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Journal articles on the topic 'Chemical Process Modeling'

Author: Grafiati

Published: 6 September 2023

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1

Byun, Ki Ryang, Jeong Won Kang, Ki Oh Song, and Ho Jung Hwang. "Atomic Scale Modeling of Chemical Mechanical Polishing Process." Journal of the Korean Institute of Electrical and Electronic Material Engineers 18, no. 5 (May 1, 2005): 414–22. http://dx.doi.org/10.4313/jkem.2005.18.5.414.

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2

Tiong, Low Soon, and Arshad Ahmad. "A Hybrid Model for Chemical Process Modeling." IFAC Proceedings Volumes 30, no. 25 (September 1997): 163–68. http://dx.doi.org/10.1016/s1474-6670(17)41318-8.

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3

Bhat, N. V., P. A. Minderman, T. McAvoy, and N. S. Wang. "Modeling chemical process systems via neural computation." IEEE Control Systems Magazine 10, no. 3 (April 1990): 24–30. http://dx.doi.org/10.1109/37.55120.

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4

BILIAIEV, М. М., V. V. BILIAIEVA, O. V. BERLOV, V. A. KOZACHYNA, and Z. M. YAKUBOVSKA. "MATHEMATICAL MODELING OF UNSTATIONARY AIR POLLUTION PROCESS." Ukrainian Journal of Civil Engineering and Architecture, no. 3 (015) (June 24, 2023): 13–19. http://dx.doi.org/10.30838/j.bpsacea.2312.140723.13.949.

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Problem statement. The task of determining the dynamics of air pollution in the working room when air containing a chemically hazardous substance flows into it is considered. The peculiarity of this problem is that the formation of pollution areas in the room is influenced by many factors, especially the internal geometry (the presence of technological equipment in the room, furniture, etc.). Therefore, it is necessary to have specialized mathematical models that allow predicting the level of chemical air pollution in the room for a given type of pollution. The purpose of the article. Development of a three-dimensional numerical model for indoor air flow aerodynamics and mass transfer of a chemically hazardous substance entering the room through the ventilation system to predict the risk of toxic damage to workers. Methodology. A three-dimensional equation of convective-diffusion transport for a chemically hazardous substance is used to model the process of a chemically hazardous substance spread in the working room air. The air flow velocity field in the working room is calculated on the basis of the model for the incompressible fluid potential motion. For the numerical integration of the Laplace equation for the velocity potential, two finite-difference schemes are used. The splitting method and finite-difference schemes are used for the numerical integration of the three-dimensional mass transfer equation of the impurity. At each splitting step, the determination of the unknown concentration of the impurity is carried out according to an explicit formula. A computer code was created to conduct computational experiments based on the developed numerical model. Scientific novelty. A three-dimensional numerical model has been developed to analyse the dynamics of the formation of chemical air pollution areas in workplaces when impurities enter the premises through the ventilation system. A feature of the model is the consideration of the main physical factors affecting the formation of pollution areas and the calculation speed. Practical value. The numerical model and the computer code developed on its basis allow solving specific problems that arise when assessing the risk of toxic damage to workers at chemically hazardous facilities. Conclusions. An effective three-dimensional numerical model and computer code have been created, which allow predicting the level of chemical contamination of working premises when a toxic substance enters the premises through the ventilation system. The results of the computational experiment are presented.

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5

Dong, Gao, Xu Xin, Zhang Beike, Ma Xin, and Wu Chongguang. "A Framework for Agent-based Chemical Process Modeling." Journal of Applied Sciences 13, no. 17 (August 15, 2013): 3490–96. http://dx.doi.org/10.3923/jas.2013.3490.3496.

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6

Bogomolov, B. B., E. D. Bykov, V. V. Men’shikov, and A. M. Zubarev. "Organizational and technological modeling of chemical process systems." Theoretical Foundations of Chemical Engineering 51, no. 2 (March 2017): 238–46. http://dx.doi.org/10.1134/s0040579517010043.

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7

Nie, Miaomiao, Jing Tan, Wen-Sheng Deng, and Yue-Feng Su. "Modeling Investigation of Concurrent-flow Chemical Extraction Process." Journal of Physics: Conference Series 1284 (August 2019): 012024. http://dx.doi.org/10.1088/1742-6596/1284/1/012024.

