A single events microkinetic model for hydrocracking of vacuum gas oil

International audienceThe single events microkinetic modeling approach is extended to include saturated and unsaturated cyclic molecules, in addition to straight chained paraffins. The model is successfully applied to hydrocracking (HCK) of a hydrotreated Vacuum Gas Oil (VGO) residue in a pilot plant, under industrial operating conditions, on a commercial bi-functional catalyst. The molecular composition of the VGO feed is obtained by reconstruction based on a combination of analytical data (SIMDIS, GCxGC, mass spectroscopy). The necessary extensions to the single events methodology, which has previously only been applied to much simpler reacting systems (i.e. HCK of paraffins) are detailed in this work. Feeds typically used in the petrochemical industry typically contain a far more complex mixture of hydrocarbons, including cyclic species (i.e. naphtenes & aromatics). A more complex reaction network is therefore required in order to apply a single events model to such feeds. Hydrogenation, as well as endo-and exo-cyclic reactions have been added to the well-known acyclic β-scission and PCP-isomerization reactions. A model for aromatic ring hydrogenation was included in order to be able to simulate the reduction in aromatic rings, which is an important feature of HCK units. The model was then applied to 8 mass balances with a wide range of residue conversion (20 – 90%). The single events model is shown to be capable of correctly simulate the macroscopic effluent characteristics, such as residue conversion, yield structure, and weight distribution of paraffinic, naphthenic, and aromatic compounds in the standard cuts. This validates the overall model. The single events model provides far more detail about the fundamental chemistry of the system. This is shown in a detailed analysis of the reaction kinetics. The evolution of molecule size (i.e. carbon number), number of saturated/unsaturated rings, or the ratio of branched and un-branched species can be followed along the reactor. This demonstrates the explanatory power of this type of model. Calculations are performed on the IFPEN high performance computing cluster, with parallelization via MPI (message passing interface). This was very useful in order to reduce time consuming problems especially for the parameter fitting step.

Integration of an Informatics System in a High Throughput Experimentation. Description of a Global Framework Illustrated Through Several Examples.

International audienceHigh Throughput Experimentation (HTE) is a rapidly expanding field. However, the productivity gains obtained via the synthesis or parallel testing of catalysts may be lost due to poor data management (numerous manual inputs, information difficult to access, etc.). A global framework has then been developed. It includes the HTE pilot plants in the global information system. It produces dedicated computer tools offering spectacular time savings in the operation of HTE units, information storage and rapid extraction of relevant information. To optimize the productivity of engineers, Excel has been included in the system by adding specific features in order to treat it as an industrial tool (development of additional modules, update of modules, etc.). The success obtained by setting up the information system is largely due to the chosen development method. An Agile method (Agile Alliance (2012) http://www.agilealliance.org/the-alliance/)[1] was chosen since close collaboration between the computer specialists and the chemist engineers is essential. Rather than a global and precise description of the framework which might be boring and tedious, the global framework is presented through 3 examples: - scheduling experiments applied to zeolite synthesis; - data management (storage and access); - real application to pilot plant: dedicated interfaces to pilot and supervise HTE pilot plants, comparison of tests runs coming from several pilot plants

Raisonnement causal pour la supervision et la communication Homme Machine

Traité Ic2 Série Systèmes Automatisés. Ed. Hermès Sciences, 16 mars 2007. ISBN-13: 978-2746214108International audienc

Reducing the number of experiments required for modeling the hydrocracking process with kriging through Bayesian transfer learning

International audienceThe objective is to improve the learning of a regression model of the hydrocracking process using a reduced number of observations. When a new catalyst is used for the hydrocracking process, a new model must be fitted. Generating new data is expensive and therefore it is advantageous to limit the amount of new data generation. Our idea is to use a second dataset of measurements made on a process using an old catalyst. This second dataset is large enough to fit performing models for the old catalyst. In this work, we use the knowledge from this old catalyst to learn a model on the new catalyst. This task is a transfer learning task. We show that the results are greatly improved with a Bayesian approach to transfer linear model and kriging model

Modeling the hydrocracking process with kriging through Bayesian Transfer Learning

International audienc

Bayesian Transfer Learning to Improve Predictive Performance of an ODE-Based Kinetic Model

International audienceThis work focuses on the hydrodenitrogenation reaction modelisation. The removal of nitrogen-containing molecules is part of the hydrotreating process which is required before the catalytic conversion process. Impurities in the oil fraction are eliminated by mixing the feedstock with hydrogen and passing it through a fixed bed catalytic reactor at high temperature and pressure. Hydrotreating is a common operation in every oil refinery andallows satisfying the environmental regulations for the final products.Reactions take place in presence of catalyst and when supplying a catalyst, a vendor must guarantee its performance. The aim is to predict the nitrogen content (N) after the hydrotreating stage based on information on the feedstock and operating conditions (x). In order to model the nitrogen content evolution during the reaction, kinetic models (1) are used. The structure of f is fixed and it depends on parameters θ to be optimized.dNdt= fθ(N|x). (1)The predictive model fitting is based on experimental data and experiments are very expensive. New catalysts are constantly being developed so that each new generation of a catalyst requires a new model that is until now built from scratch from new experiments. The aim of this work is to build the best predictive model for a new catalyst from fewer observations and using the observations of previous generation catalysts. This task is known as transfer learning (Pan and Yang 2010 and Tsung et al. 2018).In order to adapt the past knowledge to the new catalyst, a Bayesian approach is considered. The Bayes Theorem gives the posterior distribution of the model parameters θ (2), where N is the vector of the nitrogen content for the different observations, X the matrix of new observations, π(θ) the prior distribution of parameters, L(N|θ,X) the likelihood and L(N|X) the marginal likelihood.π(θ|N,X) =π(θ)L(N|θ,X)L(N|X). (2)The likelihood represents the knowledge about the new observations, thus the posterior distribution will be modified when adding observations. The idea of the approach is to take as prior π(θ) a distribution centered on the previous model parameters, with variance large enough to allow parameter change and small enough to retain the information. Previous work has shown the effectiveness of this Bayesian transfer approach for modelling using linear models and kriging models (Iapteff et al. 2021), and this current work aims to adapt it on kinetic models. Results are good and show a reduction in the number of observations required to achieve good results

Hydrotreatment modeling for a variety of VGO feedstocks : a continuous lumping approach.

International audienceThe Hydrotreatment of different VGO feedstocks in a pilot plant is modeled using the continuous lumping approach. A model with five continuous families (Parrafins, Naphtenes, sulfur-containing Aromatics, Nitrogen-containing Aromatics, and Sulfur and Nitrogen free aromatics) is proposed and validated in this work. The model considers the five families to be a continuous distribution. A reaction network, including hydrogenation, desulfurization, denitrogenization, and cracking is established and used to derive the kinetic rate expressions for each family. A total of 46 model parameters are adjusted, using data from 44 experimental runs, using a pilot plant. Three feedstocks of different origin and thus different characteristics (e.g. True Boiling Point, sulfur- and nitrogen content) were used in this this study. The same set of parameters was used to simulate hydrotreatment of all three feedstocks, which extends the range of applicability of the model beyond the models wich are typically proposed in literature, which rely on feedstock-specific parameters. The results show that this modeling approach is capable of accurately predicting the total hydrocarbon conversion and yield regardless of feed composition. Furthermore, insights into the underlying reaction mechanisms can be gained from the kinetic parameters

Bayesian Transfer Learning for modelling the hydrocracking process using kriging

International audienc

Planification d'expériences dans l'industrie chimique

Chap.

Planification d'expériences dans l'industrie chimique

National audienc