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Journal articles on the topic 'Process Control and Simulation'

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Author: Grafiati

Published: 4 June 2021

Last updated: 28 January 2023

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1

Golten, J. W. "Process Control Simulation Package ‘PCS’." International Journal of Electrical Engineering Education 22, no. 3 (September 1985): 205–12. http://dx.doi.org/10.1177/002072098502200304.

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2

Tyler, D. W., and A. K. Quibell. "Computer simulation in process control." Mathematics and Computers in Simulation 27, no. 2-3 (April 1985): 259–66. http://dx.doi.org/10.1016/0378-4754(85)90047-3.

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3

MATSUMURA, Takashi, and Jurgen LEOPOLD. "D18 Simulation of Drilling Process for Control of Burr Formation(Analytical advancement of machining process)." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2009.5 (2009): 489–94. http://dx.doi.org/10.1299/jsmelem.2009.5.489.

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4

Pedersen, J. "Controlling Activated Sludge Process Using EFOR." Water Science and Technology 26, no. 3-4 (August 1, 1992): 783–90. http://dx.doi.org/10.2166/wst.1992.0459.

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A newly developed simulation program, based on the Activated Sludge Model No. 1, has been investigated for its controlling abilities. The program is capable of simulating most of the control types which have been applied to wastewater treatment plants. The program was tested on a nitrifying and a denitrifying treatment plant. The results showed that the model makes good simulations of the applied controls.

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5

Gostev, Ivan M., and Pavel Е. Golosov. "SIMULATION MODEL OF PROCESS SPORADIC CONTROL." International Journal of Manufacturing Economics and Management 2, no. 1 (June 20, 2022): 16–22. http://dx.doi.org/10.54684/ijmem.2022.2.1.16.

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The control solutions for modern production with a large number of processes performed simultaneously require equal or more computing power than, for example, calculating hashes for bitcoin. Hereinafter the reference is made to the problem of maximizing the production of the quantity of some mechanical units, which include several types of details required to assemble the unit in a certain quantity. The details are assumed to be manufactured and delivered to the assembly shop in a certain set. It is required to ensure the production of details on a specified number of machines for the assembly of maximum units for a fixed time. Any detail is expected to be produced on any machine. The same details can be produced in parallel. The optimization parameters will be provided by number of details of the required nomenclature, as produced with limited machine support used for the time-constraint production of these details. The production of a unit results from the available details of required nomenclature and its assembly from such details. We consider the production simulation model focused on maximizing the output of assemblies and increasing the efficiency of the use of available equipment. To solve the problem, we use the sporadic control mechanism for the number of manufactured details.

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6

Bui, R. T., and R. Ouellet. "Optimal Process Control Through Computer Simulation." SIMULATION 60, no. 3 (March 1993): 151–64. http://dx.doi.org/10.1177/003754979306000302.

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7

Garner, K. C., N. J. Peberdy, and C. N. Moreton. "Process and process control design using dynamic flowsheet simulation." Mining, Metallurgy & Exploration 3, no. 1 (February 1986): 41–45. http://dx.doi.org/10.1007/bf03402634.

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8

., M. Shaharurrizal B. M. W., Farrah D. Herman ., A. Arunagiri ., and Stella Morris . "Fuzzy Logic Simulation to Process Control Systems." Information Technology Journal 1, no. 3 (March 1, 2002): 272–79. http://dx.doi.org/10.3923/itj.2002.272.279.

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9

Ponton, J. W., and R. McKinnel. "Nonlinear process simulation and control using transputers." IEE Proceedings D Control Theory and Applications 137, no. 4 (1990): 189. http://dx.doi.org/10.1049/ip-d.1990.0024.

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10

Davidson, J., and J. L. Houle. "Simulation of hierarchical process control computer systems." IFAC Proceedings Volumes 18, no. 1 (May 1985): 91–103. http://dx.doi.org/10.1016/b978-0-08-031664-2.50020-6.

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11

Şipoș, Anca, and Mariana Liliana Păcală. "Teaching process control in food engineering: dynamic simulation of a fermentation control process." Balkan Region Conference on Engineering and Business Education 2, no. 1 (December 20, 2017): 313–19. http://dx.doi.org/10.1515/cplbu-2017-0041.

