Sharif Digital Repository

In this study, ensemble Kalman filter (EnKF) is first applied to estimate absolute and relative permeabilities jointly under three-phase flow condition in the porous media. By assimilating historical data, absolute permeability field is adjusted progressively towards its reference. However, assimilation process does not improve the estimation of all relative permeability parameters, and some of them are poorly estimated at the end of assimilation. To improve the estimation of the relative permeability curves, we propose a new hybrid approach in which the estimation process of the absolute and relative permeabilities is separated. In this approach, gridblock permeabilities are again estimated... 

In a gas transmission network (GTN), faults can easily propagate due to the interconnections of streams. The main objective of this paper is to develop a systematic framework for an online decision support system (DSS) in order to make the right decisions to get the GTN out of critical conditions (which cannot be handled by the plant controllers) smoothly. One of the key features of the proposed scheme is its lack of dependence on prior knowledge of the fault signals (e.g., number of faults, and their origin). In this article, the GTN is modeled by a fuzzy directed graph (FDG). The proposed approach utilizes a reasoning algorithm based on the deviations that exist in the process variables... 

An ensemble-based, sequential assimilation procedure is developed and successfully applied to estimate absolute and relative permeabilities jointly under multi-phase (oil, gas and water) flow condition in the porous media. Two-phase oil-water and gas-oil relative permeabilities are represented by power-law models, and Stone's Model II is used to calculate three-phase oil relative permeability. Prior absolute permeability field is also generated with isotropic Gaussian covariance. The proposed method is validated by a twin numerical setup with three different case studies, in which a synthetic 2D reservoir under multi-phase flow condition is considered. In the first case, there is... 

Ensemble-based assimilation methods are the most promising tools for dynamic characterization of reservoir models. However, because of inherent assumption of Gaussianity, these methods are not directly applicable to channelized reservoirs wherein the distribution of petrophysical properties is multimodal. Transformation of facies field to level set functions have been proposed to alleviate the problem of multimodality. Level set representation ensures that the estimated fields are facies realizations as well as no modification of the assimilation method is required. Moreover, due to the complexity of the history matching problem in the channelized reservoirs, most researchers conventionally... 

The aim of this paper is to synthesize a hybrid controller for pressure swing adsorption (PSA) processes. Since the process is described by a set of partial differential algebraic equations, first a local reduced-order model (LROM) for the process is developed and is formalized as a hybrid system. A hybrid controller is designed for purity control of the process in the presence of external disturbances by determining the maximal safe set of the LROM. A hybrid backward reachability analysis is performed for this purpose. Considering a realistic scenario for PSA processes where the states are not available and the number of measurement sensors is very limited, the desired states are estimated... 

A set of conventional feedforward multilayer neural networks have been proposed to predict basic properties (e.g., critical temperature (T c), critical pressure (Pc), critical volume (V c), acentric factor (ω) and molecular weight (MW)) of pure compounds and petroleum fractions based on their normal boiling point (T b) and liquid density at 293 K. The accuracy of the method is evaluated by its application for basic property estimation of various components not used in the development of the method. Furthermore, the performance of the method is compared against the performance of the other alternatives reported as the most accurate and general methods for basic property prediction. Results of... 

The activity coefficient at infinite dilution is a representative of the limiting non-ideality of a solute in a mixture. Various methods for the prediction of infinite dilution activity coefficients (IDACs) have been developed. Artificial neural networks are powerful mapping tools for nonlinear function approximations. Accordingly, an artificial neural network model is proposed for the prediction of the IDACs of binary systems where the properties of the individual components are used as inputs to the network. The input parameters of the neural network are the mixture temperature, critical temperature, critical pressure, critical volume, molecular weight, dipole moment and the acentric... 

Ensemble Kalman filter, EnKF, as a Monte Carlo sequential data assimilation method has emerged promisingly for subsurface media characterization during past decade. Due to high computational cost of large ensemble size, EnKF is limited to small ensemble set in practice. This results in appearance of spurious correlation in covariance structure leading to incorrect or probable divergence of updated realizations. In this paper, a universal/adaptive thresholding method is presented to remove and/or mitigate spurious correlation problem in the forecast covariance matrix. This method is, then, extended to regularize Kalman gain directly. Four different thresholding functions have been considered... 

In order to safe design and optimize performance of some industrial systems, it's often needed to categorize two-phase flow into different regimes. In each flow regime, flow conditions have similar geometric and hydrodynamic characteristics. Traditionally, flow regime identification was carried out by flow visualization or instrumental indicators. In this research3 kind of neural networks have been used to predict system characteristic and flow regime, and results of them were compared: radial basis function neural networks, self organized and Multilayer perceptrons (supervised) neural networks. The data bank contains experimental pressure signalfor a wide range of operational conditions in... 

A continuous-discrete Distributed and Decentralized Switching Kalman Filter (DDSKF) is designed for estimation of spatial profiles in Pressure Swing Adsorption (PSA) processes. The introduced observer is an integral part of the control strategy of hybrid systems in general and PSA systems in particular. A reduced order model is developed based on the mechanistic model of the process. The sensors are optimally located and observability of the process is studied. The proposed observer is used to estimate the spatial profiles of various states of a two-bed, six-step PSA system used for production of pure H2 from a H2–CH4 gas mixture. The spatial profiles of the system have been estimated using... 

