What features characterise complex system dynamics? Power laws and scale invariance of fluctuations are often taken as the hallmarks of complexity, drawing on analogies with equilibrium critical phenomena[1-3]. Here we argue that slow, directed dynamics, during which the system's properties change significantly, is fundamental. The underlying dynamics is related to a slow, decelerating but spasmodic release of an intrinsic strain or tension. Time series of a number of appropriate observables can be analysed to confirm this effect. The strain arises from local frustration. As the strain is released through "quakes", some system variable undergoes record statistics with accompanying log-Poisson statistics for the quake event times[4]. We demonstrate these phenomena via two very different systems: a model of magnetic relaxation in type II superconductors and the Tangled Nature model of evolutionary ecology, and show how quantitative indications of ageing can be found.Comment: 8 pages, 5 figures all in one fil

-In this paper, kinetic modeling techniques for complex chemical processes are reviewed. After a brief historical overview of chemical kinetics, an overview is given of the theoretical background of kinetic modeling of elementary steps and of multistep reactions. Classic lumping techniques are introduced and analyzed. Two examples of lumped kinetic models (atmospheric gasoil hydrotreating and residue hydroprocessing) developed at IFP Energies nouvelles (IFPEN) are presented. The largest part of this review describes advanced kinetic modeling strategies, in which the molecular detail is retained, i.e. the reactions are represented between molecules or even subdivided into elementary steps. To be able to retain this molecular level throughout the kinetic model and the reactor simulations, several hurdles have to be cleared first: (i) the feedstock needs to be described in terms of molecules, (ii) large reaction networks need to be automatically generated, and (iii) a large number of rate equations with their rate parameters need to be derived. For these three obstacles, molecular reconstruction techniques, deterministic or stochastic network generation programs, and single-event micro-kinetics and/or linear free energy relationships have been applied at IFPEN, as illustrated by several examples of kinetic models for industrial refining processes.Résumé -Une revue de méthodes de modélisation cinétique pour des procédés complexesDans cet article, les techniques de modélisation cinétique des processus chimiques complexes sont examinées. Après un bref aperçu historique de la cinétique chimique, un aperçu des bases théoriques de la modélisation cinétique d'étapes élémentaires et de réactions globales est présenté. Les techniques classiques de regroupement (lumping) sont ensuite présentées et analysées. Deux exemples de modèles cinétiques regroupés (pour l'hydrotraitement de gazole atmosphérique et pour l'hydrotraitement de résidus) développés à IFP Energies nouvelles (IFPEN) sont présentés. La plus grande partie de cette revue décrit des stratégies avancées de modélisation cinétique, dans lesquelles le détail moléculaire est retenu : les réactions entre les molécules sont représentées ou même subdivisées en étapes élémentaires. Pour être en mesure de conserver ce niveau moléculaire à la fois dans le modèle cinétique et dans les simulations de réacteurs, plusieurs obstacles doivent d'abord être éliminés : (i) la charge doit être décrite en termes de molécules, (ii) les grands réseaux réactionnels doivent être générés automatiquement et (iii) un grand nombre d'équations de vitesse avec leurs paramètres de vitesse doit être dérivé. Pour ces trois obstacles, des techniques de reconstruction moléculaire, des programmes de génération de réseaux déterministes ou stochastiques, et des modèles microcinétiques basés sur des événements constitutifs (single events) et/ou des

We use Monte Carlo simulations of a coarse-grained three dimensional model to demonstrate that the experimentally observed approximate temperature independence of the magnetic creep rate for a broad range of temperatures may be explained in terms of record dynamics, viz. the dynamical properties of the times at which a stochastic fluctuating signal establishes records.

A vacuum residue is a complex hydrocarbon mixture of several thousand different chemical species. Even today, no analytical technique is powerful enough to obtain the molecular detail that is required for the development of a detailed kinetic model. To overcome this drawback, a two-step reconstruction algorithm has been developed to build a representative set of molecules from partial analytical data. The first step, called Stochastic Reconstruction (SR), creates an initial mixture of molecules by a Monte Carlo sampling method. The second step, termed Reconstruction by Entropy Maximization (REM), modifies the molar fractions of the molecules in order to improve the representativeness of the generated mixture. The combined SR-REM algorithm creates a synthetic blend of molecules whose mixture properties are close to the analytical data of the petroleum fraction. The method has been applied to petroleum vacuum residue fractions from four different geographic locations with substantially different compositions. All cases are well represented, clearly illustrating the versatility of the SR-REM method. As an extension to this base algorithm, a novel indirect two-step reconstruction algorithm was developed, in which the SR step is used to build a single reference mixture. The set of molecules thus obtained is subsequently used in the second step to represent various petroleum fractions via the REM method. This allows to simultaneously reduce the computational burden and to represent the vacuum residue fractions with the same set of molecules. To validate this alternative approach, eight vacuum residues from different origins have been reconstructed with this technique. The results in terms of analysis prediction have shown a very good agreement.

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Twenty-three gas oil samples from different origins were analyzed in positive and negative ion modes by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI(±)-FT-ICR MS). Sample ionization and ion transfer conditions were first optimized using Design of Experiment approach. Advanced characterization of basic and neutral nitrogen compounds in these samples was then performed through ESI(±)-FT-ICR MS analysis. A good repeatability was observed from the analysis of six replicates for each gas oil sample. Significant differences in molecular composition were spotted between the gas oils, either considering identified heteroatomic classes or within nitrogen families and were later correlated to samples macroscopic properties. The evolution of nitrogen relative intensities for one feed and two corresponding effluents has also been studied to monitor hydrotreatment reaction pathways toward aromaticity and alkylation levels evolutions.

Sulfur content in gas oils is strictly regulated by legal specifications for environmental reasons. Gas oils are composed of various aromatic sulfur compounds, and some of them are known to be very refractory for sulfur removal reactions. Thus, an accurate analysis of sulfur compounds is important to find the appropriate operating conditions of the gas oil hydrotreating processes. Aromatic sulfur compounds contained in 23 gas oils samples were analyzed using APPI(+)-FT-ICR MS considering six replicates. Significant differences were spotted within several processed gas oils. A comparison of one feed and its corresponding effluents also confirmed the well-known refractory character of sulfur compounds such as polyalkylated dibenzothiophenes. To go deeper in the molecular exploration, chemometric tools were applied on this spectral data set including principal component analysis (PCA) and hierarchical cluster analysis (HCA). A unique data rearrangement was performed directly inspired on DBE vs carbon number plots that are systematically used in petroleomics studies. Then, these chemometric tools provided a successful classification of each type of gas oils. The PCA model has also been validated on mixed blends allowing us to conclude that it could be applied to unknown samples in order to identify the process used to produce them. Moreover, the exploration of the generated loadings revealed key types of molecules driving the classification such as C3-DBT which is a dibenzothiophene core with three additional carbon atoms. Indeed, it is known to remain mainly in deeply hydrotreated samples, validating previous observations regarding its potential refractory character. The ability of chemometric tools to extract specific molecular information from ultra-high resolution MS spectra reveals its huge potential for an exhaustive study of highly complex mixtures such as crude oils.