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8

Gao, Li, and Norman W. Loney. "Evolutionary polymorphic neural network in chemical process modeling." Computers & Chemical Engineering 25, no. 11-12 (November 2001): 1403–10. http://dx.doi.org/10.1016/s0098-1354(01)00708-6.

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9

Gau, Chao-Yang, and Mark A. Stadtherr. "New interval methodologies for reliable chemical process modeling." Computers & Chemical Engineering 26, no. 6 (June 2002): 827–40. http://dx.doi.org/10.1016/s0098-1354(02)00005-4.

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10

McBride, Kevin, and Kai Sundmacher. "Overview of Surrogate Modeling in Chemical Process Engineering." Chemie Ingenieur Technik 91, no. 3 (January 3, 2019): 228–39. http://dx.doi.org/10.1002/cite.201800091.

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11

Khorami, Hassan, Hedia Fgaier, Ali Elkamel, Mazda Biglari, and Baoling Chen. "Multivariate Modeling of a Chemical Toner Manufacturing Process." Chemical Engineering & Technology 40, no. 3 (February 9, 2017): 459–69. http://dx.doi.org/10.1002/ceat.201400433.

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12

Tkachev, A. G., A. A. Kovynev, V. M. Nechaev, and V. F. Pershin. "Modeling the screening process." Theoretical Foundations of Chemical Engineering 42, no. 4 (August 2008): 463–65. http://dx.doi.org/10.1134/s0040579508040155.

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13

Lu, Junde, and Furong Gao. "Process Modeling Based on Process Similarity." Industrial & Engineering Chemistry Research 47, no. 6 (March 2008): 1967–74. http://dx.doi.org/10.1021/ie0704851.

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14

Wiens, Avery E., Andreas V. Copan, and Henry F. Schaefer. "Multi-fidelity Gaussian process modeling for chemical energy surfaces." Chemical Physics Letters: X 3 (July 2019): 100022. http://dx.doi.org/10.1016/j.cpletx.2019.100022.

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15

Witko, M., R. Tokarz, and J. Haber. "Vanadium pentoxide. II. Quantum chemical modeling." Applied Catalysis A: General 157, no. 1-2 (September 1997): 23–44. http://dx.doi.org/10.1016/s0926-860x(97)00019-7.

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16

Nentwich, Corina, Philip Gebus, Alexander Brächer, and Ana Markovic. "Hybrid Process Modeling of an Industrial Process." Chemie Ingenieur Technik 93, no. 12 (November 11, 2021): 2092–96. http://dx.doi.org/10.1002/cite.202100085.

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17

Mhilu, C. F. "Modeling Performance of High-Temperature Biomass Gasification Process." ISRN Chemical Engineering 2012 (November 1, 2012): 1–13. http://dx.doi.org/10.5402/2012/437186.

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Biomass utilization is becoming a subject of increasing interest as an alternative to clean fuel. A novel gasification process using highly preheated air gasifier using agricultural residue such as sugar bagasse, rice husks, and palm stem widely available in Tanzania is presented. The study examines, irreversibilities making the gasifier the least efficient unit in the gasification process employing a thermodynamic equilibrium model allowing predicting the main product gas composition CO, CO2, H2, and CH4. The derived model equations are computed using the MAPLE process simulation code in MATLAB. The gasification regime is investigated at temperatures ranging from 800 K to 1400 K and at equivalence ratio (ER) values between 0.3 and 0.4. The results obtained conform to the second law efficiency based on chemical exergy yielding maximum values for the types of biomass materials used. These results indicate that the application of preheated air has an effect on the increase of the chemical exergy efficiency of the product gas, hence reducing the level of irreversibility. Similarly, these results show that the combined efficiency based on physical and chemical exergy is low, suggesting that higher irreversibilities are encountered, since the exergy present in the form of physical exergy is utilized to heat the reactants. Such exergy losses can be minimized by altering the ratio of physical and chemical exergy in the syngas production.

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18

Bhuyan, Debojit, Larry W. Lake, and Gary A. Pope. "Mathematical Modeling of High-pH Chemical Flooding." SPE Reservoir Engineering 5, no. 02 (May 1, 1990): 213–20. http://dx.doi.org/10.2118/17398-pa.