As an integral part of a hybrid control system for Pressure Swing Adsorption (PSA) processes, a dynamical hybrid observer is proposed for online reconstruction of the active mode and continuous states of these processes. Hybrid systems feature both continuous and discrete-event dynamics and hence are very suited to describe PSA processes. For estimation of the active mode, a mode observer is designed, and the continuous spatial profiles in each mode are estimated by distributed and decentralized Kalman filters. The proposed hybrid observer has been applied, in silico, for a two-bed, six-step PSA process used for Hydrogen purification. The active mode of the process along with the continuous... 

In this paper we propose a method towards purity control of pressure swing adsorption (PSA) processes which is based on the use of hybrid systems formalism. Hybrid systems feature both continuous and discrete-event dynamics and hence are very suited to describe in detail PSA processes. Based on mechanistic model of the processes, a local reduced-order model (LROM) is developed for PSA processes. Then the processes are represented as hybrid systems whose continuous evolution is described by the LROM. We then perform an analysis of hybrid reachability properties of the hybrid system obtained, based on which the so-called maximal safe set is computed. The analysis is performed for a two-bed,... 

In this study, coupled Generative Adversarial and Auto-Encoder neural networks have been used to reconstruct realizations of three-dimensional porous media. The gradient-descent-based optimization method is used for training and stabilizing the neural networks. The multi-scale reconstruction has been conducted for both sandstone and carbonate samples from an Iranian oilfield. The sandstone contains inter and intra-grain porosity. The generative adversarial network predicts the inter-grain pores and the auto-encoder provides the generative adversarial network result with intra-grain pores (micro-porosity). Different matching criteria, including porosity, permeability, auto-correlation... 

A new two-dimensional dynamic model was developed to simulate asphaltene precipitation, aggregation, and deposition at isothermal and non-isothermal conditions. The perturbed-chain statistical associating fluid theory equation of state was used to model the asphaltene precipitation. Also, novel kinetic models were used to account for the aggregation and deposition of asphaltene particles. The effect of the aggregate size on the rate of aggregation and deposition was studied, and it was concluded that the rate of asphaltene deposition increases, while the concentration of nanoaggregates increases in the well column. The tendency of smaller aggregates to deposit on the surface could be... 

In order to safe design and optimize performance of industrial systems which work under two phase flow conditions, it's often needed to categorize flow into different regimes. In present work the experiments of two phase flow were done in a large scale test facility with length of 6m and 5cm diameter. Four main flow regimes were observed in vertical air-water two phase flows at moderate superficial velocities of gas and water: Bubbly, Slug, Churn and Annular. Some image processing techniques were used to extract information from each picture. This information include number of bubbles or objects, area, perimeter, height and width of objects (second phase).Also a texture feature extraction... 

In this paper sustainability analysis (exergy, environmental and economic) and multi-objective optimization for an aromatic plant are provided and interactions between decision variables are discussed. Environmental evaluation shows that the cancer human toxicity and global warming are the most important environmental concerns and the weight of EIs (environmental impacts) are mainly due to process wastes. The optimizations results demonstrate parameters like reactor temperature have a wide range in the optimizations while some variables, such as extraction unit variables have the same value. Utility EI reduction occurred in economic and exergy optimizations rather than environmental... 

Multiphase impeller stirred tank reactors enhance mixing of reacting species used in a variety of chemical industries. Gas-liquid mixing and mass transfer between these phases is an important application in reactor design. One such an example is hydrogenation reaction of aqueous NDMA (Nitroso dimethyl amine) solution in which one of the parameters having a significant effect on the reaction rate is mass transfer. This parameter in turn is a function of impeller shape and rotational speed, gas flow rate and reactor scale. Computational Fluid Dynamics (CFD) is a useful tool in the analysis, design and scale up of stirred tank reactors via investigating hydrodynamics of gas and liquid flow,... 

Centralized and cloud computing-based network architectures are the promising tracks of future communication systems where a large scale compute power can be virtualized for various algorithms. These architectures rely on high-performance communication links between the base stations and the central computing systems. On the other hand, massive Multiple-Input Multiple-Output (MIMO) technology is a promising solution for base stations toward higher spectral efficiency. To reduce system complexity and energy consumption, 1-bit analog-to-digital converters (ADCs) are leveraged with the cost of lowering the signal quality. To recover the lost information, more sophisticated algorithms, like... 

We proposed a personalized bolus advisor for patients with type 1 diabetes (T1D). A bolus advisor is a decision support system that recommends insulin doses based on an open-loop model-based optimization. To construct the bolus advisor, the optimal open-loop control of blood glucose (BG) concentration in T1D patients was represented as a multi-objective optimization problem. The insulin types, doses, and times for each injection were provided by the bolus advisor based on a personalized model and an average daily diet, which should be re-tuned frequently in specific time intervals. The constructed personalized model for T1D patients incorporates effects of the patient's age and body weight.... 

This paper is aimed at investigating some computational aspects of different isoperimetric problems on weighted trees. In this regard, we consider different connectivity parameters called minimum normalized cuts/isoperimetric numbers defined through taking the minimum of the maximum or the mean of the normalized outgoing flows from a set of subdomains of vertices, where these subdomains constitute a partition/subpartition. We show that the decision problem for the case of taking k-partitions and the maximum (called the max normalized cut problem NCP^M), and the other two decision problems for the mean version (referred to as IPP^m and NCP^m) are NP-complete problems for weighted trees. On...