Bibliografias temáticas / Kinetic modelling

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Literatura científica selecionada sobre o tema "Kinetic modelling"

Autor: Grafiati
Publicado: 4 de junho de 2021
Última modificação: 14 de março de 2023

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Artigos de revistas sobre o assunto "Kinetic modelling"

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Provis, J. L., e J. S. J. van Deventer. "Geopolymerisation kinetics. 2. Reaction kinetic modelling". Chemical Engineering Science 62, n.º 9 (maio de 2007): 2318–29. http://dx.doi.org/10.1016/j.ces.2007.01.028.

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

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Gotch, F. A. "Urea kinetic modelling". Nephrology Dialysis Transplantation 10, n.º 12 (dezembro de 1995): 2378–79. http://dx.doi.org/10.1093/ndt/10.12.2378.

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4

L Salami, DO Olumuyiwa, EA Alfred e OS Olakanmi. "Kinetic modelling of dumpsite leachate treatment using Musa sapientum peels as bio-sorbent". Global Journal of Engineering and Technology Advances 9, n.º 2 (30 de novembro de 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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Symak, Dmytro, Vira Sabadash, Jaroslaw Gumnitsky e Zoriana Hnativ. "Kinetic Regularities and Mathematical Modelling of Potassium Chloride Dissolution". Chemistry & Chemical Technology 15, n.º 1 (15 de fevereiro de 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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Rossetti, Ilenia, Francesco Conte e Gianguido Ramis. "Kinetic Modelling of Biodegradability Data of Commercial Polymers Obtained under Aerobic Composting Conditions". Eng 2, n.º 1 (20 de fevereiro de 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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Zalazar, C. S., M. D. Labas, M. E. Lovato, R. J. Brandi e A. E. Cassano. "Modelling the kinetics of UV/H2O2 oxidation of dichloroacetic acid". Water Science and Technology 55, n.º 12 (1 de junho de 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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Lo Schiavo, M. "Kinetic modelling andelectoral competition". Mathematical and Computer Modelling 42, n.º 13 (dezembro de 2005): 1463–86. http://dx.doi.org/10.1016/j.mcm.2004.11.006.

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Dhir, S., R. Uppaluri e M. K. Purkait. "Oxidative desulfurization: Kinetic modelling". Journal of Hazardous Materials 161, n.º 2-3 (janeiro de 2009): 1360–68. http://dx.doi.org/10.1016/j.jhazmat.2008.04.099.

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Frontistis, Z., M. Papadaki e D. Mantzavinos. "Modelling of sonochemical processes in water treatment". Water Science and Technology 55, n.º 12 (1 de junho de 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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Teses / dissertações sobre o assunto "Kinetic modelling"

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Munkondya, Ferguson Mukozoke. "Kinetic modelling of leaching". Thesis, Imperial College London, 1989. http://hdl.handle.net/10044/1/47583.

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Johansson, David. "Kinetic modelling of autoignition phenomena". Licentiate thesis, Stockholm : Kemiteknik, Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4516.

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Gilliland, David. "Kinetic modelling of preservative systems". Thesis, Queen's University Belfast, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334622.

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Marsano, Flavio. "Chemical kinetic modelling of hydrocarbon combustion". Thesis, Cardiff University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.402067.

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Potter, Mark Lee. "Detailed chemical kinetic modelling of propulsion fuels". Thesis, Imperial College London, 2004. http://hdl.handle.net/10044/1/7995.

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Hagey, H. Louis. "Kinetic modelling of synthesis gas into hydrocarbons". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ58214.pdf.

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Rizos, Konstantinos-Athanassios. "Detailed chemical kinetic modelling of homogeneous systems". Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407143.

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Binns, Michael John. "Kinetic modelling of chemical and biochemical networks". Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496236.

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The modelling of chemical and biochemical systems is highly dependent on the reaction network of the system. A reaction network should contain all the reactions which can occur in addition to the species involved. However in many cases there will be reactions occurring which are missing from the network. These are called gaps (Reed et al., 2003; Duarte et al, 2004) and occur because the databases of reactions used to build them are incomplete. The main aim of this work is to identify all the reactions which can occur in a given system including the unknown ones. This is accomplished through a new procedure which will be called metabolic network development and is aimed at constructing reaction networks for biochemical systems.
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Reece, Christian. "Kinetic analysis and modelling in heterogeneous catalysis". Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/103737/.

