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Journal articles on the topic 'Online process control'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Govindhasamy, James J., Seán F. McLoone, George W. Irwin, John J. French, and Richard P. Doyle. "Reinforcement Learning for Online Industrial Process Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 1 (2005): 23–30. http://dx.doi.org/10.20965/jaciii.2005.p0023.

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Reinforcement learning, in the form of Adaptive Critic Designs (ACDs), have the ability to analyse or evaluate a situation and respond to it accordingly. They offer an excellent alternative for adaptively controlling and optimising the highly nonlinear processes found in industry. Here, an enhanced implementation of the action dependent adaptive critic design (ADAC) of Si and Wang [9] is investigated for modelling and control of an industrial grinding process used in the manufacture of hard disk drive platters. This study, one of the first reported industrial applications of this emerging technology, shows that the proposed ADAC control scheme can achieve a 33% reduction in platter rejects compared to an existing proprietary controller.

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2

Kalúz, Martin, L'uboš Čirka, and Miroslav Fikar. "Advances in Online Courses on Process Control." IFAC Proceedings Volumes 45, no. 11 (2012): 235–40. http://dx.doi.org/10.3182/20120619-3-ru-2024.00029.

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3

Parlos, A. G., Sanjay Parthasarathy, and A. F. Atiya. "Neuro-predictive process control using online controller adaptation." IEEE Transactions on Control Systems Technology 9, no. 5 (2001): 741–55. http://dx.doi.org/10.1109/87.944469.

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4

Johnston, Stephen P., David O. Kazmer, and Robert X. Gao. "Online simulation-based process control for injection molding." Polymer Engineering & Science 49, no. 12 (2009): 2482–91. http://dx.doi.org/10.1002/pen.21481.

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5

Cheng, Yuan, and Mohsen A. Jafari. "Vision-Based Online Process Control in Manufacturing Applications." IEEE Transactions on Automation Science and Engineering 5, no. 1 (2008): 140–53. http://dx.doi.org/10.1109/tase.2007.912058.

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6

Na, Wenbo, Quanmin Zhu, Zhiwei Su, and Qingfeng Jiang. "Optimisation control and online monitoring for cleaning process." International Journal of Modelling, Identification and Control 23, no. 2 (2015): 173. http://dx.doi.org/10.1504/ijmic.2015.068876.

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7

Sunusi, Idris Idris, Jun Zhou, Zhen Zhen Wang, et al. "Intelligent tractors: Review of online traction control process." Computers and Electronics in Agriculture 170 (March 2020): 105176. http://dx.doi.org/10.1016/j.compag.2019.105176.

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8

Amrit, Rishi, James B. Rawlings, and Lorenz T. Biegler. "Optimizing process economics online using model predictive control." Computers & Chemical Engineering 58 (November 2013): 334–43. http://dx.doi.org/10.1016/j.compchemeng.2013.07.015.

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9

Michaeli, Walter, and Andreas Schreiber. "Online control of the injection molding process based on process variables." Advances in Polymer Technology 28, no. 2 (2009): 65–76. http://dx.doi.org/10.1002/adv.20153.

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10

Won, Wangyun, Kwang Soon Lee, Seokho Lee, and Chansul Jung. "Repetitive Control and Online Optimization of Catofin Propane Process." IFAC Proceedings Volumes 42, no. 11 (2009): 273–78. http://dx.doi.org/10.3182/20090712-4-tr-2008.00042.

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11

Hansen, Aren, Rion Merlo, Seppi Henneman, Bob Witzgall, Steve Ramberg, and William Yu. "Testing Online Analyzers for Biological Nutrient Removal Process Control." Proceedings of the Water Environment Federation 2014, no. 9 (2014): 594–618. http://dx.doi.org/10.2175/193864714815941388.

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12

Zhu, Y. Q., and Xiang Yu Li. "A Service Oriented Product Development Process Online Control Model." Advanced Materials Research 1049-1050 (October 2014): 1090–93. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1090.

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For implementing the online control of product development process in Micro-Plant, an integration control platform is proposed based on web service technology. Professional software such as G2, GAMS, Matlab, are wrapped as web services based on multi-agent technology, and the web service named Data_Service is given to implement the data communication among different web services, then we can control and optimize the product development process in Micro-Plant. Finally the practicability and validity of the integration control platform are verified through the application in the product development of Nipagin ester.

