Liquid spreading in trickle-bed reactors: Experiments and numerical simulations using Eulerian--Eulerian two-fluid approach

Liquid spreading in gas-liquid concurrent trickle-bed reactors is simulated using an Eulerian twofluid CFD approach. In order to propose a model that describes exhaustively all interaction forces acting on each fluid phase with an emphasis on dispersion mechanisms, a discussion of closure laws available in the literature is proposed. Liquid dispersion is recognized to result from two main mechanisms: capillary and mechanical (Attou and Ferschneider, 2000; Lappalainen et al., 2009- The proposed model is then implemented in two trickle-bed configurations matching with two experimental set ups: In the first configuration, simulations on a 2D axisymmetric geometry are considered and the model is validated upon a new set of experimental data. Overall pressure drop and liquid distribution obtained from $\gamma$-ray tomography are provided for different geometrical and operating conditions. In the second configuration, a 3D simulation is considered and the model is compared to experimental liquid flux patterns at the bed outlet. A sensitivity analysis of liquid spreading to bed geometrical characteristics (void-fraction and particles diameter) as well as to gas and liquid flow rates is proposed. The model is shown to achieve very good agreement with experimental data and to predict, accurately, tendencies of liquid spreading for various geometrical bed characteristics and/or phases flow-rates

Modeling of Trickle-Bed Reactors with Exothermic Reactions using Cell Network Approach

One-Dimensional (1D) and Two-Dimensional (2D) Cell Network Models Were Developed to Simulate the Steady-State Behavior of Trickle-Bed Reactors Employed for the Highly Exothermic Hydrotreating of Benzene. the Multiphase Mass Transfer-Reaction Model and Novel Solution Method Are Discussed in This Report. the 1D Model Was Shown to Satisfactorily Simulate the Axial Temperature Field Observed Experimentally for Multiphase Flow with Exothermic Reactions. the 2D Reactor Modeling Provided Valuable Information About Local Hot Spot Behavior within the Multiphase Reactor, Identifying Situations in Which Hot Spots May Form. the Model Took into Consideration the Heterogeneous Nature of Liquid Distribution, Including Radial Liquid Maldistribution and Partial External Wetting. This Approach Was Proven to Be Stable and Efficient in Dealing with the Complex Interaction of Phase Vaporization and Temperature Rise. through Analysis and Discussion, This Report Established the Cell Network Model as a Valid Representation of the Flow Environment Produced in a Trickle Bed with Exothermic Reactions. © 2007 Elsevier Ltd. All Rights Reserved

Adaptive swarm optimisation assisted surrogate model for pipeline leak detection and characterisation.

Pipelines are often subject to leakage due to ageing, corrosion and weld defects. It is difficult to avoid pipeline leakage as the sources of leaks are diverse. Various pipeline leakage detection methods, including fibre optic, pressure point analysis and numerical modelling, have been proposed during the last decades. One major issue of these methods is distinguishing the leak signal without giving false alarms. Considering that the data obtained by these traditional methods are digital in nature, the machine learning model has been adopted to improve the accuracy of pipeline leakage detection. However, most of these methods rely on a large training dataset for accurate training models. It is difficult to obtain experimental data for accurate model training. Some of the reasons include the huge cost of an experimental setup for data collection to cover all possible scenarios, poor accessibility to the remote pipeline, and labour-intensive experiments. Moreover, datasets constructed from data acquired in laboratory or field tests are usually imbalanced, as leakage data samples are generated from artificial leaks. Computational fluid dynamics (CFD) offers the benefits of providing detailed and accurate pipeline leakage modelling, which may be difficult to obtain experimentally or with the aid of analytical approach. However, CFD simulation is typically time-consuming and computationally expensive, limiting its pertinence in real-time applications. In order to alleviate the high computational cost of CFD modelling, this study proposed a novel data sampling optimisation algorithm, called Adaptive Particle Swarm Optimisation Assisted Surrogate Model (PSOASM), to systematically select simulation scenarios for simulation in an adaptive and optimised manner. The algorithm was designed to place a new sample in a poorly sampled region or regions in parameter space of parametrised leakage scenarios, which the uniform sampling methods may easily miss. This was achieved using two criteria: population density of the training dataset and model prediction fitness value. The model prediction fitness value was used to enhance the global exploration capability of the surrogate model, while the population density of training data samples is beneficial to the local accuracy of the surrogate model. The proposed PSOASM was compared with four conventional sequential sampling approaches and tested on six commonly used benchmark functions in the literature. Different machine learning algorithms are explored with the developed model. The effect of the initial sample size on surrogate model performance was evaluated. Next, pipeline leakage detection analysis - with much emphasis on a multiphase flow system - was investigated in order to find the flow field parameters that provide pertinent indicators in pipeline leakage detection and characterisation. Plausible leak scenarios which may occur in the field were performed for the gas-liquid pipeline using a three-dimensional RANS CFD model. The perturbation of the pertinent flow field indicators for different leak scenarios is reported, which is expected to help in improving the understanding of multiphase flow behaviour induced by leaks. The results of the simulations were validated against the latest experimental and numerical data reported in the literature. The proposed surrogate model was later applied to pipeline leak detection and characterisation. The CFD modelling results showed that fluid flow parameters are pertinent indicators in pipeline leak detection. It was observed that upstream pipeline pressure could serve as a critical indicator for detecting leakage, even if the leak size is small. In contrast, the downstream flow rate is a dominant leakage indicator if the flow rate monitoring is chosen for leak detection. The results also reveal that when two leaks of different sizes co-occur in a single pipe, detecting the small leak becomes difficult if its size is below 25% of the large leak size. However, in the event of a double leak with equal dimensions, the leak closer to the pipe upstream is easier to detect. The results from all the analyses demonstrate the PSOASM algorithm's superiority over the well-known sequential sampling schemes employed for evaluation. The test results show that the PSOASM algorithm can be applied for pipeline leak detection with limited training datasets and provides a general framework for improving computational efficiency using adaptive surrogate modelling in various real-life applications