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A combination of Temporal Analysis of Products, Temperature Programmed Reduction, and Density Functional theory techniques have been used to perform kinetic analysis on data from heterogeneous catalysis experiments. A new method of data filtering has been developed for Temporal Analysis of Products, and has been applied to a system of 4 Pt−Mo2C, and the current methodology has been expanded upon to calculate rate coefficients for the oxidation of CO to CO2 via the Boudard reaction. From the kinetic constants it appears that a phase change occurs in the material at approximately 200�C. The current theory for analysing Temperature Programmed Reduction has been applied in a new methodology which is able to perform the deconvolution of thermograms with high accuracy, while also calculating the kinetic parameters related to the reduction processes. This new methodology has been applied to a system of CeO2 calcined at 400, 500 and 600�C and the strengths and limitations of the methodology are explored. From the deconvolution procedure it was found that there are three distinct reduction processes occurring on the CeO2 and that a phase change occurs between 400 and 500�C. Finally Density Functional Theory combined with classical dynamics has been used to explore the mechanism of the hydrogenation of Levulinic Acid to gamma-Valerolactone over a CuZrO2 catalyst. It was found that the Levulinic Acid is more likely to hydrogenate then cyclise, and from using molecular dynamics simulations it was shown that the solvent H2O plays a very important role in the cyclisation of the hydrogenated intermediate.
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Hameed, Samreen. "Kinetic Analysis and Modelling of Cellulose Pyrolysis". Thesis, Curtin University, 2019. http://hdl.handle.net/20.500.11937/75984.

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Pyrolysis is one of the thermochemical techniques to convert biomass into bio-oil, bio-char and gaseous products. This work has focused on modelling the kinetic behaviour of cellulose, which is the most abundant component in any type of biomass, under slow and fast pyrolysis conditions and in the presence of inorganic species using Distributed Activation Energy Model (DAEM). In addition, cellulose pyrolysis product yield has been estimated using Computational Fluid Dynamics (CFD) modelling framework.
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Livros sobre o assunto "Kinetic modelling"

1

G, Compton R., e Hancock G, eds. Applications of kinetic modelling. Amsterdam: Elsevier, 1999.

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Demin, Oleg. Kinetic modelling in systems biology. Boca Raton: Chapman & Hall/CRC, 2009.

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3

Boekel, Tiny Van. Kinetic modelling of reactions in foods. Boca Raton: Taylor & Francis, 2008.

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4

Lorenzo, Pareschi, e Russo Giovanni, eds. Modelling and numerics of kinetic dissipative systems. Hauppauge, N.Y: Nova Science Publishers, 2005.

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Knolle, Helmut. Cell Kinetic Modelling and the Chemotherapy of Cancer. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-45651-0.

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Cell kinetic modelling and the chemotherapy of cancer. Berlin: Springer-Verlag, 1988.

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7

N, Bellomo, e Pulvirenti M. 1946-, eds. Modeling in applied sciences: A kinetic theory approach. Boston: Birkhäuser, 2000.

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8

Canada Centre for Mineral and Energy Technology. Spoc Simulated Processing of Ore and Coal: Chapter 4.1 Industrial Ball Mill Modelling : Industrial Ball Mill Modelling: Documented Application of the Kinetic Model. S.l: s.n, 1985.

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9

Harmsen, Johannes Maria Antonius. Kinetic modelling of the dynamic behaviour of an automotive three-way catalyst under cold-start conditions. Eindhoven: Technische Universiteit Eindhoven, 2001.

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10

Bjorn, Kristiansen, e Bu'Lock J. D, eds. Fermentation kinetics and modelling. Milton Keynes: Open University Press, 1987.

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Capítulos de livros sobre o assunto "Kinetic modelling"

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van den Hoff, Jörg. "Kinetic Modelling". In Small Animal Imaging, 387–403. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-12945-2_27.

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Lammertsma, Adriaan A. "Tracer Kinetic Modelling". In PET and SPECT in Neurology, 59–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54307-4_3.

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Aliev, Yuri M., Hans Schüter e Antonia Shivarova. "Kinetic Numerical Modelling". In Guided-Wave-Produced Plasmas, 205–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57060-5_6.

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Lammertsma, Adriaan A. "Tracer Kinetic Modelling". In PET and SPECT in Neurology, 37–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53168-3_2.

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Lemos, F., M. A. N. D. A. Lemos, I. S. Silva, C. Costa e M. M. Marques. "Modelling Complex Kinetic Systems". In Combinatorial Catalysis and High Throughput Catalyst Design and Testing, 175–204. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-011-4329-5_6.

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Lemos, F., C. Costa, J. M. Lopes, C. Pinheiro, X. Wang e F. Ramôa Ribeiro. "Modelling Complex Kinetic Systems". In Combinatorial Catalysis and High Throughput Catalyst Design and Testing, 205–38. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-011-4329-5_7.

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Sudar, Martina, e Zvjezdana Findrik Blažević. "Enzyme Cascade Kinetic Modelling". In Enzyme Cascade Design and Modelling, 91–108. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65718-5_6.

