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Journal articles on the topic 'Kinetic modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2025

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1

Provis, J. L., and J. S. J. van Deventer. "Geopolymerisation kinetics. 2. Reaction kinetic modelling." Chemical Engineering Science 62, no. 9 (May 2007): 2318–29. http://dx.doi.org/10.1016/j.ces.2007.01.028.

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2

L Salami, DO Olumuyiwa, EA Alfred, and OS Olakanmi. "Kinetic modelling of dumpsite leachate treatment using Musa sapientum peels as bio-sorbent." Global Journal of Engineering and Technology Advances 9, no. 2 (November 30, 2021): 024–31. http://dx.doi.org/10.30574/gjeta.2021.9.2.0117.

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Kinetics models are very vital to dumpsite operators and planners as they provide relevant information for effective treatment of leachates. The aim of this work is to model the kinetic process of treatment of Lagos dumpsite leachate using Musa sapientum peels as bio-sorbent with a view of establishing the kinetic parameters of the treatment process. Musa sapientum peels which were collected from Ayetoro market in Epe Local Government area of Lagos State were used to prepare the bio-sorbent. Kinetic process was carried out using 1 g of the prepared bio-sorbent in 100 ml Lagos dumpsite leachate in different conical flasks and at various contacting time. The kinetic data obtained were fitted to different kinetics models. The kinetics models tested were Fractional power model, Lagregren pseudo first – order model, Pseudo second – order model, Kuo – Lotse kinetic model, Blanchard kinetic model and Elovich kinetic model. Other kinetics models considered were Sobkowsk – Czerwi kinetic model, Intraparticle diffusion (IPD) model, Behnajady – Modirshahla – Ghanbery (BMG) model and Diffusion – Chemisorption model. Coefficient of determination (R2) values and the expected nature of the plots of the models were used to screen the tested models. The results revealed that the Pseudo second – order kinetic model has the best R2 value of 0.99996 and the graph followed the expected nature of the plot hence it was adopted in this work. It was concluded that Pseudo second – order kinetic model can be used to navigate the treatment process of Lagos dumpsite using Musa sapientum peels as bio-sorbent.

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3

Chalyy, K. O., I. P. Kryvenko, and M. D. Andriychuk. "KINETIC MODELLING OF BIOCHEMICAL REACTIONS USING MATHСAD ANALYTICAL TOOLKIT." Medical Science of Ukraine (MSU) 20, no. 2 (June 30, 2024): 68–78. http://dx.doi.org/10.32345/2664-4738.2.2024.09.

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Background. The study of the kinetics of biochemical reactions provides a better understanding of how biological processes occur in living organisms. Understanding the peculiarities of such reactions is important for the development of new technologies, in particular for the production of biologically active substances and for the synthesis of drugs. A powerful tool for solving problems in biochemical reaction kinetics is mathematical modelling, which can be carried out using computer mathematical systems, in particular the MATHCAD analytical toolkit. Aim: to substantiate the feasibility and effectiveness of using the MATHCAD analytical toolkit to solve problems of kinetic modelling of biochemical reactions in pharmaceutical research, and to review the capabilities of MATHCAD for computer modelling in pharmacy. Materials and methods. In the context of studying the rate of enzymatic reactions and developing models, such as the Michaelis-Menten model, to describe reactions in which enzymes catalyze the transformation of substrates, the use of a computer mathematical system (CMS) is considered. CMS is a software package and environment for performing mathematical computations, modelling and visualization. The possibilities of using the MATHCAD system to create mathematical models of biochemical reactions based on kinetic equations are demonstrated. This involves the creation of differential equations describing changes in reagent concentrations over time. These equations were solved using numerical methods in MATHCAD. In addition, the results obtained are visualized using 3D graphics in MATHCAD. The stages of using the MATHCAD analytical toolkit in the kinetic modelling of biochemical reactions have been determined. Results. The use of MATHCAD in the kinetic modelling of biochemical reactions is effective for the study of: (1) the kinetics of enzymatic reactions, e.g. reactions in which an enzyme catalyzes the conversion of a substrate into a product; (2) biochemical reactions that take place in reaction vessels in which reagents mix and interact; (3) modelling of reactions in reaction vessels based on the solution of differential equations of reaction kinetics; (4) the effect of inhibitors or activators on enzymatic reactions; (5) scenarios of interaction of reagents to determine changes in the kinetics of reactions that occur when different active substances are introduced; (6) kinetics of biochemical reactions in cases where reactions are accompanied by diffusion of reagents through membranes or other semi-permeable barriers; (7) modelling the effect of diffusion processes on the kinetics of biochemical reactions; (8) models describing the kinetics of decomposition of substances, for example the decomposition of biologically active compounds in the body or in the environment; (9) predicting the effect of changes in the conditions of the reaction medium (temperature, pH, concentration of reagents) on the kinetics of biochemical reactions. It is substantiated that model descriptions of the kinetics of biochemical reactions are important for forming an understanding of the functions of biological systems, including metabolism, enzymatic reactions, and other physiological phenomena. Tools have been used to visualize the modelling results in the form of three-dimensional MATHCAD graphics, which improves the understanding of the reaction mechanism and allows a more thorough analysis of its kinetics. Conclusion. MATHCAD provides an optimized environment for kinetic modelling of biochemical reactions through its ergonomic interface. Particular advantages are the ability to work with symbolic expressions and to use a wide range of built-in functions and tools for exploring mathematical models and visualizing results. The obtained results may be important both for further scientific pharmaceutical research and for implementation in the training of future Masters of Pharmacy in the discipline of "Computer Modelling in Pharmacy" in higher medical education institutions.

