Development of Coordinated Methodologies for Modeling CO2-Containing Systems in Petroleum Industry.

Masters Degree. University of KwaZulu-Natal, Durban.Clathrate hydrates formation in natural gas processing facilities or transportation pipelines may lead to process and/or safety hazards. On the other hand, a number of applications are suggested on the basis of promoting the gas hydrate formation. Some researchers have investigated separation and purification processes through gas hydrate crystallization technology. Some works report that the hydrate formation is applicable to the gas transportation and storage. Gas hydrate concept is also studied as a potential method for CO2 capture and/or sequestration. Water desalination/sweetening, and refrigeration and air conditioning systems are other proposed uses of hydrates phenomenon. In the realm of food processing and engineering, several studies have been done investigating the application of gas hydrate technology as an alternative to the conventional processes. Accurate knowledge of phase equilibria of clathrate hydrates is crucial for preventing or utilizing the hydrates. It is believed that energy production or extraction from different fossil fuels is responsible for considerable emissions of CO2, as an important greenhouse gas, into the atmosphere. Furthermore, CO2 removal from the streams of natural gas is important for enhancing the gaseous streams’ heating value. Employment of solvent-based processes and technologies for removing the CO2 is a widely employed approach in practical applications. Amine-based or pure amine solutions are the most common choice to remove the produced CO2 in numerous carbon capture systems. Further to the above, ionic liquids (ILs) are capable to be utilized to capture CO2 from industrial streams. Other potential solvent are sodium piperazine (PZ) and glycinate (SG) solutions. Equilibrium absorption of carbon dioxide in the aqueous phase is a key parameter in any solvent-based CO2 capture process designing. The captured CO2, then, can be injected into the hydrocarbon reservoirs. In addition to the fact that injection of CO2 into potential sources is one of the most reliable methodologies for enhanced hydrocarbon recovery, utilizing this process in conjunction with the CO2 capture systems mitigates the greenhouse effects of CO2. One of the most significant variables determining the success of CO2 injection is known to be the minimum miscibility pressure (MMP) of CO2-reservoir oil. This research study concerns implementation of computer-based methodologies called artificial neural networks (ANNs), classification and regression trees (CARTs)/AdaBoost-CART, adaptive neuro-fuzzy inference systems (ANFISs) and least squares support vector machines (LSSVMs) for modeling: (a) phase equilibria of clathrate hydrates in: 1- pure water, 2- aqueous solutions of salts and/or alcohols, and 3- ILs, (b) phase equilibria (equilibrium) of hydrates of methane in ILs; (c) equilibrium absorption of CO2 in amine-based solutions, ILs, PZ solutions, and SG solutions; and (d) MMP of CO2-reservoir oil. To this end, related experimental data have been gathered from the literature. Performing error analysis, the performance of the developed models in representing/ estimating the independent parameter has been assessed. For the studied hydrate systems, the developed ANFIS, LSSVM, ANN and AdaBoost-CART models show the average absolute relative deviation percent (AARD%) of 0.04-1.09, 0.09-1.01, 0.05-0.81, and 0.03-0.07, respectively. In the case of hydrate+ILs, error analysis of the ANFIS, ANN, LSSVM, and CART models showed 0.31, 0.15, 0.08, and 0.10 AARD% of the results from the corresponding experimental values. Employing the collected experimental data for carbon dioxide (CO2) absorption in amine-based solutions, the presented models based on ANFIS, ANN, LSSVM, and AdaBoost-CART methods regenerated the targets with AARD%s between 2.06 and 3.69, 3.92 and 8.73, 4.95 and 6.52, and 0.51 and 2.76, respectively. For the investigated CO2+IL systems, the best results were obtained using CART method as the AARD% found to be 0.04. Amongst other developed models, i.e. ANN, ANFIS, and LSSVM, the LSSVM model provided better results (AARD%=17.17). The proposed AdaBoost-CART tool for the CO2+water+PZ system reproduced the targets with an AARD% of 0.93. On the other hand, LSSVM, ANN, and ANFIS models showed AARD% values equal to 16.23, 18.69, and 15.99, respectively. Considering the CO2+water+SG system, the proposed AdaBoost-CART tool correlated the targets with a low AARD% of 0.89. The developed ANN, ANFIS, and LSSVM showed AARD% of more than 13. For CO2-oil MMP, the proposed AdaBoost-CART model (AARD%=0.39) gives better estimations than the developed ANFIS (AARD%=1.63). These findings revealed the reliability and accuracy of the CART/AdaBoost-CART methodology over other intelligent modeling tools including ANN, ANFIS, and LSSVM

Intelligent Management on the Home Consumers with Zero Energy Consumption

The energy and environment crisis has forced modern humans to think about new and clean energy sources and in particular, renewable energy sources. With the development of home network, the residents have the opportunity to plan the home electricity usage with the goal of reducing the cost of electricity. In this regard, to improve the energy consumption efficiency in residential buildings, smart buildings with zero energy consumption were considered as a proper option. Zero-energy building is a building that has smart equipment whose integral of generated and consumed power within a year is zero. In this article, smart devices submit their power consumption with regard to the requested activity associated with the user’s time setting for run times and end times of the work to the energy management unit and ultimately the time to start work will be determined. The problem’s target function is reducing the energy cost for the consumer with taking into account the applicable limitations

A comparative study on common power flow techniques in the power distribution system of the Tehran metro

Overall, a power-flow study is a steady-state assessment whose goal is to specify the currents, voltages, and real and reactive flows in a power system under a given load conditions. This paper presents a comparison of common power flow techniques in the Tehran metro power distribution system at the presence of non-linear loads. Moreover, a modelling, simulation and analysis of this power distribution system is implemented with the Electrical Transient Analyser Program (ETAP) software. In this assessment, common power flow techniques including the Newton-Raphson (NR), Fast Decoupled (FD), and Accelerated Gauss-Seidel (AGS) techniques are provided and compared. The obtained results (total generation, loading, demand, system losses, and critical report of the power flow) are analysed. In this paper, we focus on the detailed assessment and monitoring by using the most modern ETAP software, which performs numerical calculations of a large integrated power system with fabulous speed and also generates output reports. The capability and effectiveness of the power flow analysis are demonstrated according to the simulation results obtained with ETAP by applying it to the power distribution system of the Tehran metro. In developing countries such as Iran, off-line modelling and simulation of power grids by a powerful software are beneficial and helpful for the best usage of the electrical energy

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models