Sharif Digital Repository

The Ensemble Kalman Filter (EnKF) is a Monte Carlo based method to assimilate the measurement data sequentially in time. Although, EnKF has some advantages over the other Kalman based methods to deal with non-linear and/or high dimensional reservoir models, it also suffers from deficiency in estimation of non-Gaussian parameters. In this work, we propose a re-parameterization method to handle non-Gaussian parameters via Ensemble Kalman Filter framework. For this purpose, concept of cumulative distribution function transformation has been used. In addition, the statistics of prior information have been aggregated in the state vector in order to capture the prior uncertainties of non-Gaussian... 

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... 

In this work, optimization of metalorganic chemical vapor deposition process for uniform layer thickness with especial attention to reactor geometric parameters as decision variables is presented. A numerical solution to a steady thermal flow associated with multi-species and chemical reactions in atmospheric pressure axisymmetrical rotating disk reactor by the CFD technique is obtained. Such a simulation is conducted on the assumption that the low Mach number flow is laminar. Then the validation of the numerical results with the benchmark solutions is conducted. Finally, integrating the CFD simulator with an optimization program, based on the Nelder-Mead algorithm, as a new approach is... 

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 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... 

Object-oriented modeling methodology is used for encapsulating different methods and attributes of data reconciliation (DR) in classes. Classes which are defined for DR, cover steady-state, dynamic, linear and nonlinear DR problems. Two main classes are Constraints and DR and defined for manipulating constraints and general DR problem. The remaining classes are derived from these two classes. A class namely DDRMethod is developed for encapsulating all common attributes and methods needed for any DDR method. Developed DR software and the method of performing dynamic DR are discussed in this paper. Two illustrative examples of Extended Kalman Filtering and artificial neural networks are used... 

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... 

This paper concerns a study on the optimal control for nonlinear systems. An appropriate alternative in order to alleviate the nonlinearity of a system is the exact linearization approach. In this fashion, the nonlinear system has been linearized using input-output feedback linearization (IOFL). Then, by utilizing the well developed optimal control theory of linear systems, the compensated nonlinear system has been controlled. Thus, the structure of the objective function will be converted into a quadratic form which is qualitativly comparable with usual cost functions, and from operating viewpoint is more favorable. To qualify such a procedure, it has been applied to two minimum and... 

Fuzzy sliding mode control (FSMC) as a robust and intelligent nonlinear control technique is proposed to control processes with severe nonlinearity and unknown models. The performance of the proposed method has been evaluated for both single input single output (SISO) and MIMO nonlinear systems through its application in three severely nonlinear processes that are frequently used as benchmarks of nonlinear process control strategies. The evaluation shows that, despite its lack of dependence on the process model, the proposed method performs almost the same as conventional sliding mode control alternatives that utilize all the information that exists in the mathematical model of the process.... 

An adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. Another key feature of this structure is the fact that, it does not need model of the system. In this scheme, neurolinearizer has few weights, so it is practical in adaptive situations. Online training of neurolinearizer is compared to model predictive recurrent training. Relationships between this controller and neural network based model reference adaptive controller are established. A CSTR reactor and pH control in a neutralization process illustrate performance of this method. Simulation studies show a superior performance with respect to a PI controller  

Kinetic modeling is an important issue, whose objective is the accurate determination of the rates of various reactions taking place in a reacting system. This issue is a pivotal element for the process design and development particularly for novel processes which are based on reactions taking place between various types of species. In this paper, the Genetic Algorithms have been used to develop a systematic computational framework for kinetic modeling of various reacting systems. This framework can be used to find the optimum values of various parameters that exist in the kinetic model of a reacting system. The Fischer-Tropsch (FT) reactions have been used as the kinetic modeling bench... 

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.... 

The bronchial tree plays a main role in the human respiratory system because the air distribution throughout the lungs and gas exchange with blood occur in the airways whose dimensions vary from several centimeters to micrometers. Organization of about 60,000 conducting airways and 33 million respiratory airways in a limited space results in a complex structure. Due to this inherent complexity and a high number of airways, using target-oriented dimensional reduction is inevitable. In addition, there is no general reduced-order model for various types of problems. This necessitates coming up with an appropriate model from a variety of different reduced-order models to solve the desired... 

Gas exchange, particle deposition, and drug delivery in the human lungs depend on the structure of the human bronchial tree. The conducting airways occupy the main portion of the human lungs and transport air from outside into pulmonary acini where the O2–CO2 exchange with blood occurs. Therefore, the generation of three-dimensional accurate structure of the conducting airways is required for simulation of the transport phenomena in the human respiratory system. The present study proposes an intelligent method for generation of conducting airways based on stochastic parametric Lindenmayer system (L-system). The conducting airways grow into the bronchopulmonary segments simultaneously using... 

The bronchial tree plays a main role in the human respiratory system because the air distribution throughout the lungs and gas exchange with blood occur in the airways whose dimensions vary from several centimeters to micrometers. Organization of about 60,000 conducting airways and 33 million respiratory airways in a limited space results in a complex structure. Due to this inherent complexity and a high number of airways, using target-oriented dimensional reduction is inevitable. In addition, there is no general reduced-order model for various types of problems. This necessitates coming up with an appropriate model from a variety of different reduced-order models to solve the desired... 

Gas exchange, particle deposition, and drug delivery in the human lungs depend on the structure of the human bronchial tree. The conducting airways occupy the main portion of the human lungs and transport air from outside into pulmonary acini where the O2–CO2 exchange with blood occurs. Therefore, the generation of three-dimensional accurate structure of the conducting airways is required for simulation of the transport phenomena in the human respiratory system. The present study proposes an intelligent method for generation of conducting airways based on stochastic parametric Lindenmayer system (L-system). The conducting airways grow into the bronchopulmonary segments simultaneously using... 

In pore network modeling, the void space of a rock sample is represented at the microscopic scale by a network of pores connected by throats. Construction of a reasonable representation of the geometry and topology of the pore space will lead to a reliable prediction of the properties of porous media. Recently, the theory of multi-cellular growth (or L-systems) has been used as a flexible tool for generation of pore network models which do not require any special information such as 2D SEM or 3D pore space images. In general, the networks generated by this method are irregular pore network models which are inherently closer to the complicated nature of the porous media rather than regular...