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4

Gunn, Roger, V. Schmid, B. Whitcher, and V. Cunningham. "Bayesian kinetic modelling." NeuroImage 31 (January 2006): T71. http://dx.doi.org/10.1016/j.neuroimage.2006.04.061.

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5

Gotch, F. A. "Urea kinetic modelling." Nephrology Dialysis Transplantation 10, no. 12 (December 1995): 2378–79. http://dx.doi.org/10.1093/ndt/10.12.2378.

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6

Rossetti, Ilenia, Francesco Conte, and Gianguido Ramis. "Kinetic Modelling of Biodegradability Data of Commercial Polymers Obtained under Aerobic Composting Conditions." Eng 2, no. 1 (February 20, 2021): 54–68. http://dx.doi.org/10.3390/eng2010005.

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Methods to treat kinetic data for the biodegradation of different plastic materials are comparatively discussed. Different samples of commercial formulates were tested for aerobic biodegradation in compost, following the standard ISO14855. Starting from the raw data, the conversion vs. time entries were elaborated using relatively simple kinetic models, such as integrated kinetic equations of zero, first and second order, through the Wilkinson model, or using a Michaelis Menten approach, which was previously reported in the literature. The results were validated against the experimental data and allowed for computation of the time for half degradation of the substrate and, by extrapolation, estimation of the final biodegradation time for all the materials tested. In particular, the Michaelis Menten approach fails in describing all the reported kinetics as well the zeroth- and second-order kinetics. The biodegradation pattern of one sample was described in detail through a simple first-order kinetics. By contrast, other substrates followed a more complex pathway, with rapid partial degradation, subsequently slowing. Therefore, a more conservative kinetic interpolation was needed. The different possible patterns are discussed, with a guide to the application of the most suitable kinetic model.

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7

Zalazar, C. S., M. D. Labas, M. E. Lovato, R. J. Brandi, and A. E. Cassano. "Modelling the kinetics of UV/H2O2 oxidation of dichloroacetic acid." Water Science and Technology 55, no. 12 (June 1, 2007): 31–35. http://dx.doi.org/10.2166/wst.2007.377.

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The intrinsic reaction kinetics of the decomposition of dichloroacetic acid (DCA) using UV/H2O2 was studied. A complete mathematical model, including the effect of the absorbed radiation intensities and H2O2 concentration was developed. The results of the kinetic measurements were analysed using a complete mathematical model of the experimental device that was used for the laboratory operation (a differential reactor inside a recycle). In this way it was expected to obtain intrinsic kinetic parameters. Experimental data agree well with theoretical predictions esmploying just two kinetic parameters derived from the proposed reaction mechanism.

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8

Symak, Dmytro, Vira Sabadash, Jaroslaw Gumnitsky, and Zoriana Hnativ. "Kinetic Regularities and Mathematical Modelling of Potassium Chloride Dissolution." Chemistry & Chemical Technology 15, no. 1 (February 15, 2021): 148–52. http://dx.doi.org/10.23939/chcht15.01.148.

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The dissolution process of potassium chloride particles in the apparatus with two-blade mechanical stirrer was investigated and the mass transfer coefficient was determined. The experimental results were generalized by criterion dependence. The independence of the mass transfer coefficient from the solid particles diameter was confirmed. A countercurrent process of potassium salt dissolution in two apparatuses with a mechanical stirring was considered. A mathematical model for countercurrent dissolution was developed and the efficiency of this process was determined.

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9

Frontistis, Z., M. Papadaki, and D. Mantzavinos. "Modelling of sonochemical processes in water treatment." Water Science and Technology 55, no. 12 (June 1, 2007): 47–52. http://dx.doi.org/10.2166/wst.2007.376.

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The mechanisms and kinetics of the sonochemical degradation of organic molecules in water are relatively complex since several parameters such as physicochemical properties, substrate concentration, water matrix, reactor geometry, ultrasound properties (frequency, power, emission system) all typically affect the process. In this work, simple kinetic models were used to predict the degradation of 2-chlorophenol and sodium dodecylbenzene sulphonate in aqueous solutions and verified against experimental data taken from previous studies. A pseudo-first order kinetic expression can adequately describe the degradation of the phenolic substrate, while a heterogeneous model based on the Langmuir-Hinshelwood equation is suitable for the surfactant degradation.

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10

Wentzel, M. C., G. A. Ekama, and G. v. R. Marais. "Processes and Modelling of Nitrification Denitrification Biological Excess Phosphorus Removal Systems – A Review." Water Science and Technology 25, no. 6 (March 1, 1992): 59–82. http://dx.doi.org/10.2166/wst.1992.0114.

