] [Indexed: 03/12/2023]
Abstract
The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.
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Affiliation(s)
- Nannan Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Yunsen Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Hongyu Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Guanghui Hu
- Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Department of Public Health and Medicinal Administration, Faculty of Health Sciences (FHS), University of Macau, Macau, China.
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5
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Rahman AABA. Successful Role of Data Science In Managing Covid-19 Battle. 2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SMART COMMUNICATION (AISC) 2023. [DOI: 10.1109/aisc56616.2023.10085065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Azrul Azlan Bin Abd Rahman
- National Defence University Malaysia,Research Fellow, Centre for Defence and International Studies (CDISS),Kuala Lumpur,57000
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6
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Hu X, Zeng J, Ma L, Wang X, Du J, Yao L, Wu Z. End-to-end dataflow engineering framework of honey manufacturing from intermediates to process by TAS1R2@AuNPs/SPCE biosensor coupled with quality transfer principle. FUNDAMENTAL RESEARCH 2022. [DOI: 10.1016/j.fmre.2022.09.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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7
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FIEMA, a system of fuzzy inference and emission analytics for sustainability-oriented chemical process design. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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8
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Vasilas N, Papadopoulos AI, Papadopoulos L, Salamanis A, Kazepidis P, Soudris D, Kehagias D, Seferlis P. Approximate computing, skeleton programming and run-time scheduling in an algorithm for process design and controllability in distributed and heterogeneous infrastructures. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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Zhu LT, Chen XZ, Ouyang B, Yan WC, Lei H, Chen Z, Luo ZH. Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01036] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Li-Tao Zhu
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Xi-Zhong Chen
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, S1 3JD, U.K
| | - Bo Ouyang
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Wei-Cheng Yan
- School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - He Lei
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zhe Chen
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zheng-Hong Luo
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
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10
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Fuentes-Cortés LF, Flores-Tlacuahuac A, Nigam KDP. Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c00335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Luis Fabián Fuentes-Cortés
- Departamento de Ingeniería Química, Tecnologico Nacional de México - Instituto Tecnológico de Celaya, Celaya, Guanajuato 38010, Mexico
| | - Antonio Flores-Tlacuahuac
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias Ave. Eugenio Garza Sada 2501, Monterrey, N.L. 64849, Mexico
| | - Krishna D. P. Nigam
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias Ave. Eugenio Garza Sada 2501, Monterrey, N.L. 64849, Mexico
- Department of Chemical Engineering, Indian Institute of Technology Delhi 600036, India
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11
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Digitalization—The Engine of Sustainability in the Energy Industry. ENERGIES 2022. [DOI: 10.3390/en15062164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The goal of this paper is to conduct a bibliometric analysis of the scientific literature about the sustainability of digitalization in the energy sector in order to capture the main challenges and trends in the transition towards it. The bibliometric analysis of the scientific literature was carried out by interrogating the Scopus database, using a set of keywords considered relevant for the analyzed field and for the goal of the proposed research. The purpose of the study was, on the one hand, the depth of the research into these topics during the 2013–2021 period, in terms of the number of scientific papers, the topic, abstract and keywords associated, the geographical area of origin and the authors’ affiliation and on the other hand, an analysis of the existence of possible links between these topics formulated through three hypotheses. The results obtained reveal the researchers’ concerns for digitalization in the energy sector, the existing correlations between the keywords analyzed and the tendencies registered in the field of digitalization in the energy industry in order to ensure higher sustainability.