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13

Bobzin, K., T. Brögelmann, and N. C. Kruppe. "Enhanced PVD process control by online substrate temperature measurement." Surface and Coatings Technology 354 (November 2018): 383–89. http://dx.doi.org/10.1016/j.surfcoat.2018.07.096.

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14

Uhlmann, Eckart, Tobias Holznagel, and Raheel Masood Alavi. "Online process control and self-configuration of turning operations." Procedia CIRP 88 (2020): 294–99. http://dx.doi.org/10.1016/j.procir.2020.05.051.

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15

Srour, M. H., Vincent G. Gomes, I. S. Altarawneh, and J. A. Romagnoli. "Online model-based control of an emulsion terpolymerisation process." Chemical Engineering Science 64, no. 9 (2009): 2076–87. http://dx.doi.org/10.1016/j.ces.2009.01.044.

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16

Won, Wangyun, Kwang Soon Lee, Seokho Lee, and Chansul Jung. "Repetitive control and online optimization of Catofin propane process." Computers & Chemical Engineering 34, no. 4 (2010): 508–17. http://dx.doi.org/10.1016/j.compchemeng.2009.12.011.

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17

Ahmed, Bulbul. "Process Performance Optimization through Advanced Automatic Control and Online Monitoring." Proceedings of the Water Environment Federation 2016, no. 5 (2016): 32–38. http://dx.doi.org/10.2175/193864716819715527.

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18

Zimmer, Martin, Antje Oltersdorf, and Jochen Rentsch. "Online process control of acidic texturisation baths with ion chromatography." Talanta 80, no. 2 (2009): 499–503. http://dx.doi.org/10.1016/j.talanta.2009.07.019.

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19

Govindhasamy, J. J., S. F. McLoone, and G. W. Irwin. "Second-Order Training of Adaptive Critics for Online Process Control." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 35, no. 2 (2005): 381–85. http://dx.doi.org/10.1109/tsmcb.2004.843276.

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20

Mansour, S. E., G. C. Kember, R. Dubay, and B. Robertson. "Online optimization of fuzzy-PID control of a thermal process." ISA Transactions 44, no. 2 (2005): 305–14. http://dx.doi.org/10.1016/s0019-0578(07)60183-9.

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21

Qin, Yan, Chunhui Zhao, and Furong Gao. "Multi-stage Process Analysis and Modelling based Online Monitoring for Chip Packaging Process." IFAC-PapersOnLine 48, no. 28 (2015): 993–98. http://dx.doi.org/10.1016/j.ifacol.2015.12.260.

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22

Louzada, Francisco, Paulo Ferreira, Anderson Ara, and Caroline Godoy. "CEP ONLINE: A WEB-ORIENTED EXPERT SYSTEM FOR STATISTICAL PROCESS CONTROL." Pesquisa Operacional 39, no. 1 (2019): 177–204. http://dx.doi.org/10.1590/0101-7438.2019.039.01.0177.

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23

Zimmer, Martin, Katrin Birmann, and Jochen Rentsch. "Online process control of alkaline texturing baths with near-infrared spectroscopy." Vibrational Spectroscopy 53, no. 2 (2010): 269–73. http://dx.doi.org/10.1016/j.vibspec.2010.04.005.

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24

Drevermann, A., C. Pickmann, R. Tiefers, and G. Zimmermann. "Online process control for directional solidification by ultrasonic pulse echo technique." Ultrasonics 42, no. 1-9 (2004): 105–8. http://dx.doi.org/10.1016/j.ultras.2004.01.010.

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25

Hongya, Fu, Li Maoyue, Liu Yuan, and Han Zhenyu. "Study on Online Intelligent Control of Cutting Vibration in Milling Process." Noise & Vibration Worldwide 42, no. 10 (2011): 75–80. http://dx.doi.org/10.1260/0957-4565.42.10.75.