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This paper reviews developments in modelling the kinetics of activated sludge systems: Completely aerobic nitrification, anoxic/aerobic nitrification denitrification (ND), and anaerobic/anoxic/aerobic nitrification denitrification biological excess phosphorus removal (NDBEPR) systems. The paper highlights the progress in developing a general NDBEPR activated sludge kinetic model – development of polyP organism enhanced cultures, their kinetics, simplification of the kinetics for enhanced cultures under constant flow and load conditions, extension of the simplified model to mixed culture NDBEPR systems under constant flow and load conditions, integration of the polyP organism enhanced culture kinetics with the ND kinetics to give a general NDBEPR kinetic model for cyclic flow and load which incorporates the increased specific denitrification rates observed in NDBEPR systems compared to ND systems. Areas of research that require attention to complete the development of the general NDBEPR kinetic model are identified – denitrification by polyP organisms, calibration and verification of the model for cyclic flow and load, etc.

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11

Trninić, Marta. "Mathematical modelling of primary and secondary pyrolysis: State of the art." FME Transactions 48, no. 4 (2020): 733–44. http://dx.doi.org/10.5937/fme2004733t.

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Pyrolysis process converts biomass into liquid, gaseous and solid fuels. Chemical kinetics play a key role in explaining the characteristics of pyrolysis reactions and developing mathematical models. Many studies have been undertaken to understand the kinetics of biomass pyrolysis; however, due to the heterogeneity of biomass and the complexity of the chemical and physical changes that occur during pyrolysis, it is difficult to develop a simple kinetic model that is applicable in every case. In this review, different methods to describe biomass primary and secondary pyrolysis with different types of kinetic mechanisms are discussed.

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12

Lo Schiavo, M. "Kinetic modelling andelectoral competition." Mathematical and Computer Modelling 42, no. 13 (December 2005): 1463–86. http://dx.doi.org/10.1016/j.mcm.2004.11.006.

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13

Dhir, S., R. Uppaluri, and M. K. Purkait. "Oxidative desulfurization: Kinetic modelling." Journal of Hazardous Materials 161, no. 2-3 (January 2009): 1360–68. http://dx.doi.org/10.1016/j.jhazmat.2008.04.099.

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14

König, Matthias. "cy3sabiork: A Cytoscape app for visualizing kinetic data from SABIO-RK." F1000Research 5 (July 18, 2016): 1736. http://dx.doi.org/10.12688/f1000research.9211.1.

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Kinetic data of biochemical reactions are essential for the creation of kinetic models of biochemical networks. One of the main resources of such information is SABIO-RK, a curated database for kinetic data of biochemical reactions and their related information. Despite the importance for computational modelling there has been no simple solution to visualize the kinetic data from SABIO-RK. In this work, I present cy3sabiork, an app for querying and visualization of kinetic data from SABIO-RK in Cytoscape. The kinetic information is accessible via a combination of graph structure and annotations of nodes, with provided information consisting of: (I) reaction details, enzyme and organism; (II) kinetic law, formula, parameters; (III) experimental conditions; (IV) publication; (V) additional annotations. cy3sabiork creates an intuitive visualization of kinetic entries in form of a species-reaction-kinetics graph, which reflects the reaction-centered approach of SABIO-RK. Kinetic entries can be imported in SBML format from either the SABIO-RK web interface or via web service queries. The app allows for easy comparison of kinetic data, visual inspection of the elements involved in the kinetic record and simple access to the annotation information of the kinetic record. I applied cy3sabiork in the computational modelling of galactose metabolism in the human liver.

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15

Bakar, Siti Asmah, Hussein Saed Geedi, Mohd Hairul Khamidun, Radin Maya Saphira Radin Mohamed, Mohammad Faizal Che Daud, and Umi Fazara Md Ali. "Evaluation lead removal kinetics modelling of adsorption by using composite of Chitosan and Ceramic waste." IOP Conference Series: Earth and Environmental Science 1205, no. 1 (June 1, 2023): 012010. http://dx.doi.org/10.1088/1755-1315/1205/1/012010.

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Abstract This study focuses on understanding the Pb adsorption kinetics from greywater using a composite of chitosan and ceramic waste (CCCW), which is suitable for preserving water quality. For ease and general application, a kinetic model with a simple expression and a manageable small set of parameters that nevertheless provides a fair adsorption description in the equilibrium state is still critical. Although some current kinetic models, such as the pseudo-second-order type, meet these conditions, their performance is still questionable, especially when applied to a variety of experimental data. Batch adsorption experiments were carried out with the predetermined value of the operational parameter such as adsorbent dosage, contact time, and shaking speed. Kinetic models such as pseudo-first order, pseudo-second order, intraparticle diffusion kinetic model, Avrami model, and the Bangham model were used in this study to understand the kinetics of removal of lead from greywater. The efficacy results of adsorbent’s dose in lead removal process with increasing adsorption capacity with contact time from 0.0014 to 0.00277 mg/g, the removal efficiency increases from 45.90 to 90.83%. The most significant contribution of this work is an understanding of the optimal kinetics model that can describe the behaviour of lead adsorption on CCCW. Five models for the adsorption of Pb2+ have been identified to clarify the kinetics models’ usefulness in accuracy based on rank order. This study may provide insight into understanding the ability and usability of the appropriate model in kinetics adsorption.