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12
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Zhao L, Yang J. Batch Process Monitoring Based on Quality-Related Time-Batch 2D Evolution Information. SENSORS (BASEL, SWITZERLAND) 2022; 22:2235. [PMID: 35336405 PMCID: PMC8954576 DOI: 10.3390/s22062235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
This paper proposed a quality-related online monitoring strategy based on time and batch two-dimensional evolution information for batch processes. In the direction of time, considering the difference between each phase and the steady part and the transition part in the same phase, the change trend of the regression coefficient of the PLS model is used to divide each batch into phases, and each phase into parts. The phases, the steady parts, and the transition parts are finally distinguished and dealt with separately in the subsequent modeling process. In the batch direction, considering the slow time-varying characteristics of batch evolution, sliding windows are used to perform mode division by analyzing the evolution trend of the score matrix T in the PLS model on the base of phase division and within-phase part division. Finally, an online monitoring model that comprehensively considers the evolution information of time and batch is obtained. In a typical batch operation process, injection molding is used as an example for experimental analysis. The results show that the proposed algorithm takes advantage of mixing the time-batch two-dimensional evolution information. Compared with the traditional methods, the proposed method can overcome the shortcomings caused by the single dimension analysis and has better monitoring results.
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13
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Lim HR, Khoo KS, Chia WY, Chew KW, Ho SH, Show PL. Smart microalgae farming with internet-of-things for sustainable agriculture. Biotechnol Adv 2022; 57:107931. [PMID: 35202746 DOI: 10.1016/j.biotechadv.2022.107931] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 12/28/2021] [Accepted: 02/17/2022] [Indexed: 12/30/2022]
Abstract
Agriculture farms such as crop, aquaculture and livestock have begun the implementation of Internet of Things (IoT) and artificial intelligence (AI) technology in improving their productivity and product quality. However, microalgae farming which requires precise monitoring, controlling and predicting the growth of microalgae biomass has yet to incorporate with IoT and AI technology, as it is still in its infancy phase. Particularly, the cultivation stage of microalgae involves many essential parameters (i.e. biomass concentration, pH, light intensity, temperature and tank level) which require precise monitoring as these parameters are important to ensure an effective biomass productivity in the microalgae farming. Besides, the conventional practices in the current process equipment are still powered by electricity, thus further development by integrating IoT into these processes can ease the production process. Further to that, many researchers has studied the machine learning approach for the identification and classification of microalgae. However, there are still limited studies reported on applying machine learning for the application of microalgae industry such as optimising microalgae cultivation for higher biomass productivity. Therefore, the implementation of IoT and AI in microalgae farming can contribute to the development of the global microalgae industry. The purpose of this current review paper focuses on the overview microalgae biomass production process along with the implementation of IoT toward the future of smart farming. To bridge the gap between the conventional and microalgae smart farming, this paper also highlights the insights on the implementation phases of microalgae smart farming starting from the infant stage that involves the installation and programming of IoT hardware. Then, it is followed by the application of machine learning to predict and auto-optimise the microalgae smart farming process. Furthermore, the process setup and detailed overview of microalgae farming with the integration of IoT have been discussed critically. This review paper would provide a new vision of microalgae farming for microalgae researchers and bio-processing industries into the digitalisation industrial era.
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Affiliation(s)
- Hooi Ren Lim
- State Key Laboratory of Urban Water Resources and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia
| | - Kuan Shiong Khoo
- Faculty of Applied Sciences, UCSI University, UCSI Heights, 56000 Cheras, Kuala Lumpur, Malaysia.
| | - Wen Yi Chia
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia
| | - Kit Wayne Chew
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900 Sepang, Selangor, Malaysia; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China.
| | - Shih-Hsin Ho
- State Key Laboratory of Urban Water Resources and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China.
| | - Pau Loke Show
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia.