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19

Kuwahara, Kazunari, Yoshihiro Hiramura, Shintaro Ohmura, Masahiro Furutani, Yasuyuki Sakai, and Hiromitsu Ando. "OS3-3 Chemical Kinetics Study on Effect of Pressure on Hydrocarbon Ignition Process(OS3 Application of chemical kinetics to combustion modeling,Organized Session Papers)." Proceedings of the International symposium on diagnostics and modeling of combustion in internal combustion engines 2012.8 (2012): 128–33. http://dx.doi.org/10.1299/jmsesdm.2012.8.128.

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20

Nemchinova, Nina V., Andrey A. Tyutrin, and Sergei N. Fedorov. "Mathematical Modeling оf the Silicon Production Process from Pelletized Charge." Materials Science Forum 989 (May 2020): 394–99. http://dx.doi.org/10.4028/www.scientific.net/msf.989.394.

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The paper considers the problem of recycling the dust waste resulting from metallurgical silicon production; such dust contains considerable amounts of valuable silica. The problem is solved by redirecting this byproduct to the silicon smelting process. We herein propose using the dust left in silicon and aluminum production as a component of pelletized charge, used for silicon smelting in ore-thermal furnaces (OTF). Mathematical (physico-chemical) modeling was applied to study the behavior of pelletized-charge components, in order to predict the chemical composition of smelting-produced silicon. We generated a model that simulated the four temperature zones of a furnace, as well as the crystalline-silicon phase (25°С). The model contained 17 elements entering the furnace, due to being contained in raw materials, electrodes, and the air. Modeling produced molten silicon, 91.73 wt% of which was the target product. Modeling showed that, when using the proposed combined charge, silicon extraction factor would amount to 69.25%, which agrees well with practical data. Results of modeling the chemical composition of crystalline silicon agreed well with the chemical analysis of actually produced silicon.

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21

Fahmi, Ismail, Aroonsri Nuchitprasittichai, and Selen Cremaschi. "A new representation for modeling biomass to commodity chemicals development for chemical process industry." Computers & Chemical Engineering 61 (February 2014): 77–89. http://dx.doi.org/10.1016/j.compchemeng.2013.10.012.

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22

Mantripragada, Hari C., and Götz Veser. "Intensifying chemical looping dry reforming: Process modeling and systems analysis." Journal of CO2 Utilization 49 (July 2021): 101555. http://dx.doi.org/10.1016/j.jcou.2021.101555.

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23

Ling, LIU, and CUI Guangbai. "Modeling Study on the Chemical Biodegradation Process in Remediation Sites." Journal of Lake Sciences 12, no. 3 (2000): 255–64. http://dx.doi.org/10.18307/2000.0310.

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24

Askarova, A. S., S. A. Bolegenova, E. I. Lavrishcheva, and I. V. Loktionova. "The Modeling of Chemical Technological Process in the Fire Chambers." Eurasian Chemico-Technological Journal 4, no. 3 (June 30, 2017): 147. http://dx.doi.org/10.18321/ectj527.

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<p>In this paper the results obtained by the method of numerical modeling of Ekibastuz coal burning in furnace on the example of fire chamber fixed on Aksy hydroelectric station are represented. Numerical experiment was carried out on the basis of three-dimensional equations of convective heat and mass transfer, taking into account the heat propagation, heat radiation, chemical reactions and multiphase structure of the medium. After the numerical experiment, the pictures of temperature distribution on the height of the chamber and concentration of CO, CO<sub>2</sub>, ash and coke distribution along the chamber were obtained. The results are represented graphically.</p>

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25

Bhat, N., P. Minderman, and T. McAvoy. "Use of Neural Nets for Modeling of Chemical Process Systems." IFAC Proceedings Volumes 22, no. 8 (August 1989): 169–75. http://dx.doi.org/10.1016/s1474-6670(17)53353-4.

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26

Wu, Lixiao. "Modeling of Wafer Topography's Effect on Chemical–Mechanical Polishing Process." IEEE Transactions on Semiconductor Manufacturing 20, no. 4 (November 2007): 439–50. http://dx.doi.org/10.1109/tsm.2007.907624.

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27

Theißen, Manfred, Ri Hai, and Wolfgang Marquardt. "A framework for work process modeling in the chemical industries." Computers & Chemical Engineering 35, no. 4 (April 2011): 679–91. http://dx.doi.org/10.1016/j.compchemeng.2010.10.012.