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Cannaerts, Corneel. "Kinetic Pavilion Extendible and Adaptable Architecture". In Computational Design Modelling, 335–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23435-4_38.

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Ramos, M. Piedade M., C. Ribeiro e Ana Jacinta Soares. "Kinetic Modelling of Autoimmune Diseases". In Recent Advances in Kinetic Equations and Applications, 309–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82946-9_13.

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Grzegorzewska, Alicja E., Ahmad Taher Azar, Laura M. Roa, J. Sergio Oliva, José A. Milán e Alfonso Palma. "Single Pool Urea Kinetic Modeling". In Modelling and Control of Dialysis Systems, 563–626. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-27458-9_12.

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Trabalhos de conferências sobre o assunto "Kinetic modelling"

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Declercq, Julien, Rob Bowell e Michael Herrell. "Environmental Impacts Predictions via Kinetic Modelling". In Goldschmidt2020. Geochemical Society, 2020. http://dx.doi.org/10.46427/gold2020.536.

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Ranada Shaw, A., D. Van der Burg, A. I. M. Denneman e C. P. A. Wapenaar. "Forward Modelling for Electro-Kinetic Effects". In 63rd EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2001. http://dx.doi.org/10.3997/2214-4609-pdb.15.p031.

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Declercq, Julien, Michael Herrell e Robert Bowell. "Environmental Impacts Predictions via Kinetic Modelling". In Goldschmidt2021. France: European Association of Geochemistry, 2021. http://dx.doi.org/10.7185/gold2021.6801.

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Gatignol, R. "Kinetic Modelling of a Heterogeneous Dispersed Medium". In RAREFIED GAS DYNAMICS: 24th International Symposium on Rarefied Gas Dynamics. AIP, 2005. http://dx.doi.org/10.1063/1.1941512.

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Mendes, Pedro, Natalie J. Stanford e Kieran Smallbone. "Kinetic modelling of large-scale metabolic networks". In the 9th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2037509.2037511.

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Lachowicz, Mirosław. "On bilinear kinetic equations. Between micro and macro descriptions of biological populations". In Mathematical Modelling of Population Dynamics. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2003. http://dx.doi.org/10.4064/bc63-0-10.

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Seniūnaitė, Jurgita, Rasa Vaiškūnaitė e Kristina Bazienė. "Mathematical Modelling for Copper and Lead Adsorption on Coffee Grounds". In Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.007.

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Research studies on the adsorption kinetics are conducted in order to determine the absorption time of heavy metals on coffee grounds from liquid. The models of adsorption kinetics and adsorption diffusion are based on mathe-matical models (Cho et al. 2005). The adsorption kinetics can provide information on the mechanisms occurring be-tween adsorbates and adsorbents and give an understanding of the adsorption process. In the mathematical modelling of processes, Lagergren’s pseudo-first- and pseudo-second-order kinetics and the intra-particle diffusion models are usually applied. The mathematical modelling has shown that the kinetics of the adsorption process of heavy metals (copper (Cu) and lead (Pb)) is more appropriately described by the Lagergren’s pseudo-second-order kinetic model. The kinetic constants (k2Cu = 0.117; k2Pb = 0,037 min−1) and the sorption process speed (k2qeCu = 0.0058–0.4975; k2qePb = 0.021–0.1661 mg/g per min) were calculated. After completing the mathematical modelling it was calculated that the Langmuir isotherm better reflects the sorption processes of copper (Cu) (R2 = 0.950), whilst the Freundlich isotherm – the sorption processes of lead (Pb) (R2 = 0.925). The difference between the mathematically modelled and experimen-tally obtained sorption capacities for removal of heavy metals on coffee grounds from aqueous solutions is 0.059–0.164 mg/l for copper and 0.004–0.285 mg/l for lead. Residual concentrations of metals in a solution showed difference of 1.01 and 0.96 mg/l, respectively.
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Musin, Timur. "KINETIC MODELLING OF HYDROLYZED LIGNIN PYROLYSIS USING THERMAL ANALYSIS". In 17th International Multidisciplinary Scientific GeoConference SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgem2017h/43/s18.013.

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BARBERIS, MATTEO, THOMAS W. SPIESSER e EDDA KLIPP. "KINETIC MODELLING OF DNA REPLICATION INITIATION IN BUDDING YEAST". In Proceedings of the 10th Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2010). IMPERIAL COLLEGE PRESS, 2010. http://dx.doi.org/10.1142/9781848166585_0001.

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Ramakrishnan, Sivakumar, Teong Chen Kwok e Sheikh Abdul Rezan Sheikh Abdul Hamid. "Kinetic modelling of chlorination of nitrided ilmenite using MATLAB". In THE 2ND INTERNATIONAL CONFERENCE ON FUNCTIONAL MATERIALS AND METALLURGY (ICoFM 2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4958773.