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14
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Affiliation(s)
- Leo H. Chiang
- Core R&D The Dow Chemical Company Lake Jackson Texas 77566 USA
| | - Birgit Braun
- Core R&D The Dow Chemical Company Lake Jackson Texas 77566 USA
| | - Zhenyu Wang
- Chemometrics, AI & Statistics The Dow Chemical Company Lake Jackson Texas 77566 USA
| | - Ivan Castillo
- Chemometrics, AI & Statistics The Dow Chemical Company Lake Jackson Texas 77566 USA
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15
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16
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Chaugule V, Wong CY, Inthavong K, Fletcher DF, Young PM, Soria J, Traini D. Combining experimental and computational techniques to understand and improve dry powder inhalers. Expert Opin Drug Deliv 2022; 19:59-73. [PMID: 34989629 DOI: 10.1080/17425247.2022.2026922] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
INTRODUCTION : Dry Powder Inhalers (DPIs) continue to be developed to deliver an expanding range of drugs to treat an ever-increasing range of medical conditions; with each drug and device combination needing a specifically designed inhaler. Fast regulatory approval is essential to be first to market, ensuring commercial profitability. AREAS COVERED : In vitro deposition, particle image velocimetry, and computational modelling using the physiological geometry and representative anatomy can be combined to give complementary information to determine the suitability of a proposed inhaler design and to optimise its formulation performance. In combination they allow the entire range of questions to be addressed cost-effectively and rapidly. EXPERT OPINION : Experimental techniques and computational methods are improving rapidly, but each needs a skilled user to maximize results obtained from these techniques. Multidisciplinary teams are therefore key to making optimal use of these methods and such qualified teams can provide enormous benefits to pharmaceutical companies to improve device efficacy and thus time to market. There is already a move to integrate the benefits of Industry 4.0 into inhaler design and usage, a trend that will accelerate.
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Affiliation(s)
- V Chaugule
- Laboratory for Turbulence Research in Aerospace and Combustion (LTRAC), Department of Mechanical and Aerospace Engineering, Monash University, Clayton Campus, Melbourne, VIC 3800, Australia
| | - C Y Wong
- Respiratory Technology, Woolcock Institute of Medical Research, Sydney, NSW 2037, Australia
| | - K Inthavong
- Mechanical and Automotive Engineering, School of Engineering, RMIT University, Bundoora, VIC 3083, Australia
| | - D F Fletcher
- School of Chemical and Biomolecular Engineering, The University of Sydney, Sydney, NSW 2006, Australia
| | - P M Young
- Respiratory Technology, Woolcock Institute of Medical Research, Sydney, NSW 2037, Australia.,Department of Marketing, Macquarie Business School, Macquarie University, NSW 2109, Australia
| | - J Soria
- Laboratory for Turbulence Research in Aerospace and Combustion (LTRAC), Department of Mechanical and Aerospace Engineering, Monash University, Clayton Campus, Melbourne, VIC 3800, Australia
| | - D Traini
- Respiratory Technology, Woolcock Institute of Medical Research, Sydney, NSW 2037, Australia.,Macquarie Medical School, Department of Biological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, NSW 2109, Australia
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17
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Raja V, Krishnamoorthy S, Moses J, Anandharamakrishnan C. ICT applications for the food industry. FUTURE FOODS 2022. [DOI: 10.1016/b978-0-323-91001-9.00001-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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18
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Sadat Lavasani M, Raeisi Ardali N, Sotudeh-Gharebagh R, Zarghami R, Abonyi J, Mostoufi N. Big data analytics opportunities for applications in process engineering. REV CHEM ENG 2021. [DOI: 10.1515/revce-2020-0054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Big data is an expression for massive data sets consisting of both structured and unstructured data that are particularly difficult to store, analyze and visualize. Big data analytics has the potential to help companies or organizations improve operations as well as disclose hidden patterns and secret correlations to make faster and intelligent decisions. This article provides useful information on this emerging and promising field for companies, industries, and researchers to gain a richer and deeper insight into advancements. Initially, an overview of big data content, key characteristics, and related topics are presented. The paper also highlights a systematic review of available big data techniques and analytics. The available big data analytics tools and platforms are categorized. Besides, this article discusses recent applications of big data in chemical industries to increase understanding and encourage its implementation in their engineering processes as much as possible. Finally, by emphasizing the adoption of big data analytics in various areas of process engineering, the aim is to provide a practical vision of big data.