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28

Bangi, Mohammed Saad Faizan, and Joseph Sang-Il Kwon. "Deep hybrid modeling of chemical process: Application to hydraulic fracturing." Computers & Chemical Engineering 134 (March 2020): 106696. http://dx.doi.org/10.1016/j.compchemeng.2019.106696.

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29

You, Dali, Susanne Katharina Michelic, and Christian Bernhard. "Modeling of Ladle Refining Process Considering Mixing and Chemical Reaction." steel research international 91, no. 11 (May 4, 2020): 2000045. http://dx.doi.org/10.1002/srin.202000045.

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30

Ma, Lijuan, Yanjiang Qiao, and Zhisheng Wu. "Process quality control of the manufacturing of Chinese Materia Medica by process analysis technology." NIR news 30, no. 7-8 (September 20, 2019): 14–18. http://dx.doi.org/10.1177/0960336019875924.

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Process quality control is essential for the manufacturing of Chinese Materia Medica. Non-destructive monitoring is still a challenge for the quality control of Chinese Materia Medica manufacturing. As the commonly used non-destructive process analysis technology, near infrared spectroscopy, near infrared chemical imaging, and laser-induced breakdown spectroscopy have been applied to the quality control of Chinese Materia Medica manufacturing. The characteristic near infrared bands of 29 natural chemical components have been assigned. Ten spectral pre-processing methods and five variable selection methods have been optimized, respectively. Given the interrelationship among modeling parameters, a system modeling concept was put forward to establish a global model. Accuracy Profile was further proposed as the validation method of models. In addition, the homogeneity of chlorpheniramine maleate tablets from six brands was successfully visualized by near infrared-chemical imaging. As and Hg variation in An-Gong-Niu-Huang Wan have been rapidly monitored by laser-induced breakdown spectroscopy. A systematic modeling method (model establishment, evaluation, and validation) and the non-destructive technology of homogeneity visualization and toxic element detection have been developed for quality control of Chinese Materia Medica manufacturing, which is shown in Figure 1 of the article. These achievements have greatly promoted the research and application of process analysis technology in Chinese Materia Medica manufacturing, laying a solid foundation for the development of intelligent Chinese pharmaceutical industry.

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31

Polo, C., and M. Pepio. "Global Modeling of a Textile Process." Textile Research Journal 61, no. 2 (February 1991): 114–18. http://dx.doi.org/10.1177/004051759106100210.

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32

Sun, Weike, and Richard D. Braatz. "Smart process analytics for predictive modeling." Computers & Chemical Engineering 144 (January 2021): 107134. http://dx.doi.org/10.1016/j.compchemeng.2020.107134.

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33

Sridhar, Dasaratha V., Richard C. Seagrave, and Eric B. Bartlett. "Process modeling using stacked neural networks." AIChE Journal 42, no. 9 (September 1996): 2529–39. http://dx.doi.org/10.1002/aic.690420913.

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34

Eskinat, Esref. "Dynamic consistency relations for process modeling." AIChE Journal 49, no. 8 (August 2003): 2224–27. http://dx.doi.org/10.1002/aic.690490829.

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35

Gurbanova, U. M., Z. S. Safaraliyeva, N. R. Abishova, R. G. Huseynova, and D. B. Tagiyev. "MATHEMATICAL MODELING THE ELECTROCHEMICAL DEPOSITION PROCESS OF Ni–Mo THIN FILMS." Azerbaijan Chemical Journal, no. 3 (September 28, 2021): 6–11. http://dx.doi.org/10.32737/0005-2531-2021-3-6-11.

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To avoid the numerous experiments for determining optimal conditions and electrolyte composition at co-deposition of two metals we have cleated the regression equation. Mathematical calculations have been carried out using the Optum ME package program with the study of some factors as current density, concentration of main components, temperature, etc. which effect on the co-deposition process. Three independent variables have been selected. The amount of molybdenum in the deposit has been chosen as the dependent variable. The developed regression equation quite adequately describes the co-deposition process of nickel with molybdenum and can be used at planning the works on obtaining alloys with the required composition by the electrochemical method

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36

Zhu, Yaochan, and Eckart Schnack. "Numerical Modeling Chemical Vapor Infiltration of SiC Composites." Journal of Chemistry 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/836187.