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16

Giménez, Jaime, David Curcó, and Pilar Marco. "Reactor modelling in the photocatalytic oxidation of wastewater." Water Science and Technology 35, no. 4 (February 1, 1997): 207–13. http://dx.doi.org/10.2166/wst.1997.0120.

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Two different experimental devices have been tested for the photocatalytic oxidation of phenol, by using TiO2 suspensions. At the laboratory level, experiments were carried out in microreactors with Xe lamps. At pilot plant scale, the experiments were done at the Plataforma Solar de Almería (PSA), Spain, by using a high concentrating radiation systems (Heliomans) and solar radiation. Both systems were characterized from the point of view of the radiation field. Kinetic experiments and radiation measurements showed that kinetics are first order with respect to the phenol concentration, and a linear dependence of the reaction rate on the square root of the photonic flow. Kinetic constants (k) were calculated for both systems considering only concentration-time data. Results indicate that k values obtained at the laboratory were ten times greater than these obtained at the PSA. However, results improve when the radiation entering and the radiation absorbed by the catalyst were considered. The fitting of concentration-radiation data drives to values of the kinetic constants more similar for both systems and for all the catalyst concentrations tested. Thus, these new constants can be useful for the change of scale.

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17

MACKAY, F., R. MARCHAND, K. KABIN, and J. Y. LU. "Test kinetic modelling of collisionless perpendicular shocks." Journal of Plasma Physics 74, no. 3 (June 2008): 301–18. http://dx.doi.org/10.1017/s0022377808007095.

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AbstractTest kinetic simulation results are presented for perpendicular collisionless shocks in magnetized plasmas that are representative of the Earth's bow shock. In this approach, particle kinetics are described by tracing particle trajectories in prescribed electromagnetic fields obtained in the MHD approximation, and applying Liouville's theorem. This provides a first-order description of particle dynamics in complex systems, given approximate fields obtained with a low-level description of the plasma. The method also provides a useful consistency check in assessing the validity of approximate solutions such as those obtained in the ideal MHD approximation. Compared with the more familiar test particle approach, in which trajectories of randomly injected particles are followed in time, the present approach has the advantage of being numerically more efficient, and producing results without statistical errors.

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18

Santabarbara, Stefano, and Giuseppe Zucchelli. "Comparative kinetic and energetic modelling of phyllosemiquinone oxidation in Photosystem I." Physical Chemistry Chemical Physics 18, no. 14 (2016): 9687–701. http://dx.doi.org/10.1039/c5cp06590a.

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The comparison different energetic scenarios proposed for Phyllosemiquinone oxidation in Photosystem I within a unified kinetic and theoretical framework indicates that only a weakly activate or a largely exergonic reaction describe the experimental kinetics.

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19

Sciazko, M., B. Mertas, and L. Stepien. "Kinetic modelling of coking coal fluidity development." Journal of Thermal Analysis and Calorimetry 142, no. 2 (March 10, 2020): 977–90. http://dx.doi.org/10.1007/s10973-020-09487-0.

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Abstract Coal plasticity is a phenomenon directly affecting the creation of coke structure. It is very much a time- and temperature-dependent transformation of the coal matrix, which allows changing the physical phase from solid to liquid-like and again into solid of different properties. The coking process, particularly in a plasticization temperature range, can be considered as a non-isothermal reaction at a constant heating rate. In this work, a macro-kinetics approach is applied that results in effective kinetic parameters, i.e. pre-exponential factor and activation energy. It is postulated in this work that the original content of metaplast (M0) is a part of volatile matter that melts under the effect of temperature. The coal sample can melt steadily with the temperature increase, achieving the maximum fluidity (Fmax) when the total amount of metaplast available turns into the plastic state. Coal behaviour while it is being heated can be described by two mechanisms. Under first one, the coal turns into plastic phase starting at t1 and ending at tmax, where solidification starts. This can be considered as independent reactions model. In the second model, both plasticization and solidification reactions compete over entire range of phenomena. This can be considered as reactions in the series model. The developed models were validated against experimental data of coal fluidity delivering kinetic parameters.

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20

TĂNASE, DOBRE, OANA CRISTINA PÂRVULESCU, and CRISTIAN RĂDUCANU. "Stochastic modelling of polysaccharide hydrolysis." Journal of Engineering Sciences and Innovation 3, no. 1 (January 10, 2018): 25–38. http://dx.doi.org/10.56958/jesi.2018.3.1.25.

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A stochastic model was selected and developed to describe polysaccharide hydrolysis kinetics. This model can accurately predict the hydrolysis kinetics and covers the limitations of some classical kinetic models (e.g., complexity of mathematical models, large number of parameter estimations, change in parameters with a change in hydrolysis conditions, etc.). One of the main advantages of the stochastic mathematical model approach is represented by the fact that the polysaccharide structural characteristics and operating parameters can be separately incorporated into the model. The stochastic process characterizing the model considers that the breakdown of a polysaccharide by hydrolysis is a random process based on the cleavage of a parent macromolecule within a molecular mass range into two descendants within lower molecular mass ranges. The model description and its implementation in the hydrolysis of a hypothetical polysaccharide were presented.