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To realize online and real-time suppressing vibration amplitude in milling, an open architecture intelligent controller is designed and developed, which is modular and can achieve machining parameters' online adjustment based on acceleration constraint condition. According to the idea of the velocity control, a spindle speed control model is constructed. This controller can realize both the right codes translation and assume the effective coordination and synchronization among the thread of adaptive control, interface display and interpolation and so on. A series of fuzzy control rules have been designed to reduce the vibration amplitude about stepped-weir work-piece, and some actual experiments based on acceleration constraints have been also completed. The results show the open architecture variable spindle speed controller can be used in the practical machining, and the spindle speed can realize online real-time adjustment based on the acceleration constraint condition. All these technologies can effectively suppress the cutting vibration, which protect the machine and work-piece tool system.

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26

Trung, Thanh, and Tomohide Mogami. "Advanced Online Process Analyzer for Chemical Recovery and Pulp Mill Control." JAPAN TAPPI JOURNAL 74, no. 1 (2020): 57–66. http://dx.doi.org/10.2524/jtappij.74.57.

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27

Rusin, Andrzej, Grzegorz Nowak, and Marian Lipka. "Practical Algorithms for Online Thermal Stress Calculations and Heating Process Control." Journal of Thermal Stresses 37, no. 11 (2014): 1286–301. http://dx.doi.org/10.1080/01495739.2014.937219.

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28

Gupta, Abhijit, and Sukalyan Sengupta. "Online Control Charts for Process Averages Based on Repeated Median Filters." Communications in Statistics - Simulation and Computation 37, no. 1 (2007): 178–202. http://dx.doi.org/10.1080/03610910701724029.

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29

Klocke, Fritz, Stephan Kratz, Thomas Auerbach, Sascha Gierlings, Guido Wirtz, and Dražen Veselovac. "Process Monitoring and Control of Machining Operations." International Journal of Automation Technology 5, no. 3 (2011): 403–11. http://dx.doi.org/10.20965/ijat.2011.p0403.

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In today’s production, the overall technical aim of a monitoring and control system is to introduce robust manufacturing with maximum performance. Generating targeted values means to transfer production know-how to the application of monitoring and control strategies, whereas such systems include a decisive potential for increasing added value in production companies. Especially in complex processes, important operational sequences should be analyzed and optimized online by means of system analysis and control. This paper reviews certain aspects and introduces recent investigations on process monitoring and control strategies for milling, drilling and broaching operations.

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30

Kopčeková, Alena, Michal Kopček, and Pavol Tanuška. "BUSINESS INTELLIGENCE IN PROCESS CONTROL." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 21, no. 33 (2013): 43–53. http://dx.doi.org/10.2478/rput-2013-0039.

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Abstract The Business Intelligence technology, which represents a strong tool not only for decision making support, but also has a big potential in other fields of application, is discussed in this paper. Necessary fundamental definitions are offered and explained to better understand the basic principles and the role of this technology for company management. Article is logically divided into five main parts. In the first part, there is the definition of the technology and the list of main advantages. In the second part, an overview of the system architecture with the brief description of separate building blocks is presented. Also, the hierarchical nature of the system architecture is shown. The technology life cycle consisting of four steps, which are mutually interconnected into a ring, is described in the third part. In the fourth part, analytical methods incorporated in the online analytical processing and data mining used within the business intelligence as well as the related data mining methodologies are summarised. Also, some typical applications of the above-mentioned particular methods are introduced. In the final part, a proposal of the knowledge discovery system for hierarchical process control is outlined. The focus of this paper is to provide a comprehensive view and to familiarize the reader with the Business Intelligence technology and its utilisation.

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31

Ma, Xi, Jinqiu Hu, and Laibin Zhang. "EMD-based online Filtering of Process Data." Control Engineering Practice 62 (May 2017): 79–91. http://dx.doi.org/10.1016/j.conengprac.2017.03.008.

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32

Zhang, Xiao Zhong, Fan Qin Meng, and Jie Hui Wang. "Optimizing Control Methods of Airport Pipeline Refueling Process." Applied Mechanics and Materials 738-739 (March 2015): 1007–11. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.1007.

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Due to the non-linearity and long delay of airport pipe line refueling system, it is difficult for classical control methods to ensure stable pumping pressure and refueling flow. To improve the stability of the refueling process, the paper introduces human-simulated intelligent control (HSIC) method to the recognization of refueling status, online self-adjusting and control of refueling parameters. Simulation shows that the stability and accuracy of HSIC control methods were better than that of classical PID and fuzzy PID control.