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Relatórios de organizações sobre o assunto "Kinetic modelling"

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Mousseau, Vincent Andrew. Fully implicit kinetic modelling of collisional plasmas. Office of Scientific and Technical Information (OSTI), maio de 1996. http://dx.doi.org/10.2172/239296.

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Sandermann, Heinrich, Duncan Jr. e Thomas M. Lipid-Dependent Membrane Enzymes. Kinetic Modelling of the Activation of Protein Kinase C by Phosphatidylserine. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 1991. http://dx.doi.org/10.21236/ada302987.

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Issler, D. R., e L. S. Lane. Report of activities for the GEM-2 multi-kinetic apatite fission track (MK-AFT) modelling and method development. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/299247.

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Waganet, R. J., John Duxbury, Uri Mingelgrin, John Hutson e Zev Gerstl. Consequences of Nonequilibrium Pesticide Fate Processes on Probability of Leaching from Agricultural Lands. United States Department of Agriculture, janeiro de 1994. http://dx.doi.org/10.32747/1994.7568769.bard.

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Pesticide leaching in heterogeneous field soils is relatively unstudied and is the focus of this project. A wide variety of heterogeneous soils exist, characterized by processes that result from the presence of structural cracks, worm holes, and other preferred pathways within which the majority of transport can occur (called physical non-equilibrium processes), along with the presence of sorption processes that are both equilibrium and kinetic (chemical non-equilibrium processes). Previous studies of pesticide leaching have focused primarily on relatively homogeneous soils, which are less widely distributed in nature, but more studied due to the relative ease with which quantitative theory can be applied to interpret experimental results. The objectives of the proposed project were: first, to gain greater insight into the basic physical and chemical processes that characterize non-equilibrium systems, second, to improve our ability to predict pesticide leaching in heterogeneous field soils, and third, to estimate the consequences of non-equilibrium processes at the field scale by conducting an analysis of the probability of pesticide leaching when non-equilibrium processes prevail. The laboratory, theoretical and modelling aspects of the project were successful; the field aspects less so. We gained greater insight into basic processes in heterogeneous field soils, and we improved and tested tools (simulation models) and the methodology of using such tools for assessing the probability of pesticide leaching as a contribution to broader risk analysis efforts.
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B. Widman. Modelling Mixed Bed Ion Exchange Kinetics for Removal of Trace Levels of Divalent Cations in Ultrapure Water. Office of Scientific and Technical Information (OSTI), janeiro de 2003. http://dx.doi.org/10.2172/822270.

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PASSALACQUA, Roberto, e Rossella BOVOLENTA. Integration of spatially distributed data for the 3-D modelling of a kinematic phenomenon in Italy. Cogeo@oeaw-giscience, setembro de 2011. http://dx.doi.org/10.5242/iamg.2011.0185.

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Mukhopadhyay, P. K. Organic petrography and kinetics of limestone and shale source rocks in wells adjacent to Sable Island, Nova Scotia and the interpretation on oil-oil or oil-source rock correlation and basin modelling. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1995. http://dx.doi.org/10.4095/205325.

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Perdigão, Rui A. P., e Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, novembro de 2020. http://dx.doi.org/10.46337/201111.

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Resumo:
Causality and Predictability of Complex Systems pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known. However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic and information-theoretic metrics to provide reliable information about the non-recurrent emergence of fundamental new properties absent from the a priori kinematic geometric and statistical features. Unveiling causal mechanisms and eliciting system dynamic predictability under such challenging conditions is not only a fundamental problem in mathematical and statistical physics, but also one of critical importance to dynamic modelling, risk assessment and decision support e.g. regarding non-recurrent critical transitions and extreme events. In order to address these challenges, generalized metrics in non-ergodic information physics are hereby introduced for unveiling elusive dynamics, causality and predictability of complex dynamical systems undergoing far-from-equilibrium structural-functional coevolution. With these methodological developments at hand, hidden dynamic information is hereby brought out and explicitly quantified even beyond post-critical regime collapse, long after statistical information is lost. The added causal insights and operational predictive value are further highlighted by evaluating the new information metrics among statistically independent variables, where traditional techniques therefore find no information links. Notwithstanding the factorability of the distributions associated to the aforementioned independent variables, synergistic and redundant information are found to emerge from microphysical, event-scale codependencies in far-from-equilibrium nonlinear statistical mechanics. The findings are illustrated to shed light onto fundamental causal mechanisms and unveil elusive dynamic predictability of non-recurrent critical transitions and extreme events across multiscale hydro-climatic problems.
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