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21

Buekers, Joren, Jan Theunis, Alberto Peña Fernández, Emiel F. M. Wouters, Martijn A. Spruit, Patrick De Boever, and Jean-Marie Aerts. "Box-Jenkins Transfer Function Modelling for Reliable Determination of VO2 Kinetics in Patients with COPD." Applied Sciences 9, no. 9 (May 1, 2019): 1822. http://dx.doi.org/10.3390/app9091822.

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Oxygen uptake (VO2) kinetics provide information about the ability to respond to the increased physical load during a constant work rate test (CWRT). Box-Jenkins transfer function (BJ-TF) models can extract kinetic features from the phase II VO2 response during a CWRT, without being affected by unwanted noise contributions (e.g., phase I contribution or measurement noise). CWRT data of 18 COPD patients were used to compare model fits and kinetic feature values between BJ-TF models and three typically applied exponential modelling methods. Autocorrelation tests and normalised root-mean-squared error values (BJ-TF: 2.8 ± 1.3%; exponential methods A, B and C: 10.5 ± 5.8%, 11.3 ± 5.2% and 12.1 ± 7.0%; p < 0.05) showed that BJ-TF models, in contrast to exponential models, could account for the most important noise contributions. This led to more reliable kinetic feature values compared to methods A and B (e.g., mean response time (MRT), BJ-TF: 74 ± 20 s; methods A-B: 100 ± 56 s–88 ± 52 s; p < 0.05). Only exponential modelling method C provided kinetic feature values comparable to BJ-TF features values (e.g., MRT: 75 ± 20 s). Based on theoretical considerations, we recommend using BJ-TF models, rather than exponential models, for reliable determinations of VO2 kinetics.

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22

Garrote, Gil, Herminia Domı́nguez, and Juan Carlos Parajó. "Kinetic modelling of corncob autohydrolysis." Process Biochemistry 36, no. 6 (January 2001): 571–78. http://dx.doi.org/10.1016/s0032-9592(00)00253-3.

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23

Ungerer, Philippe, Francoise Behar, Marlène Villalba, Odd Ragmar Heum, and Annie Audibert. "Kinetic modelling of oil cracking." Organic Geochemistry 13, no. 4-6 (January 1988): 857–68. http://dx.doi.org/10.1016/0146-6380(88)90238-0.

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24

Matyash, K., R. Schneider, and H. Kersten. "Kinetic modelling of dusty plasmas." Journal of Physics: Conference Series 11 (January 1, 2005): 248–53. http://dx.doi.org/10.1088/1742-6596/11/1/024.

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25

Olsson, Louise, and Bengt Andersson. "Kinetic Modelling in Automotive Catalysis." Topics in Catalysis 28, no. 1-4 (April 2004): 89–98. http://dx.doi.org/10.1023/b:toca.0000024337.50617.8e.

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26

Arlotti, L., N. Bellomo, and M. Lachowicz. "Kinetic equations modelling population dynamics." Transport Theory and Statistical Physics 29, no. 1-2 (January 2000): 125–39. http://dx.doi.org/10.1080/00411450008205864.

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27

Korla, Kalyani, and Chanchal K. Mitra. "Kinetic modelling of mitochondrial translation." Journal of Biomolecular Structure and Dynamics 32, no. 10 (September 13, 2013): 1634–50. http://dx.doi.org/10.1080/07391102.2013.833135.

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28

Matyash, K., and R. Schneider. "Kinetic modelling of dusty plasmas." Contributions to Plasma Physics 44, no. 13 (April 2004): 157–61. http://dx.doi.org/10.1002/ctpp.200410021.

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29

Khan, Ahmed Faraz, Philip John Roberts, and Alexey A. Burluka. "Modelling of Self-Ignition in Spark-Ignition Engine Using Reduced Chemical Kinetics for Gasoline Surrogates." Fluids 4, no. 3 (August 17, 2019): 157. http://dx.doi.org/10.3390/fluids4030157.

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A numerical and experimental investigation in to the role of gasoline surrogates and their reduced chemical kinetic mechanisms in spark ignition (SI) engine knocking has been carried out. In order to predict autoignition of gasoline in a spark ignition engine three reduced chemical kinetic mechanisms have been coupled with quasi-dimensional thermodynamic modelling approach. The modelling was supported by measurements of the knocking tendencies of three fuels of very different compositions yet an equivalent Research Octane Number (RON) of 90 (ULG90, PRF90 and 71.5% by volume toluene blended with n-heptane) as well as iso-octane. The experimental knock onsets provided a benchmark for the chemical kinetic predictions of autoignition and also highlighted the limitations of characterisation of the knock resistance of a gasoline in terms of the Research and Motoring octane numbers and the role of these parameters in surrogate formulation. Two approaches used to optimise the surrogate composition have been discussed and possible surrogates for ULG90 have been formulated and numerically studied. A discussion has also been made on the various surrogates from the literature which have been tested in shock tube and rapid compression machines for their autoignition times and are a source of chemical kinetic mechanism validation. The differences in the knock onsets of the tested fuels have been explained by modelling their reactivity using semi-detailed chemical kinetics. Through this work, the weaknesses and challenges of autoignition modelling in SI engines through gasoline surrogate chemical kinetics have been highlighted. Adequacy of a surrogate in simulating the autoignition behaviour of gasoline has also been investigated as it is more important for the surrogate to have the same reactivity as the gasoline at all engine relevant p − T conditions than having the same RON and Motored Octane Number (MON).