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33

KOIKE, Masayoshi, Ken-ichi TANAKA, Takashi KAMOSHITA, and Hiroshi YANO. "Designing of online-measurement control system in plastic injection molding process. (1)." Journal of the Japan Society for Precision Engineering 54, no. 1 (1988): 164–69. http://dx.doi.org/10.2493/jjspe.54.164.

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34

Sælid, Steinar. "A Truly Distributed Process Control System with Online Configuration and Expansion Capabilities." Modeling, Identification and Control: A Norwegian Research Bulletin 8, no. 4 (1987): 201–22. http://dx.doi.org/10.4173/mic.1987.4.2.

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35

Krishnamoorthy, Dinesh, and Sigurd Skogestad. "Online Process Optimization with Active Constraint Set Changes using Simple Control Structures." Industrial & Engineering Chemistry Research 58, no. 30 (2019): 13555–67. http://dx.doi.org/10.1021/acs.iecr.9b00308.

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36

Scott, Leonard L., Youzhen Ding, Sloane M. Stalder, Paul A. Kohl, Jack Winnick, and Lawrence A. Bottomley. "Electrosynthesis of Sodium Hydrosulfite: I. Development of an Online Process Control Monitor." Journal of The Electrochemical Society 145, no. 12 (1998): 4052–56. http://dx.doi.org/10.1149/1.1838913.

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37

Heller,, B., S. Chatti,, N. Ridane,, and M. Kleiner,. "Online-Process Control of Air Bending for Thin and Thick Sheet Metal." Journal of the Mechanical Behavior of Materials 15, no. 6 (2004): 455–62. http://dx.doi.org/10.1515/jmbm.2004.15.6.455.

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38

Zimmer, Martin, Katrin Krieg, and Jochen Rentsch. "Online Process Control of Alkaline Texturing Baths: Determination of the Chemical Concentrations." American Journal of Analytical Chemistry 05, no. 03 (2014): 205–10. http://dx.doi.org/10.4236/ajac.2014.53025.

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39

Karlsson, Hakan, and Yoshihiro Ohkawa. "Advanced Control of the Pulping Process by Using New Online Fibre Analysers." JAPAN TAPPI JOURNAL 62, no. 7 (2008): 780–86. http://dx.doi.org/10.2524/jtappij.62.780.

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40

Gauri, Susanta Kumar. "Globally applicable control chart for online monitoring of stability of process mean." Journal of Statistical Computation and Simulation 81, no. 12 (2011): 1847–69. http://dx.doi.org/10.1080/00949655.2010.507204.

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41

JIN, JIONGHUA, and YU DING. "Online automatic process control using observable noise factors for discrete-part manufacturing." IIE Transactions 36, no. 9 (2004): 899–911. http://dx.doi.org/10.1080/07408170490473105.

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42

Zhang, Lihui, Helei Cui, Hongli Li, Feng Han, Yaqiu Zhang, and Wenfu Wu. "Parameters Online Detection and Model Predictive Control during the Grain Drying Process." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/924698.

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In order to improve the grain drying quality and automation level, combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us, the temperature, moisture, and other parameters measuring sensors were placed on the dryer, to achieve online automatic detection of process parameters during the grain drying process. A drying model predictive control system was set up. A grain dry predictive control model at constant velocity and variable temperature was established, in which the entire process was dried at constant velocity (i.e., precipitation rate per hour is a constant) and variable temperature. Combining PC with PLC, and based on LabVIEW, a system control platform was designed.

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43

Krättli, M., F. Steinebach, and M. Morbidelli. "Online control of the twin-column countercurrent solvent gradient process for biochromatography." Journal of Chromatography A 1293 (June 2013): 51–59. http://dx.doi.org/10.1016/j.chroma.2013.03.069.

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44

Zhou, Jing, Yingguang Li, Di Li, and Youyi Wen. "Online learning based intelligent temperature control during polymer composites microwave curing process." Chemical Engineering Journal 370 (August 2019): 455–65. http://dx.doi.org/10.1016/j.cej.2019.03.204.