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Abstract Students usually have difficulties to understand abstract concepts of process control. Implementing in teaching process the inquiry-based learning helps students to follow methods and practices similar to those of professional scientists in order to construct knowledge. The paper describes the steps reached in simulation-based learning: from experimental data obtained by the students in their practical method (study and measurement of variables to some fermentation processes) to the simulated the behaviour of the process under a feedback control system. By providing opportunities for students to check their understanding and reflect on their learning process performance is enhanced over a traditional lecture course.

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12

Okolnishnikov, Victor, Sergey Rudometov, and Sergey Zhuravlev. "Simulation Environment for Development of Automated Process Control System in Coal Mining." International Journal of Energy 15 (November 21, 2021): 98–101. http://dx.doi.org/10.46300/91010.2021.15.15.

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This paper describes a visual interactive simulation system of technological processes intended for the development and execution of simulation and emulation models for automated process control systems in coal mining. A set of simulation models of various subsystems of a mine was developed with the help of this simulation system. These models united to create simulation environment. Simulation environment is visually interactive, include emulation models of technological equipment and allow simulating complex situations in mines and working faces. Simulation environment was used for testing of control programs executed in programmable logic controllers.

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13

Marquardt, W., P. Holl, and E. D. Gilles. "Dynamic Process Flowsheet Simulation - An Important Tool in Process Control." IFAC Proceedings Volumes 20, no. 5 (July 1987): 369–74. http://dx.doi.org/10.1016/s1474-6670(17)55465-8.

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14

Scholz, Marco, Michael Alders, Jonas Lölsberg, and Matthias Wessling. "Dynamic process simulation and process control of biogas permeation processes." Journal of Membrane Science 484 (June 2015): 107–18. http://dx.doi.org/10.1016/j.memsci.2015.03.008.

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15

Yao, Ming Hai, Xiao Ji Chen, and Lei Zuo. "Time Step Method of Computer Simulation Process Control." Applied Mechanics and Materials 494-495 (February 2014): 1257–61. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1257.

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With the development of information technology, computer simulation technology has become a necessary mean for many complex systems analysis, design, testing, evaluation and other work. Time step method to carry on the research is applied in this paper according to the situation of simulation on time process control complex for computer. First, time step method mechanism describes by graphics; Then, we can research on the simulation of process control procedure, including the system's entity scan, all kinds of events scan, entities and events to combine scan; Finally, we can study on the same time step method that usually used on the computer simulation of process control. Content of this study provide technical and methodological support for computer simulation. With the deepening understanding for computer simulation technology, computer simulation of process control based on the time step method is bound to get a wide range of applications.

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16

Lis, Stanisław, Marcin Tomasik, Sławomir Kurpaska, Jarosław Knaga, and Piotr Łyszczarz. "Analysis of the bioethanol production process control." E3S Web of Conferences 154 (2020): 02009. http://dx.doi.org/10.1051/e3sconf/202015402009.

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The article presents the analysis of the automatic control of the bioethanol production process intended for biofuel. It presents the formulated general concept of the system and the method of designing a closed control system based on the iterative prototyping procedure. The modeling and the simulation were carried out in the Matlab®-Simulink environment. The simulation model of the object was developed based on the experimentally registered characteristics. It has been adjusted, i.e. the compatibility of its behavior with the object it reproduces has been confirmed. Based on the tuned model of the object, a control system model was created, which was the basis for computer simulation which enabled the control algorithm parameters to be established. The final verification of the correct operation of the system was performed with the use of hardware simulation. It was based on entering a negative feedback loop of the virtual control system of the real object elements into the loop. The results of the simulation confirmed the correctness of the adopted design.

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17

Li, Xue Tong, Zhao Meng Huang, Min Ting Wang, and Feng Shan Du. "Research and Design of Polynomial Short Stroke Control Curve for Roughing Process." Applied Mechanics and Materials 182-183 (June 2012): 1508–12. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1508.