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Junker, Björn H., Dirk Koschützki, and Falk Schreiber. "Kinetic Modelling with the Systems Biology Modelling Environment SyBME." Journal of Integrative Bioinformatics 3, no. 1 (June 1, 2006): 11–20. http://dx.doi.org/10.1515/jib-2006-18.

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Summary Kinetic modelling and simulation is an important approach in systems biology. While the focus of current modelling tools is on simulation, model development is a highly iterative process which is currently only partly supported. To support the development of biochemical models, their simulation, and graphical understanding, we designed and implemented SyBME, the Systems Biology Modelling Environment. Here we present the architecture and the main components of SyBME and show its use by modelling sucrose breakdown in developing potato tubers.

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31

Komasi, Milad, Shohreh Fatemi, and Seyed Hesam Mousavi. "Kinetic Modelling of Propane Dehydrogenation over a Pt–Sn/hierarchical SAPO-34 Zeolite Catalyst, Including Catalyst Deactivation." Progress in Reaction Kinetics and Mechanism 42, no. 4 (December 2017): 344–60. http://dx.doi.org/10.3184/146867817x14954764850397.

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Pt–Sn/hierarchical SAPO-34 was synthesised and kinetically modelled as an efficient and selective catalyst for propylene production through propane dehydrogenation. The kinetics of the reaction network were studied in an integral fixed-bed reactor at three temperatures of 550, 600 and 650 °C and weight hourly space velocities of 4 and 8 h−1 with a feed containing hydrogen and propane with relative molar ratios of 0.2, 0.5 and 0.8, at normal pressure. The experiments were performed in accordance with the full factorial experimental design. The kinetic models were constructed on the basis of different mechanisms and various deactivation models. The kinetics and deactivation parameters were simultaneously predicted and optimised using genetic algorithm optimisation. It was further proven that the Langmuir–Hinshelwood model can well predict propane dehydrogenation kinetics through lumping together all the possible dehydrogenation steps and also by assuming the surface reaction as the rate-determining step. A coke formation kinetic model has also shown appropriate results, confirming the experimental data by equal consideration of both monolayer and multilayer coke deposition kinetic orders and an exponential deactivation model.

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32

Al-Ayed, Omar. "Approaches to Biomass Kinetic Modelling: Thermochemical Biomass Conversion Processes." 1 4, Vol4 (April 1, 2021): 1–13. http://dx.doi.org/10.48103/jjeci412021.

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Modeling of biomass pyrolysis kinetics is an essential step towards reactors design for energy production. Determination of the activation energy, frequency factor, and order of the reaction is necessary for the design procedure. Coats and Redfern's work using the TGA data to estimate these parameters was the cornerstone for modeling. There are two significant problems with biomass modeling, the first is the determination of the kinetic triplet (Activation energy, Frequency factor, and the order of reaction), and the second is the quantitative analysis of products distribution. Methods used in modeling are either One-step or Multistep methods. The one-step techniques allow the determination of kinetic triplet but fail to predict the product distribution, whereas multistep processes indicate the product's distribution but challenging to estimate the parameters. Kissinger, Coats, and Redfern, KAS, FWO, Friedman are one-step methods that have been used to estimate the kinetic parameters. In this work, after testing more than 500 data points accessed from different literature sources for coal, oil shale, solid materials, and biomass pyrolysis using one-step global method, it was found that the activation energy generated by KAS or FWO methods are related as in the following equations: 𝐸𝐾𝐴𝑆 = 0.9629 ∗ 𝐸𝐹𝑊𝑂 + 8.85, with R² =0.9945 or 𝐸𝐹𝑊𝑂 = 1.0328 ∗ 𝐸𝐾𝐴𝑆 − 8.0969 with R2= 0.9945. The multistep kinetic models employed the Distributed Activation Energy Model (DAEM) using Gaussian distribution, which suffers from symmetry, other distributions such as Weibull, and logistic has been used. These multistep kinetic models account for parallel/series and complex, primary and secondary biomass reactions by force-fitting the activation energy values. The frequency factor is assumed constant for the whole range of activation energy. Network models have been used to account for heat and mass transfer (diffusional effects), where the one-step and multistep could not account for these limitations. Three network models are available, the Bio-CPD (Chemical Percolation Devolatilization) model, Bio-FLASHCHAIN, and the Bio-FGDVC (Functional Group Depolymerization Vaporization Crosslinking models). These models tried to predict the product distributions of the biomass pyrolysis process

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33

Bellobono, Ignazio Renato, Roberto Scotti, Massimiliano D'Arienzo, Franca Morazzoni, Riccardo Bianchi, Rodica Stanescu, Cristina Costache, et al. "Nonlinear Modelling of Kinetic Data Obtained from Photocatalytic Mineralisation of 2,4-Dichlorophenol on a Titanium Dioxide Membrane." International Journal of Photoenergy 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/631768.