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45

Farsi, Hamed, Ali Fanian, and Zahra Taghiyarrenani. "A novel online state-based anomaly detection system for process control networks." International Journal of Critical Infrastructure Protection 27 (December 2019): 100323. http://dx.doi.org/10.1016/j.ijcip.2019.100323.

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46

Rasmussen, Emil D. J., Massimiliano Errico, and Stefania Tronci. "Adaptive Feedback Control for a Pasteurization Process." Processes 8, no. 8 (2020): 930. http://dx.doi.org/10.3390/pr8080930.

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The milk pasteurization process is nonlinear in nature, and for this reason, the application of linear control algorithms does not guarantee the obtainment of the required performance in every condition. The problem is here addressed by proposing an adaptive algorithm, which was obtained by starting from an observer-based control approach. The main result is the obtainment of a simple PI-like controller structure, where the control parameters depend on the state of the system and are adapted online. The proposed algorithm was designed and applied on a simulated process, where the temperature dependence of the milk’s physical properties was considered. The control strategy was tested by simulating different situations, particularly when time-varying disturbances entered the system. The use of the adaptive rule reduces the variance generally introduced by the PI or PID controller.

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47

Berntorp, Karl. "Online Bayesian Tire-Friction Learning by Gaussian-Process State-Space Models." IFAC-PapersOnLine 53, no. 2 (2020): 13939–44. http://dx.doi.org/10.1016/j.ifacol.2020.12.910.

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48

Kim, Ellen EunKyoo Kyoo, and Chung Hun Lee. "How do consumers process online hotel reviews?" Journal of Hospitality and Tourism Technology 6, no. 2 (2015): 113–26. http://dx.doi.org/10.1108/jhtt-09-2014-0045.

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Purpose – The purpose of this paper is to examine how consensus and sequence of electronic word-of-mouth (eWOM) presented on online hotel review Web sites affect consumers’ attitudes toward the company and intention to stay at a hotel. Design/methodology/approach – This experiment used a 2 (consensus: high/low) × 3 (sequence: positive-negative, neutral, negative-positive) between-subjects design. A total of 165 usable data samples were gathered. Both consensus and sequence were manipulated. Findings – The study revealed that the review consensus overrides the impact of the review sequence such that when review ratings are substantially consistent, consumers’ attitudes and intentions to stay at a hotel are not influenced by the sequence of reviews. Research limitations/implications – Other variables such as prior experience with the hotel or biases toward the hotel can affect consumer reactions to such online reviews. Future studies need to reflect on such variables that can moderate or mediate the impact of eWOM consensus and sequence. Practical implications – Our findings suggest that the online consumer review summary information should be used to control the customer message process and when consumer reviews conflict, managers should take note of the sequence in which consumers read the reviews. Originality/value – This paper adds to the body of scholarly research related to consumer information processing and further demonstrates how individuals integrate opinions from several reviews, especially in the online context.

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49

Stark, W., and Ch Kürten. "Online Control for the Processing of Urea Resin Moulding Compounds." International Polymer Science and Technology 36, no. 7 (2009): 1–5. http://dx.doi.org/10.1177/0307174x0903600701.

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Urea resin moulding compounds belong to the class of thermosetting materials. They undergo a chemical crosslinking reaction in the mould, giving them outstanding heat resistance. In contrast to thermoplastic materials, thermosets need a critical cure time before the glass transition temperature shifts sufficiently for the hot parts to be stable enough to be demoulded. The cycle times are therefore significantly longer. One possible means of reducing the cycle time would be to increase the mould temperature. As is generally known, increasing the temperature by 10 K doubles the reaction rate. However, a faster reaction rate reduces the time window available for demoulding the cured parts, and the whole process becomes more vulnerable to variations in process parameters and compound composition. By the use of an online cure monitoring system it should be possible to control the process even at higher mould temperatures. In this article the dielectric cure monitoring method is tested in the manufacture of injection moulded parts and its potential for process control is discussed.

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50

Shi, Dongfeng, and Nabil N. Gindy. "Industrial Applications of Online Machining Process Monitoring System." IEEE/ASME Transactions on Mechatronics 12, no. 5 (2007): 561–64. http://dx.doi.org/10.1109/tmech.2007.902131.

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