Sharif Digital Repository

In this paper, a framework for distributed and decentralized state estimation in high-pressure and long-distance gas transmission networks (GTNs) is proposed. The non-isothermal model of the plant including mass, momentum and energy balance equations are used to simulate the dynamic behavior. Due to several disadvantages of implementing a centralized Kalman filter for large-scale systems, the continuous/discrete form of extended Kalman filter for distributed and decentralized estimation (DDE) has been extended for these systems. Accordingly, the global model is decomposed into several subsystems, called local models. Some heuristic rules are suggested for system decomposition in gas pipeline... 

In this paper, a non-isothermal model of natural gas in pipelines including mass, momentum and energy balance equations are used as model equations for modeling and state estimation in gas pipeline systems. It is shown that differential equations describing the dynamic behavior of a high-pressure and long-distance gas transmission network (GTN) can be solved efficiently using the orthogonal collocation method. The issues corresponding to the presence of discontinuities in the dynamic model is substantially discussed and studied. The non-isothermal model of a GTN can experience discontinuities during transient operations, which causes challenges in simulation and state estimation in this... 

In natural gas transmission networks, the efficiency of daily operation is strongly dependent on our knowledge about the customer future demands. Unavailability of accurate demand forecasts makes it more important to be able to characterize the loads and quantify the corresponding uncertainty. A planning strategy for natural gas transmission networks under future demand uncertainty is addressed, which involves coupling of a gas transmission network dynamic simulator with the stochastic optimization framework. Loads from a gas-fired power plant are studied where the loads are characterized by a number of uncertain parameters, and unscented transform is utilized for uncertainty propagation.... 

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... 

In this work we prove Malliavin differentiability for the solution to an SDE with locally Lipschitz and semi-monotone drift. To prove this formula, we construct a sequence of SDEs with globally Lipschitz drifts and show that the p-moments of their Malliavin derivatives are uniformly bounded  

In this study, colloidal tungsten oxide nanoparticles were fabricated by pulsed laser ablation of tungsten target using the first harmonic of a Nd:YAG laser (1064 nm) in deionized water. After ablation, a 0.33 g/lit HAuCl4 aqueous solution was added into as-prepared colloidal nanoparticles. In this process, Au3+ ions were reduced to decorate gold metallic state (Au0) onto colloidal tungsten oxide nanoparticles surface. The morphology and chemical composition of the synthesized nanoparticles were studied by AFM, XRD, TEM and XPS techniques. UV-Vis analysis reveals a distinct absorption peak at ∼530 nm. This peak can be attributed to the surface plasmon resonance (SPR) of Au and confirms... 

A novel procedure for fingerprint enhancement filter design is described. Fingerprints are best used as unique and invariant identifiers of individuals. Identification of fingerprint images is based on matching the features obtained from a query image against those stored in a database. Poor quality of fingerprint images makes serious problems in the performance of subsequent matching process. The main contribution of this work is to quantify and justify the functional relationship between image features and filter parameters. In this work, the enhancement process is adapted to the input image characteristics to improve its efficiency. Experimental results show the superiority of the... 

In this paper, we introduce the problem of private sequential function computation, where a user wishes to compute a composition of a sequence of K linear functions, in a specific order, for an arbitrary input. The user does not run these computations locally, rather it exploits the existence of N non-colluding servers, each can compute any of the K functions on any given input. However, the user does not want to reveal any information about the desired order of computations to the servers. For this problem, we study the capacity, defined as the supremum of the number of desired computations, normalized by the number of computations done at the servers, subject to the privacy constraint. In... 

With recent considerable developments in the Internet of Things (IoT), billions of resource-constrained devices are interconnected through the Internet. Monitoring this huge number of IoT devices which are heterogeneous in terms of underlying communication protocols and data format is challenging. The majority of existing IoT device monitoring solutions heavily rely on centralized architectures. Since using centralized architectures comes at the expense of trusting an authority, it has several inherent drawbacks, including vulnerability to security attacks, lack of data privacy, and unauthorized data manipulation. Hence, a new decentralized approach is crucial to remedy these drawbacks. One... 

In this work, a reliable dimensionless correlation is proposed for prediction of permeability reduction rate in porous media, which is verified by experimental data obtained in this work in glass bead and sand pack as well as the core data from the literature. Although this correlation is based on the data which were obtained in our work in glass bead and sand-packed media at low pressure, it shows considerable flexibility to match with the extracted data for sandstone cores at high pressure, various flow rates, different temperatures and concentrations of calcium, and sulfate ions in brine solutions. In addition, a novel relationship for predicting the rate of precipitation of CaSO4 in... 

In this work, an experimental and theoretical study has been conducted to investigate the permeability reduction due to CaSO4 scale deposition in packed column porous media. Permeability reduction by calcium sulphate deposition follows a systematic trend considering various important parameters that are affected in this complex process. Hence, a novel dimensionless model has been proposed for the prediction of permeability reduction rate with high accuracy. The developed model is based on the data obtained from glass bead and carbonate grain packed column at low pressure. The proposed model was validated with Berea sandstone cores data at high pressure (100-20,678 kPa), various flow rates...