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Affiliation(s)
- Mitra Sadat Lavasani
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - Nahid Raeisi Ardali
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - Rahmat Sotudeh-Gharebagh
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - Reza Zarghami
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - János Abonyi
- Department of Process Engineering , MTA – PE “Lendület” Complex Systems Monitoring Research Group, University of Pannonia , P.O. Box 158 , Veszprém , Hungary
| | - Navid Mostoufi
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
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20
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Oeing J, Neuendorf LM, Bittorf L, Krieger W, Kockmann N. Flooding Prevention in Distillation and Extraction Columns with Aid of Machine Learning Approaches. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Jonas Oeing
- TU Dortmund University Department of Biochemical and Chemical Engineering Laboratory of Equipment Design Emil-Figge-Straße 68 44227 Dortmund Germany
| | - Laura Maria Neuendorf
- TU Dortmund University Department of Biochemical and Chemical Engineering Laboratory of Equipment Design Emil-Figge-Straße 68 44227 Dortmund Germany
| | - Lukas Bittorf
- TU Dortmund University Department of Biochemical and Chemical Engineering Laboratory of Equipment Design Emil-Figge-Straße 68 44227 Dortmund Germany
| | - Waldemar Krieger
- TU Dortmund University Department of Biochemical and Chemical Engineering Laboratory of Equipment Design Emil-Figge-Straße 68 44227 Dortmund Germany
| | - Norbert Kockmann
- TU Dortmund University Department of Biochemical and Chemical Engineering Laboratory of Equipment Design Emil-Figge-Straße 68 44227 Dortmund Germany
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21
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Wang W, Ye Z, Gao H, Ouyang D. Computational pharmaceutics - A new paradigm of drug delivery. J Control Release 2021; 338:119-136. [PMID: 34418520 DOI: 10.1016/j.jconrel.2021.08.030] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 01/18/2023]
Abstract
In recent decades pharmaceutics and drug delivery have become increasingly critical in the pharmaceutical industry due to longer time, higher cost, and less productivity of new molecular entities (NMEs). However, current formulation development still relies on traditional trial-and-error experiments, which are time-consuming, costly, and unpredictable. With the exponential growth of computing capability and algorithms, in recent ten years, a new discipline named "computational pharmaceutics" integrates with big data, artificial intelligence, and multi-scale modeling techniques into pharmaceutics, which offered great potential to shift the paradigm of drug delivery. Computational pharmaceutics can provide multi-scale lenses to pharmaceutical scientists, revealing physical, chemical, mathematical, and data-driven details ranging across pre-formulation studies, formulation screening, in vivo prediction in the human body, and precision medicine in the clinic. The present paper provides a comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling. We not only summarized the theories and progress of these technologies but also discussed the regulatory requirements, current challenges, and future perspectives in the area, such as talent training and a culture change in the future pharmaceutical industry.
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Affiliation(s)
- Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Hanlu Gao
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China.
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22
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23
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Dutta D, Upreti SR. Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering. CAN J CHEM ENG 2021. [DOI: 10.1002/cjce.24246] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Debaprasad Dutta
- Department of Chemical Engineering Ryerson University Toronto Ontario Canada
| | - Simant R. Upreti
- Department of Chemical Engineering Ryerson University Toronto Ontario Canada
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24
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Rippon L, Yousef I, Hosseini B, Bouchoucha A, Beaulieu J, Prévost C, Ruel M, Shah S, Gopaluni R. Representation learning and predictive classification: Application with an electric arc furnace. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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25
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Santana VV, Martins MAF, Rodrigues AE, Loureiro JM, Ribeiro AM, Nogueira IBR. Abnormal Operation Tracking through Big-Data-Based Gram–Schmidt Orthogonalization: Production of n-Propyl Propionate in a Simulated Moving-Bed Reactor: A Case Study. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c00214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Vinicius V. Santana
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE-LCM, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- Programa de Pós-Graduação em Engenharia Industrial, Escola Politécnica, Universidade Federal da Bahia, Rua Prof. Aristides Novis, 2-Federação, 40210-630 Salvador/Bahia, Brazil
| | - Márcio A. F. Martins
- Programa de Pós-Graduação em Engenharia Industrial, Escola Politécnica, Universidade Federal da Bahia, Rua Prof. Aristides Novis, 2-Federação, 40210-630 Salvador/Bahia, Brazil
| | - Alírio E. Rodrigues
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE-LCM, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - José M. Loureiro
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE-LCM, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Ana M. Ribeiro
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE-LCM, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Idelfonso B. R. Nogueira
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE-LCM, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
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26
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Data-driven anomaly detection and diagnostics for changeover processes in biopharmaceutical drug product manufacturing. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2020.12.018] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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27
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Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends. Processes (Basel) 2021. [DOI: 10.3390/pr9030413] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis using the Gibbs Free Energy of mixing and the NRTL model, provide a good understanding with calculation issues, when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. To clarify these matters, n-octane and water blends, are good surrogates of naphtha/water mixtures. Runs were developed in a CREC vapor–liquid (VL_Cell operated with octane–water mixtures under dynamic conditions and used to establish the two-phase (liquid–vapor) and three phase (liquid–liquid–vapor) domains. Results obtained demonstrate that the two phase region (full solubility in the liquid phase) of n-octane in water at 100 °C is in the 10−4 mol fraction range, and it is larger than the 10−5 mol fraction predicted by Aspen Plus and the 10−7 mol fraction reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a machine learning (ML) technique was implemented and successfully demonstrated, in the present study.