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Photomineralisation of 2,4-dichlorophenol (DCP) in aqueous solutions (10.0–100.0 mg/L of C) was systematically studied at318±3 K, in an annular laboratory-scale reactor, by photocatalytic membranes immobilizing titanium dioxide, as a function of substrate concentration, and absorbed power per unit length of membrane. Kinetics of both substrate disappearance, to yield intermediates, and total organic carbon (TOC) disappearance, to yield carbon dioxide, were followed (first series of experiments). At a fixed value of irradiance (1.50 W⋅cm−1), other series of mineralization experiments were repeated (second series of experiments) by carrying out only analyses of chemical oxygen demand (COD), in order to compare modelling results of the two sets of experiments. In both sets of experiments, stoichiometric hydrogen peroxide was used as oxygen donor. For the first series of experiments, a kinetic model was employed, already validated in previous work, from which, by a set of differential equations, four final optimised parameters,k1andK1,k2andK2, were calculated. By these parameters, the whole kinetic profile could be fitted adequately. The influence of irradiance onk1andk2could be rationalised very well by this four-parameter kinetic model. Modelling of quantum yields, as a function of irradiance, could also be carried out satisfactorily. As has been found previously for other kinds of substrates, modelling of quantum yields for DCP mineralization is consistent with kinetics of hydroxyl radicals reacting between themselves, leading to hydrogen peroxide, other than with substrate or intermediates leading finally to carbon dioxide, paralleled by a second competition kinetics involving superoxide radical anion. For the second series of experiments, on the contrary, the Langmuir-Hinshelwood model was employed. Uncertainties of COD analyses, coupled with discrepancies of this model and with its inability to reproduce kinetics up to complete mineralization, are underlined.

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Krebs, Olga, Martin Golebiewski, Renate Kania, Saqib Mir, Jasmin Saric, Andreas Weidemann, Ulrike Wittig, and Isabel Rojas. "SABIO-RK: A data warehouse for biochemical reactions and their kinetics." Journal of Integrative Bioinformatics 4, no. 1 (March 1, 2007): 22–30. http://dx.doi.org/10.1515/jib-2007-49.

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Abstract Systems biology is an emerging field that aims at obtaining a system-level understanding of biological processes. The modelling and simulation of networks of biochemical reactions have great and promising application potential but require reliable kinetic data. In order to support the systems biology community with such data we have developed SABIO-RK (System for the Analysis of Biochemical Pathways - Reaction Kinetics), a curated database with information about biochemical reactions and their kinetic properties, which allows researchers to obtain and compare kinetic data and to integrate them into models of biochemical networks. SABIO-RK is freely available for academic use at http://sabio.villa-bosch.de/SABIORK/.

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Marugán, Javier, Rafael van Grieken, Alberto E. Cassano, and Orlando M. Alfano. "Kinetic modelling of the photocatalytic inactivation of bacteria." Water Science and Technology 61, no. 6 (March 1, 2010): 1547–53. http://dx.doi.org/10.2166/wst.2010.057.

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This work analyzes the kinetic modelling of the photocatalytic inactivation of E. coli in water using different types of kinetic models; from an empirical equation to an intrinsic kinetic model including explicit radiation absorption effects. Simple empirical equations lead to lower fitting errors, but require a total of 12 parameters to reproduce the results of four inactivation curves when the catalyst concentration was increased. Moreover, these parameters have no physical meaning and cannot be extrapolated to different experimental conditions. The use of a pseudo-mechanistic model based on a simplified reaction mechanism reduces the number of required kinetic parameters to 6, being the kinetic constant the only parameter that depends on the catalyst concentration. Finally, a simple modification of a kinetic model based on the intrinsic mechanism of photocatalytic reactions including explicit radiation absorption effects achieved the fitting of all the experiments with only three parameters. The main advantage of this approach is that the kinetic parameters estimated for the model become independent of the irradiation form, as well as the reactor size and its geometrical configuration, providing the necessary information for scaling-up and design of commercial-scale photoreactors for water disinfection.

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Bonomo, L., G. Pastorelli, and E. Quinto. "Simplified and Monod kinetics in one-dimensional biofilm reactor modelling: a comparison." Water Science and Technology 43, no. 1 (January 1, 2001): 295–302. http://dx.doi.org/10.2166/wst.2001.0060.

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A theoretical study supported by some experimental tests has been carried out with the aim of comparing one-dimensional (1-D) biofilm reactor models that use simplified (zero- and first-order) and Monod kinetics. Two different situations have been compared: one rate-limiting substrate with or without liquid film diffusion. The results obtained show that the use of a simplified kinetic approach compared to the Monod kinetic approach determines (1) an unjustified overestimate of the removal rate, especially for thin biofilms, and (2) an excessive overestimate of the liquid film layer thickness necessary to justify high kinetic orders. Even if recent research projects show that biofilm structure is more complicated than the one assumed in the modelling approach used in this study, nevertheless 1-D models still now continue to be the only ones that can reasonably support process engineers in biofilm reactor design, due to their intrinsic simplicity and the need for small sets of input data and parameters that can be obtained theoretically or often empirically.

