Sharif Digital Repository

Kidney is one of the most complicated organs in terms of functionality and physiology due to its complicated unique vascular structure. Previous investigations have focused on specific characteristics like length and diameter. Most of these methods are based on Image Processing techniques which prevent a comprehensive study in growth and changes of the vascular system. Lindenmyaer system (L-system) is a parallel rewriting system and a type of formal grammar and had been used to create graphs and natural patterns. By absorbing the physiological characteristics of the kidney and integrating them into parametric L-system, a highly dense and accurate vascular system could be generated with few... 

In this study, in order to enhance data contaminated with random and gross errors, the implementation of data reconciliation technique on large-scale industrial unit was investigated. Data reconciliation results on the naphtha reformer unit, revealed that uncertainty in estimation of input and output reactor temperatures, decreased up to 2% in comparison with measured ones. In addition, uncertainties in estimation of the mass flow rates have declined by nearly 30%. Also, an object-oriented framework for plant wide data reconciliation was designed. This framework was designed as an extension of plant wide identification software developed previously. Moreover, adding five classes to the... 

The environmental impact of process plants is one of the causes of the environmental disturbance in the world. Including the effects of these destructive impacts can be mentioned to the phenomena such as global warming, climate change, severe air pollution and water shortage. Hence, the economic justification of a process, without the review of relevance of its environmental impacts, it does not lead to the implementation and construction of a process plant. For this reason, it is a requirement to provide a software platform (preferably object-oriented) for quick and automatic checking of this item and the exact determining of these effects. On the other hand, due to the complexity of the... 

Hydrocarbon reservoir management and development as well as planning of enhanced oil recovery (EOR) processes are based on the reservoir dynamic model. Thus, successful implementation of EOR scenarios greatly depends on the quality of the dynamic model and accuracy of the associated parameters in order to correctly describe fluid flow through porous media. First, a dynamic model is constructed based on the prior knowledge. However, because of the various types of error during model building, the prior model is not so accurate and perfect. Accordingly, new observation data, such as production and 4D seismic data, are utilized to calibrate the prior model and characterize the reservoir under a... 

A genetic-based pore network extraction method from micro-CT images is proposed in this work. Several variables such as the number, radius and location of pores, the coordination number, as well as the radius and length of the throats are used herein as the optimization parameters. Two methods are presented for modeling the pore network structure. Unlike the previous models, these methods are directly based on minimizing the error between the extracted network and the real porous medium. This leads to the generation of more accurate results while reducing required computational memories. Two different objective functions are used in building the network. In the first method, only the... 

In this research, the aim is to investigate the use of data assimilation method for better reservoir model updating in the reservoir management. In addition, multi-objective optimization concept is studied for the production optimization. The application of these methods is applied for reservoir management in the naturally fractured reservoirs. To update the reservoir, ensemble based methods, especially ensemble Kalman filter and ensemble smoother, are used. To tackle the challenges encountered with these methods, modifications are proposed to obtain a better history matching and more accurate reservoir characterization. The proposed framework for history matching is implemented for the... 

Digital analysis of reservoir rocks prepares a powerful tool in the field of porous media studies and provides appropriate information of single / multiphase flows by performing numerical simulations on porous media images. In order to access the necessary tools to benefit from digital rock analysis and characterization, we need accessing the porous media images, the geometric distribution and the spatial distribution of pores and grains through imaging or reconstruction methods. Porous media reconstruction methods are known as an alternative to imaging techniques. In addition, reconstruction methods have the ability to fix problems and imperfections associated with imaging, such as... 

The industrial paradigm shift toward intelligent and sustainable management stimulates policymakers to leverage artificial intelligence for the decentralized planning of chemical industries with energy networks. By doing so, this work firstly presents a simulation framework to investigate the rigorous transient behavior of integrated systems comprising natural gas and power transmission networks, along with a chemical plant whose feedstock is natural gas. This framework entails dynamic models for the gas transmission network and the SynGas plant, and a continuous-time AC-power flow formulation with dispatchable loads. It addresses the following key challenges: (i) analyzing energy and... 

Hydrocarbon reservoirs development and management is based on their dynamic models. To encounter various types of error during model building, model parameters are adjusted to produce reservoir historical data by assimilation (history matching) of reservoir production or 4D seismic data. Among the existing sequential methods for automatic history matching, ensemble Kalman filter and its variants have displayed promising results. The innovations of this thesis for ensemble Kalman filter (EnKF) are presented into three major orients; these includes adaptive localization/regularization, characterization of original PUNQ test model and characterization of channelized reservoir.
To mitigate... 

In control engineering we want to set dynamic behavior of the system to a desired one and having a model that simulate systems behavior is necessary . System identification is a branch of chemical engineering that provide us this model , which divided into different branches itself . In this project we use object oriented programming to design and implement a software that support black box models such as Neural networks in addition to Mechanistic models (Which was implemented in previous project ) for identification . We use UML and C++ language in C++ Borland Builder in this project  

In this project a framework for Model Based Fault Diagnosis (MBFD) in a Gas Transmission Network (GTN) was developed. A black-box linear state-space model was used to capture dynamic behavior of an industrial benchmark for GTN. A full fledge commercial network simulator were used to obtain the required data for process identification. In order to check the robustness of the model zero mean white noise was imposed on various output obtained by the commercial simulator. Kalman filter was used to estimate the states of the GTN. These estimated states along with the measured output (all obtained from the commercial simulator) are almost similar to their corresponding estimated signal. This shows... 

The main objective of Control Engineering is to lead dynamic behavior of system to an arbitrary state, accomplishment of such an objective requires a model which somehow represents the dynamic behavior of the system, On the other hand an accurate yet fast and efficient model of a complicated model cannot be easily obtained based on the original inputs, states and outputs of the system. This is true both for mechanistic (white box) and empirical (black box) models. In this project, the previous frame work developed for object oriented plant-wide identification has been extended to cover the model reduction based on various established procedures in this area (e.g., statistical and intelligent... 

A modeling framework is proposed for complex chemical process networks. Concept such as passivity systems, thermodynamic function and graph theory are some important and applicable concept which seems to be useful for process network. The state is represented by energy, volume and mass inventories. The dynamic behavior of the process system is constrained so that all trajectories satisfy the first and second laws of thermodynamics. The concavity of the entropy function is used to define a storage function for passivity design. The proposed storage function is closely related to the Gibbs tangent plane condition. Stability analysis, pairing and controller design could be done by this new... 

A statistically-based method has been developed for automated identification of steady state. The method is computationally inexpensive when compared to conventional techniques. The R-statistic is dimensionless and independent of the measurement level. Because it is a ratio of estimated variances, it is also independent of the process variance. Simulations show that for recommended critical values are also effectively independent of the magnitude and distribution of the noise.
A fuzzy-logic-based methodology for on-line steady state and transient state identification is introduced. Although steady state identification is complicated by the presence of noise, fuzzy logic can help capture...