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A numerical simulation model for vertical-horizontal rolling process in roughing trains is built according to 3-D rigid-plastic finite element method (FEM), which is validated by comparing the simulation results with measured ones of industrial production. The principle of width deviation appearing on head and tail of slab is investigated by the finite element (FE) simulations. A new polynomial short stroke control (SSC) model is developed based on the comprehensive simulation results and the analyzed results show that, at the aspect of width control, the new SSC model is more effective than the two line one.

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18

MORIMOTO, Tetsuo, Wahyu PURWANTO, and Yasushi HASHIMOTO. "Intelligent Control Simulation for Optimization of Storage Process." Shokubutsu Kojo Gakkaishi 7, no. 2 (1995): 91–96. http://dx.doi.org/10.2525/jshita.7.91.

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19

Xu, Likai, and ZhiFeng Wang. "Virtual simulation of EFPT-D process control experiment." Journal of Physics: Conference Series 2010, no. 1 (September 1, 2021): 012174. http://dx.doi.org/10.1088/1742-6596/2010/1/012174.

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20

Barrows, Elina, Katherine Martin, and Thérèse Smith. "Markup language for chemical process control and simulation." Computers & Chemical Engineering 160 (April 2022): 107702. http://dx.doi.org/10.1016/j.compchemeng.2022.107702.

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21

Zou, Yisheng, Guofu Ding, Weihua Zhang, Jian Zhang, Shengfeng Qin, and John Kian Tan. "Collaborative simulation method with spatiotemporal synchronization process control." Chinese Journal of Mechanical Engineering 29, no. 6 (October 24, 2016): 1074–82. http://dx.doi.org/10.3901/cjme.2016.0805.088.

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22

ZHU, Ming. "Simulation and Control of Consumable DE-GMAW Process." Journal of Mechanical Engineering 48, no. 10 (2012): 45. http://dx.doi.org/10.3901/jme.2012.10.045.

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23

Dagersten, N., M. I. Heywood, and C. R. Chatwin. "Batch process control using QFD matrices and simulation." Production Planning & Control 9, no. 4 (January 1998): 335–48. http://dx.doi.org/10.1080/095372898234064.

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24

Wood, A. "Control-volume simulation of the electrofusion welding process." IMA Journal of Management Mathematics 9, no. 1 (January 1, 1998): 65–88. http://dx.doi.org/10.1093/imaman/9.1.65.

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25

Kluska-Nawarecka, Stanislawa, Henryk Polcik, and Malgorzata Warmuzek. "Simulation and Control of Metal Alloys Solidification Process." IFAC Proceedings Volumes 30, no. 19 (September 1997): 523–27. http://dx.doi.org/10.1016/s1474-6670(17)42352-4.

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26

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

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27

Petrovic, S., P. Milosavljevic, and J. Lozanovic Sajic. "Rapid Evaluation of Maintenance Process Using Statistical Process Control and Simulation." International Journal of Simulation Modelling 17, no. 1 (March 15, 2018): 119–32. http://dx.doi.org/10.2507/ijsimm17(1)424.

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28

Ziegel, Eric R., and C. Mamzic. "Statistical Process Control." Technometrics 38, no. 4 (November 1996): 410. http://dx.doi.org/10.2307/1271327.

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29

Vukovinsky, Kim, and Leonard A. Doty. "Statistical Process Control." Technometrics 40, no. 1 (February 1998): 73. http://dx.doi.org/10.2307/1271395.

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30

Zhao, Si Jun, Jia Yua Shan, and Lu Yan Bi. "6-Axis Serial Robot Simulation Based on SimulationX." Applied Mechanics and Materials 152-154 (January 2012): 1010–17. http://dx.doi.org/10.4028/www.scientific.net/amm.152-154.1010.

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This paper presents research and simulation analysis on kinematics and dynamics problem based on the 6-axis serial robot. By means of Denavit-Hartenberg method, the robot kinematics model is established as well as and the derivation process of kinematic and inverse kinematic resolution is described in detail. Furthermore, in software simulationX, robot system model including mechanical sub-system and control sub-system are founded. Additionally, through simulation, different performances of robot are illustrated based on different trajectory planning and control. In this way a theoretical reference is provided for the further study on trajectory planning and controls of 6-axis serial robot.