Neural Networks for Flow Bottom Hole Pressure Prediction

Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps.  However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells.  The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and for the sake of qualitative comparison, Radial Basis neural and network and the empirical modes have been developed. The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 12.5% of the total data sets to give 3.4 RMSE and 97% of the test data achieved 90% accuracy

Modelling of an axial flow compact separator using neural network

A novel design axial flow cyclonic separator called I-SEP was tested with an extensive set of experiments using air-water two phase flow mixture at atmospheric pressure. These experiments provided valuable data on the separation efficiency and pressure drop under different inlet conditions. The performance parameters i.e. Gas Carry Under (GCU) and Liquid Carry Over (LCO) were found to be non-linearly related to the inlet operating conditions. However it was found that resistance on the tangential outlet of the I-SEP affects the GCU and that manipulating the pressure difference between the two outlets and the inlet of the I-SEP through manual control valves, the GCU could be controlled. The separator was also extensively tested and compared with a gravity separator, when they were placed at the exit of a riser, in severe slugging condition frequently encountered in the production pipe work from some oil fields. The tests revealed that the I-SEP has better tendency to suppress severe slugging as compared to the gravity separator. A framework for neural network based on multiple types of input was also developed to model the separation performance of the I-SEP. Mutual Information (one of the key elements of the information theory) was applied to select the appropriate candidate input variables to the neural network framework. This framework was then used to develop a neural network model based on dimensionless input parameters such as pressure coefficient. This neural network model produced satisfactory prediction on unseen experimental data. The inverse function of a trained neural network was combined with a PID controller in a closed loop to control the GCU and LCO at a given set point by predicting the manipulating variable i.e. pressure at the I-SEP outlets. This control scheme was simulated using the test data. Such controller could be used to assist the operator in maintaining and controlling the GCU or LCO at the I-SEP outlets.The work performed during this study also includes the development of a data repository system to store and query the experimental result. An internet based framework is also developed that allows remote access of the experimental data using internet or wireless mobile devices

Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos

A surrogate model approximates a computationally expensive solver. Polynomial Chaos is a method to construct surrogate models by summing combinations of carefully chosen polynomials. The polynomials are chosen to respect the probability distributions of the uncertain input variables (parameters); this allows for both uncertainty quantification and global sensitivity analysis. In this paper we apply these techniques to a commercial solver for the estimation of peak gas rate and cumulative gas extraction from a coal seam gas well. The polynomial expansion is shown to honour the underlying geophysics with low error when compared to a much more complex and computationally slower commercial solver. We make use of advanced numerical integration techniques to achieve this accuracy using relatively small amounts of training data

The use of the bimodal production decline curve for the analysis of hydraulically fractured shale/tight gas reservoirs