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The multiphase field model for chemical vapor infiltration (CVI) of SiC/SiC composites is developed in this study, thereby to reproduce the microstructure evolution during CVI process and to achieve better understanding of the effect of process parameters (e.g., temperature, pressure, etc.) on the final product. In order to incorporate the thermodynamics of methyltrichlorosilane (MTS) pyrolysis into phase field model framework, the reduced chemical reaction mechanism is adopted. The model consists of a set of nonlinear partial differential equations by coupling Ginzburg-Landau type phase field equations with mass balance equations (e.g., convection-diffusion equation) and the modified Navier-Stokes equations which accounts for the fluid motion. The microstructure of preferential codeposition of Si, SiC under high ratio of H2to MTS is simulated and the potential risk of blockage of the premature pores during isothermal CVI process is predicted. The competitive growth mechanism of SiC grains is discussed and the formation process of potential premature pore blockage is reproduced.

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37

Rauf Babayev, Lala Nabieva, Rauf Babayev, Lala Nabieva. "MODELING THE TECHNOLOGICAL PROCESS OF METHANOL PRODUCTION." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 26, no. 03 (March 14, 2023): 90–97. http://dx.doi.org/10.36962/pahtei26032023-90.

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Among the technologies of large-scale production of various artificial liquid fuels, one of the most promising is the technology of obtaining methanol from synthesis gas, produced in turn from coal or natural gas. Methanol is a multi-purpose intermediate, on the basis of which various important chemical products can be obtained, as well as an environmentally friendly liquid fuel and solvent. It is convenient for transportation and storage. In recent years, the value of methanol has increased dramatically. Remaining the most important chemical raw material (semi-product), it can help solve most acute and urgent problems of energy, transport, ecology, since methanol can serve as a universal energy carrier, component and raw material for the production of motor fuels, high-octane additives, hydrogen, carbon source for microbiological synthesis of proteins. Interest in methanol is also evident in the formation of long-term energy strategies aimed primarily at solving The paper presents mathematical modeling of the technological process of methanol production. The model of kinetics is calculated according to two main reactions (reactions of interaction of carbon monoxide with hydrogen, carbon dioxide with hydrogen) occurring in kolonne sinteza. Model of dependence of volumetric flow rate and methanol concentration on linear velocity. Model of the dependence of the density of aqueous methanol solutions on concentration and temperature. The influence of regime and technological parameters on the process of obtaining methanol is considered. Based on the analysis of models, the most optimal technological parameters can be selected, which are used to improve the efficiency of the process and the yield of the target product. Keywords: methyl alcohol, reactions in the synthesis column, reaction models

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38

Zivotic, Miodrag, Marta Trninic, Nebojsa Manic, Dragoslava Stojiljkovic, and Aleksandar Jovovic. "Modeling devolatalization process of Serbian lignites using chemical percolation devolatilization model." Thermal Science 23, Suppl. 5 (2019): 1543–57. http://dx.doi.org/10.2298/tsci180627195z.

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Different mathematical models can describe coal devolatilization as the part of combustion process. Some models are simple, while others are more complex and take into account coal's complexity and heterogeneity of structure. A chemical percolation devolatilization model for describing the devolatilization process of two Serbian lignites from Kostolac and Kolubara open coal mines was studied. Results of the model were compared to devolatilization measurements obtained from two experimental methods ? a wire mesh reactor and thermogravimetric analysis. Two coal samples with four different granulations were investigated for each lignite under different experimental conditions (different maximum temperatures and heating rates). Total volatile yields obtained from the wire mesh reactor and thermogravimetric analysis together with results predicted by the chemical percolation devolatilization model are presented and compared with literature data. For thermogravimetric analysis simulation, the chemical percolation devolatilization model yielded better results in cases where the kinetic parameters obtained under experimental conditions were used rather than kinetic parameters derived from predefined values in the model itself. For wire mesh reactor, the chemical percolation devolatilization model predictions of devolatilization were mixed and were dependent on temperature.

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39

Rao, Addanki Sambasiva, Medha A. Dharap, and J. V. L. Venkatesh. "Experimental Study of the Effect of Post Processing Techniques on Mechanical Properties of Fused Deposition Modelled Parts." International Journal of Manufacturing, Materials, and Mechanical Engineering 5, no. 1 (January 2015): 1–20. http://dx.doi.org/10.4018/ijmmme.2015010101.