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Kamara, A., O. Bernard, A. Genovesi, D. Dochain, A. Benhammou, and J. P. Steyer. "Hybrid modelling of anaerobic wastewater treatment processes." Water Science and Technology 43, no. 1 (January 1, 2001): 43–50. http://dx.doi.org/10.2166/wst.2001.0011.

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This paper presents a hybrid approach for the modelling of an anaerobic digestion process. The hybrid model combines a feedforward network, describing the bacterial kinetics, and the a priori knowledge based on the mass balances of the process components. We have considered an architecture which incorporates the neural network as a static model of unmeasured process parameters (kinetic growth rate) and an integrator for the dynamic representation of the process using a set of dynamic differential equations. The paper contains a description of the neural network component training procedure. The performance of this approach is illustrated with experimental data.

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38

garfinkle, Moishe. "The thermodynamic natural path in chemical reaction kinetics." Discrete Dynamics in Nature and Society 4, no. 2 (2000): 145–64. http://dx.doi.org/10.1155/s1026022600000145.

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The Natural Path approach to chemical reaction kinetics was developed to bridge the considerable gap between the Mass Action mechanistic approach and the non-mechanistic irreversible thermodynamic approach. The Natural Path approach can correlate empirical kinetic data with a high degree precision, as least equal to that achievable by the Mass-Action rate equations, but without recourse mechanistic considerations. The reaction velocities arising from the particular rate equation chosen by kineticists to best represent the kinetic behavior of a chemical reaction are the natural outcome of the Natural Path approach. Moreover, by virtue of its thermodynamic roots, equilibrium thermodynamic functions can be extracted from reaction kinetic data with considerable accuracy. These results support the intrinsic validity of the Natural Path approach.

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Rohwer, Johann M. "Kinetic modelling of plant metabolic pathways." Journal of Experimental Botany 63, no. 6 (March 2012): 2275–92. http://dx.doi.org/10.1093/jxb/ers080.

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Baulch, D. L., C. J. Cobos, R. A. Cox, C. Esser, P. Frank, Th Just, J. A. Kerr, et al. "Evaluated Kinetic Data for Combustion Modelling." Journal of Physical and Chemical Reference Data 21, no. 3 (May 1992): 411–734. http://dx.doi.org/10.1063/1.555908.

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Mihajlovic, Ivan, Nada Strbac, and Zivan Zivkovic. "Kinetic modelling of chalcocite particle oxidation." Scandinavian Journal of Metallurgy 33, no. 6 (December 2004): 316–21. http://dx.doi.org/10.1111/j.1600-0692.2004.00700.x.

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Verdu, J., J. Rychly, and L. Audouin. "Synergism between polymer antioxidants—kinetic modelling." Polymer Degradation and Stability 79, no. 3 (March 2003): 503–9. http://dx.doi.org/10.1016/s0141-3910(02)00366-x.

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LINDSTEDT, R. P., and L. Q. MAURICE. "Detailed Kinetic Modelling of Toluene Combustion." Combustion Science and Technology 120, no. 1-6 (November 1996): 119–67. http://dx.doi.org/10.1080/00102209608935571.

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Dabrowski, François, Serge Bourbigot, René Delobel, and Michel Le Bras. "Kinetic modelling of the thermal degradation." European Polymer Journal 36, no. 2 (February 2000): 273–84. http://dx.doi.org/10.1016/s0014-3057(99)00079-8.

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Aszódi, A., and P. Friedrich. "Molecular kinetic modelling of associative learning." Neuroscience 22, no. 1 (July 1987): 37–48. http://dx.doi.org/10.1016/0306-4522(87)90196-5.

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Hofmeyr, Jan-Hendrik S. "Kinetic modelling of compartmentalised reaction networks." Biosystems 197 (November 2020): 104203. http://dx.doi.org/10.1016/j.biosystems.2020.104203.

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Stamatakis, Michail. "Kinetic modelling of heterogeneous catalytic systems." Journal of Physics: Condensed Matter 27, no. 1 (November 13, 2014): 013001. http://dx.doi.org/10.1088/0953-8984/27/1/013001.

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Mignon, Denis, Thomas Manth, and Hans Offermann. "Kinetic modelling of batch precipitation reactions." Chemical Engineering Science 51, no. 11 (June 1996): 2565–70. http://dx.doi.org/10.1016/0009-2509(96)00115-7.

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Kubička, David, Tapio Salmi, Marja Tiitta, and Dmitry Yu Murzin. "Ring-opening of decalin – Kinetic modelling." Fuel 88, no. 2 (February 2009): 366–73. http://dx.doi.org/10.1016/j.fuel.2008.09.004.

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Forchheim, Daniel, Ursel Hornung, Andrea Kruse, and Tatjana Sutter. "Kinetic Modelling of Hydrothermal Lignin Depolymerisation." Waste and Biomass Valorization 5, no. 6 (April 30, 2014): 985–94. http://dx.doi.org/10.1007/s12649-014-9307-6.

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