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31

Mušič, G., and D. Matko. "Combined simulation for process control: extension of a general purpose simulation tool." Computers in Industry 38, no. 2 (March 1999): 79–92. http://dx.doi.org/10.1016/s0166-3615(98)00110-9.

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32

Ren, Li Na, Na Na Tang, Han Qi Yue, and Bing Zhao Gao. "Simulation and Optimal Control of AMT Starting-up Process." Applied Mechanics and Materials 380-384 (August 2013): 672–75. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.672.

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For a vehicle with 5-speed AMT (Automated Manual Transmission), AMESim model of the powertrain is set up, and comparison of simulation and experimental values is carried out to test the reliability of the simulation model. Through the co-simulation of AMESim and Simulink, we use optimal control and PID control respectively to control the starting process. The simulation result indicates that the optimal control strategy can satisfy the requirement for the drivability and smoothness.

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33

Wu, Shuang. "Study on Simulation of Virtual Numerical Control Machining Process." Applied Mechanics and Materials 701-702 (December 2014): 223–26. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.223.

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This paper is study on the two-dimensional simulation of the machining process, and three-dimensional simulation. Analog three-axis CNC milling machining entity, it will be processed rough triangular facets discrete and discrete vector geometry and tool scans the body to do intersection algorithm to simulate the tool cutting process by continually updating the blank data to achieve material simulation removal process.

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34

Vepsäläinen, Timo, and Seppo Kuikka. "Model-Driven Development of Automation and Control Applications: Modeling and Simulation of Control Sequences." Advances in Software Engineering 2014 (August 7, 2014): 1–14. http://dx.doi.org/10.1155/2014/470201.

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The scope and responsibilities of control applications are increasing due to, for example, the emergence of industrial internet. To meet the challenge, model-driven development techniques have been in active research in the application domain. Simulations that have been traditionally used in the domain, however, have not yet been sufficiently integrated to model-driven control application development. In this paper, a model-driven development process that includes support for design-time simulations is complemented with support for simulating sequential control functions. The approach is implemented with open source tools and demonstrated by creating and simulating a control system model in closed-loop with a large and complex model of a paper industry process.

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35

Grassi, Vincent G. "Dynamic simulation as a tool to integrate process design and process control." ISA Transactions 32, no. 4 (December 1993): 323–32. http://dx.doi.org/10.1016/0019-0578(93)90065-5.

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36

Niu, Peng Hui, Yin Lei Qin, Shun Ping Qu, and Xiao Peng Zhang. "HSIC Algorithm of Process Control." Applied Mechanics and Materials 336-338 (July 2013): 847–51. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.847.

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Process control objects mathematical model is difficult to establish. Based on humans thinking mode, control experience, action and intuitional reasoning, Human simulation intelligent control (HSIC) avoids all kinds of difficult problems that we are faced with as seeking the answer of complicated model or establishing brain model, and so there are some advantages for HSIC on process control. In this paper, a detailed algorithm is described for process control by using the method of HSIC. Finally, the results of practical application have proved that HSIC is feasible and effect for process control.

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37

Rumbo, J. Y. "SIMULATION AND CONTROL OF A PRESSURE SWING ADSORPTION PROCESS TO DEHYDRATE ETHANOL." Revista Mexicana de Ingeniería Química 17, no. 3 (July 26, 2018): 1051–81. http://dx.doi.org/10.24275/uam/izt/dcbi/revmexingquim/2018v17n3/rumbo.

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38

Thusoo, Ritika. "Simulation of Quadruple Tank Process for Liquid Level Control." International Journal for Research in Applied Science and Engineering Technology 6, no. 1 (January 31, 2018): 287–91. http://dx.doi.org/10.22214/ijraset.2018.1044.

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39

Kubosawa, Shumpei, Takashi Onishi, and Yoshimasa Tsuruoka. "AI and Simulation for Soft Sensors and Process Control." KAGAKU KOGAKU RONBUNSHU 48, no. 4 (July 20, 2022): 141–51. http://dx.doi.org/10.1252/kakoronbunshu.48.141.

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40

Ye, Mengsi, Huifang Li, Yougang Wang, and Caifu Qian. "Hydroforming of Toroidal Bellows: Process Simulation and Quality Control." Materials 14, no. 1 (December 31, 2020): 142. http://dx.doi.org/10.3390/ma14010142.