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Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy. SENSORS 2021; 21:s21020568. [PMID: 33466895 PMCID: PMC7829822 DOI: 10.3390/s21020568] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 01/07/2021] [Accepted: 01/08/2021] [Indexed: 12/24/2022]
Abstract
The paradigm of the Internet of everything (IoE) is advancing toward enriching people’s lives by adding value to the Internet of things (IoT), with connections among people, processes, data, and things. This paper provides a survey of the literature on IoE research, highlighting concerns in terms of intelligence services and knowledge creation. The significant contributions of this study are as follows: (1) a systematic literature review of IoE taxonomies (including IoT); (2) development of a taxonomy to guide the identification of critical knowledge in IoE applications, an in-depth classification of IoE enablers (sensors and actuators); (3) validation of the defined taxonomy with 50 IoE applications; and (4) identification of issues and challenges in existing IoE applications (using the defined taxonomy) with regard to insights about knowledge processes. To the best of our knowledge, and taking into consideration the 76 other taxonomies compared, this present work represents the most comprehensive taxonomy that provides the orchestration of intelligence in network connections concerning knowledge processes, type of IoE enablers, observation characteristics, and technological capabilities in IoE applications.
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Luo L, Xie L, Su H. Robust Mixture Bayesian Latent Variable Regression with Structural Sparsity and Application to Inferential Sensing of Quality Variables. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03620] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Lin Luo
- School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, Liaoning, PR China
- State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, Zhejiang, PR China
| | - Lei Xie
- State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, Zhejiang, PR China
| | - Hongye Su
- State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, Zhejiang, PR China
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30
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Affiliation(s)
- Nikita Saxena
- Department of Chemical Engineering, Indian Institute of Technology Delhi, Delhi, India
| | - Priyanka Gupta
- Department of Chemical Engineering, Indian Institute of Technology Delhi, Delhi, India
| | - Ruchir Raman
- Department of Chemical Engineering, Indian Institute of Technology Delhi, Delhi, India
| | - Anurag S. Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, Delhi, India
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31
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Mendes PSF, Siradze S, Pirro L, Thybaut JW. Open Data in Catalysis: From Today's Big Picture to the Future of Small Data. ChemCatChem 2020. [DOI: 10.1002/cctc.202001132] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Pedro S. F. Mendes
- Laboratory for Chemical Technology Department of Materials Textiles and Chemical Engineering Ghent University Technologiepark 125 9052 Ghent Belgium
| | - Sébastien Siradze
- Laboratory for Chemical Technology Department of Materials Textiles and Chemical Engineering Ghent University Technologiepark 125 9052 Ghent Belgium
| | - Laura Pirro
- Laboratory for Chemical Technology Department of Materials Textiles and Chemical Engineering Ghent University Technologiepark 125 9052 Ghent Belgium
| | - Joris W. Thybaut
- Laboratory for Chemical Technology Department of Materials Textiles and Chemical Engineering Ghent University Technologiepark 125 9052 Ghent Belgium
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32
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Hwangbo S, Al R, Sin G. An integrated framework for plant data-driven process modeling using deep-learning with Monte-Carlo simulations. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107071] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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33
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Sun W, Braatz RD. Opportunities in tensorial data analytics for chemical and biological manufacturing processes. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107099] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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34
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Integration of data analytics with cloud services for safer process systems, application examples and implementation challenges. J Loss Prev Process Ind 2020. [DOI: 10.1016/j.jlp.2020.104316] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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35
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Franzoi RE, Menezes BC, Kelly JD, Gut JAW, Grossmann IE. Cutpoint Temperature Surrogate Modeling for Distillation Yields and Properties. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c02868] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Robert E. Franzoi