The capability to conduct a rapid, near real-time model-based analysis of production data from tight/shale (TS) gas fields is important in determining fracture and matrix properties. Model-based analysis of production can range from simple analytical solutions to complex numerical models. The objective of this study is to develop a simple, Excel-based tool for the analysis of the complex problem of gas production from a fractured TS gas reservoir that is based on a robust model that is faithful to the underlying physics and can provide rapid estimates of the important system parameters. The scientifically robust model used as the basis for this tool is a significant modification and expansion of the bimodal production decline curve of Silin and Kneafsey (2012). The production period is divided into two regimes: an early-time regime before the extent of the stimulated reservoir volume (SRV) is felt, where an analytical similarity solution for gas production rate is obtained, and a late-time regime where the rate can be approximated with an exponential decline or more accurately represented with a numerical integration. Our basic model follows Silin and Kneafsey (2012) and produces the widely observed -½ slope on a log-log plot of early-time production decline curves, while our expanded model generalizes this slope to –n, where 0 < n < 1, to represent non-ideal flow geometries. The expanded model was programmed into an Excel spreadsheet to develop an interactive, user-friendly application for curve matching of well production data to the bimodal curve, from which matrix and fracture properties can be extracted. This tool allows significant insight into the model parameters that control the reservoir behavior and production: the geometry of the hydraulically-induced fracture network, its flow and transport properties, and the optimal operational parameters. This information enables informed choices about future operations, and is valuable in several different ways: (a) to estimate reserves and to predict future production, including expected ultimate recovery and the useful lifetime of the stage or the well; (b) if curve-matching is unsuccessful, to indicate the inadequacy of the mathematical model and the need for more complex numerical model to analyze the system; (c) to verify/validate numerical models, and to identify anomalous behavior or measurement errors in the data. The present approach can be adapted to gas-flow problems in dual-permeability media (hydraulically or naturally fractured) or highly heterogeneous sedimentary rock, as well as to retrograde condensation

Modeling Strategy for Injectivity in SWAG Processes

Imperial Users onl

Neural networks in multiphase reactors data mining: feature selection, prior knowledge, and model design

Les réseaux de neurones artificiels (RNA) suscitent toujours un vif intérêt dans la plupart des domaines d’ingénierie non seulement pour leur attirante « capacité d’apprentissage » mais aussi pour leur flexibilité et leur bonne performance, par rapport aux approches classiques. Les RNA sont capables «d’approximer» des relations complexes et non linéaires entre un vecteur de variables d’entrées x et une sortie y. Dans le contexte des réacteurs multiphasiques le potentiel des RNA est élevé car la modélisation via la résolution des équations d’écoulement est presque impossible pour les systèmes gaz-liquide-solide. L’utilisation des RNA dans les approches de régression et de classification rencontre cependant certaines difficultés. Un premier problème, général à tous les types de modélisation empirique, est celui de la sélection des variables explicatives qui consiste à décider quel sous-ensemble xs ⊂ x des variables indépendantes doit être retenu pour former les entrées du modèle. Les autres difficultés à surmonter, plus spécifiques aux RNA, sont : le sur-apprentissage, l’ambiguïté dans l’identification de l’architecture et des paramètres des RNA et le manque de compréhension phénoménologique du modèle résultant. Ce travail se concentre principalement sur trois problématiques dans l’utilisation des RNA: i) la sélection des variables, ii) l’utilisation de la connaissance apriori, et iii) le design du modèle. La sélection des variables, dans le contexte de la régression avec des groupes adimensionnels, a été menée avec les algorithmes génétiques. Dans le contexte de la classification, cette sélection a été faite avec des méthodes séquentielles. Les types de connaissance a priori que nous avons insérés dans le processus de construction des RNA sont : i) la monotonie et la concavité pour la régression, ii) la connectivité des classes et des coûts non égaux associés aux différentes erreurs, pour la classification. Les méthodologies développées dans ce travail ont permis de construire plusieurs modèles neuronaux fiables pour les prédictions de la rétention liquide et de la perte de charge dans les colonnes garnies à contre-courant ainsi que pour la prédiction des régimes d’écoulement dans les colonnes garnies à co-courant.Artificial neural networks (ANN) have recently gained enormous popularity in many engineering fields, not only for their appealing “learning ability, ” but also for their versatility and superior performance with respect to classical approaches. Without supposing a particular equational form, ANNs mimic complex nonlinear relationships that might exist between an input feature vector x and a dependent (output) variable y. In the context of multiphase reactors the potential of neural networks is high as the modeling by resolution of first principle equations to forecast sought key hydrodynamics and transfer characteristics is intractable. The general-purpose applicability of neural networks in regression and classification, however, poses some subsidiary difficulties that can make their use inappropriate for certain modeling problems. Some of these problems are general to any empirical modeling technique, including the feature selection step, in which one has to decide which subset xs ⊂ x should constitute the inputs (regressors) of the model. Other weaknesses specific to the neural networks are overfitting, model design ambiguity (architecture and parameters identification), and the lack of interpretability of resulting models. This work addresses three issues in the application of neural networks: i) feature selection ii) prior knowledge matching within the models (to answer to some extent the overfitting and interpretability issues), and iii) the model design. Feature selection was conducted with genetic algorithms (yet another companion from artificial intelligence area), which allowed identification of good combinations of dimensionless inputs to use in regression ANNs, or with sequential methods in a classification context. The type of a priori knowledge we wanted the resulting ANN models to match was the monotonicity and/or concavity in regression or class connectivity and different misclassification costs in classification. Even the purpose of the study was rather methodological; some resulting ANN models might be considered contributions per se. These models-- direct proofs for the underlying methodologies-- are useful for predicting liquid hold-up and pressure drop in counter-current packed beds and flow regime type in trickle beds