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FDM (Fused Deposition Modelled) parts are chemically treated with two types of chemicals viz Dimethyl ketone (Acetone) and Methyl ethyl ketone to reduce the surface roughness. This chemical treatment method technique not only reduces surface roughness but also makes effect on strength of chemically treated parts of ABS (Acrylonitrile Butadiene Styrene) material. In this study Taguchi method of DOE (Design of Experiments) is conducted on test specimen of “tensile”, “bending” and “izod impact” components which are manufactured through Fused Deposition Modeling process using ABS-P400 material. DOE is conducted to optimize the effect of chemical treatment process parameters on strength of above specimen parts. The process parameters considered for the DOE are “different levels of concentration of chemical, temperature, time, layer thickness etc. ANOVA (Analysis of variance) is used to know the significance of contribution of each of these parameters. Results reveal that the prototypes when treated at optimum condition the tensile strength, flexural strength and izod impact strength improved significantly.

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40

Johann, G., E. A. Silva, O. C. Motta Lima, and N. C. Pereira. "Mathematical modeling of a convective textile drying process." Brazilian Journal of Chemical Engineering 31, no. 4 (December 2014): 959–65. http://dx.doi.org/10.1590/0104-6632.20140314s00002685.

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41

Kovendi, Zoltan, Vlad Mureşan, Mihail Abrudean, Iulia Clitan, Mihaela Ligia Ungureşan, and Tiberiu Coloşi. "Modeling a Chemical Exchange Process for the ¹³C Isotope Enrichment." Applied Mechanics and Materials 772 (July 2015): 27–32. http://dx.doi.org/10.4028/www.scientific.net/amm.772.27.

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This paper presents a solution for modeling a chemical exchange process carbon dioxide (CO2) – carbamate for the 13C isotope enrichment. A big difficulty in the process modeling procedure is the fact that it is a non-linear one. In order to solve this problem, an original modeling solution that permits the process simulation for the entire domain of the values of the input signal is used. The process modeling is made in order to include it in an automatic control structure of the 13C isotope concentration. Some relevant simulations of the open loop process are presented, both in the case when the disturbances do not occur and the case when they occur in the system.

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42

Amanbaev, T. R. "Modeling of flotation process in dispersed systems." Theoretical Foundations of Chemical Engineering 48, no. 2 (March 2014): 188–98. http://dx.doi.org/10.1134/s0040579514020031.

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43

Huang, Kejin, Keigo Matsuda, and Takeichiro Takamatsu. "A Simple Method for Modeling Process Asymmetry." JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 39, no. 4 (2006): 448–52. http://dx.doi.org/10.1252/jcej.39.448.

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44

Hanakuma, Yoshitomo, Takashi Sasaki, and Eiji Nakanishi. "Cae system for modeling of process dynamics." KAGAKU KOGAKU RONBUNSHU 15, no. 5 (1989): 919–23. http://dx.doi.org/10.1252/kakoronbunshu.15.919.

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45

Farajzadeh, R., T. Matsuura, D. van Batenburg, and H. Dijk. "Detailed Modeling of the Alkali/Surfactant/Polymer (ASP) Process by Coupling a Multipurpose Reservoir Simulator to the Chemistry Package PHREEQC." SPE Reservoir Evaluation & Engineering 15, no. 04 (June 18, 2012): 423–35. http://dx.doi.org/10.2118/143671-pa.

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Summary Accurate modeling of an ASP flood requires detailed representation of geochemistry and, if natural acids are present, the saponification process. Geochemistry and saponification affect the propagation of the injected chemicals and the amount of generated natural soaps. These in turn determine the chemical phase behavior and, hence, the effectiveness of the ASP process. In this paper, it is shown that by coupling a multipurpose reservoir simulator (MPRS) with PHREEQC (Parkhurst and Appelo 1999; Charlton and Parkhurst 2008), a robust and flexible tool is developed to model ASP floods. PHREEQC is used as the chemical-reaction engine, which determines the equilibrium state of the chemical processes modeled. The MPRS models the impact of the chemicals on the flow properties, solves the flow equations, and transports the chemicals. The validity of the approach is confirmed by benchmarking the results with the ASP module of the UTCHEM simulator (Delshad et al. 2000). Moreover, ASP corefloods have been matched with the new tool. The functionality of the model also has been tested on a 2D sector model. The advantages of using PHREEQC as the chemical engine include its rich database of chemical species and its flexibility in changing the chemical processes to be modeled. Therefore, the coupling procedure presented in this paper can also be extended to other chemical enhanced-oil-recovery (EOR) methods.