- Department of Chemical Engineering, University of São Paulo, São Paulo, Brazil
| | - Brenno C. Menezes
- Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Jeffrey D. Kelly
- Industrial Algorithms Ltd., 15 St. Andrews Road, Toronto, Canada
| | - Jorge A. W. Gut
- Department of Chemical Engineering, University of São Paulo, São Paulo, Brazil
| | - Ignacio E. Grossmann
- Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213 United States
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Abstract
With the rapid development of high technology, chemical science is not as it used to be a century ago. Many chemists acquire and utilize skills that are well beyond the traditional definition of chemistry. The digital age has transformed chemistry laboratories. One aspect of this transformation is the progressing implementation of electronics and computer science in chemistry research. In the past decade, numerous chemistry-oriented studies have benefited from the implementation of electronic modules, including microcontroller boards (MCBs), single-board computers (SBCs), professional grade control and data acquisition systems, as well as field-programmable gate arrays (FPGAs). In particular, MCBs and SBCs provide good value for money. The application areas for electronic modules in chemistry research include construction of simple detection systems based on spectrophotometry and spectrofluorometry principles, customizing laboratory devices for automation of common laboratory practices, control of reaction systems (batch- and flow-based), extraction systems, chromatographic and electrophoretic systems, microfluidic systems (classical and nonclassical), custom-built polymerase chain reaction devices, gas-phase analyte detection systems, chemical robots and drones, construction of FPGA-based imaging systems, and the Internet-of-Chemical-Things. The technology is easy to handle, and many chemists have managed to train themselves in its implementation. The only major obstacle in its implementation is probably one's imagination.
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Affiliation(s)
- Gurpur Rakesh D Prabhu
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.,Department of Applied Chemistry, National Chiao Tung University, 1001 University Road, Hsinchu, 300, Taiwan
| | - Pawel L Urban
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.,Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan
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38
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Nogueira IBR, Martins MAF, Regufe MJ, Rodrigues AE, Loureiro JM, Ribeiro AM. Big Data-Based Optimization of a Pressure Swing Adsorption Unit for Syngas Purification: On Mapping Uncertainties from a Metaheuristic Technique. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01155] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Idelfonso B. R. Nogueira
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE/LCM Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Márcio A. F. Martins
- Departamento de Engenharia Química, Escola Politécnica (Polytechnic School), Universidade Federal da Bahia, R. Prof. Aristides Novis, 2, Federação, 40210-630 Salvador, BA, Brazil
| | - Maria João Regufe
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE/LCM Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Alírio E. Rodrigues
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE/LCM Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - José M. Loureiro
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE/LCM Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Ana M. Ribeiro
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE/LCM Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
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McBride K, Sanchez Medina EI, Sundmacher K. Hybrid Semi‐parametric Modeling in Separation Processes: A Review. CHEM-ING-TECH 2020. [DOI: 10.1002/cite.202000025] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Kevin McBride
- Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstraße 1 39106 Magdeburg Germany
| | - Edgar Ivan Sanchez Medina
- Otto-von-Guericke University Magdeburg Chair for Process Systems Engineering Universitätsplatz 2 39106 Magdeburg Germany
| | - Kai Sundmacher
- Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstraße 1 39106 Magdeburg Germany
- Otto-von-Guericke University Magdeburg Chair for Process Systems Engineering Universitätsplatz 2 39106 Magdeburg Germany
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40
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Kumar A, Bhattacharya A, Flores-Cerrillo J. Data-driven process monitoring and fault analysis of reformer units in hydrogen plants: Industrial application and perspectives. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106756] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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41
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Katz J, Pappas I, Avraamidou S, Pistikopoulos EN. Integrating deep learning models and multiparametric programming. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106801] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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42