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Having higher capacity to undertake pressures and larger compensation ability compared with the U-shape bellows, toroidal or Ω-shape bellows are being more and more widely used in engineering. The wave-shape and wall thickness reduction of bellows are the most important parameters for measuring the hydroforming quality of the bellows. In order to provide references for actual manufacturing, it is valuable to study the factors influencing the hydroforming process and quality of the bellows. In this paper, finite element simulations of the hydroforming process of a monolayer and single-wave toroidal bellows and a two-layer and four-wave toroidal bellows were carried out. Stress and strain distributions before and after unloading were analyzed and the wave height and wall thickness reduction were examined. The numerical results were verified by the actual hydroforming measurements. In addition, ranges of the significant structural or operating factors for producing better bellows were studied and a formula to compute the wall thickness reduction was fitted based on the sufficient numerical results of the hydroforming simulations.

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41

Bao, Yafeng, Katsunori Inoue, Yasuo Takahashi, Takuya Tsumura, Guifeng Zhang, and Peilin Jiang. "Numerical Simulation Method on Resistance Projection Welding Control Process." ISIJ International 40, Suppl (2000): S1—S5. http://dx.doi.org/10.2355/isijinternational.40.suppl_s1.

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42

Okolnishnikov, V. V., S. V. Rudometov, S. R. Shakirov, and S. S. Zhuravlev. "Testing of Process Control Systems in Mining using simulation." MATEC Web of Conferences 125 (2017): 04011. http://dx.doi.org/10.1051/matecconf/201712504011.

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43

Pokkunuri, Bhanu. "Knowledge-based simulation for process monitoring and regulatory control." Intelligent Systems Engineering 3, no. 1 (1994): 9. http://dx.doi.org/10.1049/ise.1994.0002.

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44

Li, Shuhui, Zhigang Wu, and Weigang Zhang. "Finite Element Simulation and Quality Control of Hydropiercing Process." Advanced Science Letters 4, no. 8 (August 1, 2011): 2613–17. http://dx.doi.org/10.1166/asl.2011.1327.

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45

Pinheiro, Carla I. C., Joana L. Fernandes, Luís Domingues, Alexandre J. S. Chambel, Inês Graça, Nuno M. C. Oliveira, Henrique S. Cerqueira, and Fernando Ramôa Ribeiro. "Fluid Catalytic Cracking (FCC) Process Modeling, Simulation, and Control." Industrial & Engineering Chemistry Research 51, no. 1 (December 21, 2011): 1–29. http://dx.doi.org/10.1021/ie200743c.

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46

Young, B. R., W. Y. Svrcek, and D. P. Mahoney. "Real-Time Simulation Workshops for Undergraduate Process Control Education." IFAC Proceedings Volumes 33, no. 31 (December 2000): 187–91. http://dx.doi.org/10.1016/s1474-6670(17)37861-8.

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47

Mu?ic, G., D. Matko, and B. Zupancic. "Modelling, Synthesis, and Simulation of Supervisory Process Control Systems." Mathematical and Computer Modelling of Dynamical Systems 6, no. 2 (June 1, 2000): 169–89. http://dx.doi.org/10.1076/1387-3954(200006)6:2;1-m;ft169.

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48

Cierpisz, Stanislaw. "Extreme Control of a Coal Blending Process Simulation Analysis." IFAC Proceedings Volumes 37, no. 15 (September 2004): 341–45. http://dx.doi.org/10.1016/s1474-6670(17)31047-9.

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49

Sawaragi, Y., and Y. Nakamori. "Computer Aided Modeling and Simulation in Process Control Systems." IFAC Proceedings Volumes 24, no. 4 (July 1991): 227–32. http://dx.doi.org/10.1016/s1474-6670(17)54276-7.

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50

MATSUMURA, Takashi, and Jürgen LEOPOLD. "Simulation of Drilling Process for Control of Burr Formation." Journal of Advanced Mechanical Design, Systems, and Manufacturing 4, no. 5 (2010): 966–75. http://dx.doi.org/10.1299/jamdsm.4.966.

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