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46

Yaqub, Z. T., B. O. Oboirien, and A. T. Akintola. "Process modeling of chemical looping combustion (CLC) of municipal solid waste." Journal of Material Cycles and Waste Management 23, no. 3 (January 22, 2021): 895–910. http://dx.doi.org/10.1007/s10163-021-01180-0.

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47

Sarigiannis, Dimosthenis, and Spyros Karakitsios. "Advancing Chemical Risk Assessment through Human Physiology-Based Biochemical Process Modeling." Fluids 4, no. 1 (January 4, 2019): 4. http://dx.doi.org/10.3390/fluids4010004.

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Physiology-Based BioKinetic (PBBK) models are of increasing interest in modern risk assessment, providing quantitative information regarding the absorption, metabolism, distribution, and excretion (ADME). They focus on the estimation of the effective dose at target sites, aiming at the identification of xenobiotic levels that are able to result in perturbations to the biological pathway that are potentially associated with adverse outcomes. The current study aims at the development of a lifetime PBBK model that covers a large chemical space, coupled with a framework for human biomonitoring (HBM) data assimilation. The methodology developed herein was demonstrated in the case of bisphenol A (BPA), where exposure analysis was based on European HBM data. Based on our calculations, it was found that current exposure levels in Europe are below the temporary Tolerable Daily Intake (t-TDI) of 4 μg/kg_bw/day proposed by the European Food Safety Authority (EFSA). Taking into account age-dependent bioavailability differences, internal exposure was estimated and compared with the biologically effective dose (BED) resulting from translating the EFSA temporary total daily intake (t-TDI) into equivalent internal dose and an alternative internal exposure reference value, namely biological pathway altering dose (BPAD); the use of such a refined exposure metric, showed that environmentally relevant exposure levels are below the concentrations associated with the activation of biological pathways relevant to toxicity based on High Throughput Screening (HTS) in vitro studies.

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Balabanova, M. Yu, S. Yu Panov, and A. A. Khvostov. "Modeling the Process of Chemical-Thermal Processing of Cellulose-Containing Materials." Vestnik Tambovskogo gosudarstvennogo tehnicheskogo universiteta 26, no. 3 (2020): 421–30. http://dx.doi.org/10.17277/vestnik.2020.03.pp.421-430.

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The issues of numerical modeling of chemical-thermal processing of cellulose-containing waste are considered. The simulation results are presented in comparison with the data of experiments carried out in a horizontal pyrolysis reactor with a turner at various process temperatures. The process and the resulting products are influenced by the factors of the mixing rate of the products in the reactor core and the temperature distribution in this zone. To describe the thermal processes and chemical kinetics of cellulose pyrolysis, a model proposed by Yu. Dean was chosen, in which the process is represented by a heterogeneous n-order reaction and the Arrhenius equation. It is assumed that the process of heat transfer during pyrolysis takes place inside a horizontal tube of circular cross-section under conditions of free convection in a limited volume, which has an excess temperature in relation to the surrounding space. It is shown that the results obtained with the help of numerical modeling make it possible to choose the most optimal value of the linear movement of the material in the reactor vessel, depending on the required mode of chemical-thermal processing of cellulose-containing waste.

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Kleijn, C. R., K. J. Kuijlaars, M. Okkerse, H. van Santen, and H. E. A. van den Akker. "Some recent developments in chemical vapor deposition process and equipment modeling." Le Journal de Physique IV 09, PR8 (September 1999): Pr8–117—Pr8–132. http://dx.doi.org/10.1051/jp4:1999815.

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Min, Kwang Gi, In-Su Han, and Chonghun Han. "Iterative Error-based Nonlinear PLS Method for Nonlinear Chemical Process Modeling." JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 35, no. 7 (2002): 613–25. http://dx.doi.org/10.1252/jcej.35.613.

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