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Milman BL, Zhurkovich IK. Big Data in Modern Chemical Analysis. JOURNAL OF ANALYTICAL CHEMISTRY 2020. [DOI: 10.1134/s1061934820020124] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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43
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Fahey W, Jeffers P, Carroll P. A business analytics approach to augment six sigma problem solving: A biopharmaceutical manufacturing case study. COMPUT IND 2020. [DOI: 10.1016/j.compind.2019.103153] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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44
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Tao D, Yang P, Feng H. Utilization of text mining as a big data analysis tool for food science and nutrition. Compr Rev Food Sci Food Saf 2020; 19:875-894. [PMID: 33325182 DOI: 10.1111/1541-4337.12540] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 12/26/2019] [Accepted: 01/13/2020] [Indexed: 12/21/2022]
Abstract
Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.
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Affiliation(s)
- Dandan Tao
- Department of Food Science and Human Nutrition, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Pengkun Yang
- Department of Electrical Engineering, Princeton University, Princeton, New Jersey
| | - Hao Feng
- Department of Food Science and Human Nutrition, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois
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45
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A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring. Processes (Basel) 2019. [DOI: 10.3390/pr8010024] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.
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Abstract
With the increasing availability of large amounts of data, methods that fall under the term data science are becoming important assets for chemical engineers to use. Methods, broadly speaking, are needed to carry out three tasks, namely data management, statistical and machine learning and data visualization. While claims have been made that data science is essentially statistics, consideration of the three tasks previously mentioned make it clear that it is really broader than just statistics alone and furthermore, statistical methods from a data-poor era are likely insufficient. While there have been many successful applications of data science methodologies, there are still many challenges that must be addressed. For example, just because a dataset is large, does not necessarily mean it is meaningful or information rich. From an organizational point of view, a lack of domain knowledge and a lack of a trained workforce among other issues are cited as barriers for the successful implementation of data science within an organization. Many of the methodologies employed in data science are familiar to chemical engineers; however, it is generally the case that not all the methods required to carry out data science projects are covered in an undergraduate chemical engineering program. One option to address this is to adjust the curriculum by modifying existing courses and introducing electives. Other examples include the introduction of a data science minor or a postgraduate certificate or a Master’s program in data science.
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Patwardhan RS, Hamadah HA, Patel KM, Hafiz RH, Al-Gwaiz MM. Applications of Advanced Analytics at Saudi Aramco: A Practitioners’ Perspective. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b06205] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Rohit S. Patwardhan
- Process & Control Systems Department, Saudi Aramco, Dhahran 31311, Saudi Arabia
| | - Hamza A. Hamadah
- Process & Control Systems Department, Saudi Aramco, Dhahran 31311, Saudi Arabia
| | - Kalpesh M. Patel
- Process & Control Systems Department, Saudi Aramco, Dhahran 31311, Saudi Arabia
| | - Rayan H. Hafiz
- Process & Control Systems Department, Saudi Aramco, Dhahran 31311, Saudi Arabia
| | - Majid M. Al-Gwaiz
- Process & Control Systems Department, Saudi Aramco, Dhahran 31311, Saudi Arabia
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48
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49
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Improving the sensitivity of safety and health index assessment in optimal molecular design framework. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2018.12.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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50
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Identification of flow regime in a bubble column reactor with a combination of optical probe data and machine learning technique. CHEMICAL ENGINEERING SCIENCE: X 2019. [DOI: 10.1016/j.cesx.2019.100023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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