ESTIRJ Volume 6, Issue4  Dec, 2022

Paper 1: OPTIMIZATION OF GAS LIFT PERFORMANCE. A SIMULATION APPROACH. (1-6)

Abstract:The most popular production technology in the world's oil and gas activities are artificial lift systems.Some wells need lifting help from the start; most everyone does at some point. To maximize the oil output from the oil wells, the Gas Lift System had to be optimized as part of this study's objective.The primary job is to create an artificial gas lift system that aid production not under the currently operating conditions but also under unsuitable future circumstances in according to reservoir predicting, water cut is anticipated to rise by up to 50%, 60% and 70%. It is clearly discussed how to construct an optimal gas lift system using PROSPER. According to the calculations, for the unloading procedure side pocket mandrels were fixed at certain depths. The remainder of the cases were then modified to fit the original side pocket mandrel spacing design, greater reservoir pressure and with lower water cut levels. The system was able to produce oil rates substantially over the minimum specified rates in every instance until the water cut level reaches 70% then well get loaded up and stopped producing. The anticipated output levels demonstrated that even poorer system productivity scenarios may be accommodated by the current completion schedule.

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Paper 2: MAGNETIC IRON OXIDE NANOPARTICLES: AN EFFECTIVE APPROACH TO REDUCE CHOLESTEROL(7-10)

Abstract: Cholesterol is a lipid molecule in lipoprotein and cell membrane and precursor of bile acids, steroids hormones and vitamin D. There are two types of cholesterol in human blood HDL (high density lipoprotein) referred to as good cholesterol and LDL (low density lipoprotein) referred to bad cholesterol. Higher levels of cholesterol, especially LDL, become a cause of cardiovascular disease (CVD). In this research iron oxide (Fe3O4) nanostructures were used as selective adsorption materials for cholesterol. Iron oxide nanoparticles were synthesized using a hydrothermal method then coated with lysine amino acid (C6H14N2O2). The uncoated (Fe3O4-P) and coated (Fe3O4-L) iron oxide nanoparticles samples were characterized by XRD, FTIR and DLS analysis. The particles were then analyzed for adsorption of cholesterol using uv-visible spectroscopy. Outstanding adsorption capacity (13.63%) was observed for Fe3O4-L in just 20 minutes.

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Paper 3: EFFECTIVE ALUMINUM-COBALT BASED MIXED METAL OXIDE NANOCOMPOSITE FOR WATER SPLITTING (11-15)

Abstract: TRenewable energy materials with better efficiency are in high demand for advanced applications. In this study, we report effective aluminum-cobalt based mixed metal oxide nanocomposite for oxygen evolution reaction application. The as synthesized nanocomposite offers better electrical conductivity and high density of oxygen vacancies. The prepared material also offers low overpotential of 321 mV at a current density of 20 mA/cm2, with charge transfer resistance of 75 Ω. The results suggest that addition of Co2O3 into Al2O3, not only modified the morphology and also altered the interfacial chemistry of the material and could lead to better charge transfer at the interface during electrochemical applications.

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Paper 4: DOCUMENTATION AND MANUAL CONSERVATION STRATEGIES FOR THE STANDING STRUCTURE OF PACCA FORT HYDERABAD SINDH PAKISTAN(16-23)

Abstract: Pacca Qila is a first class monument of 18th century, the wall is disintegrated from various spots and at some huge losses are recorded. There are various factors affecting the structure of fort such as human occupants, natural and environmental effects,can further deteriorate the Pacca Fort. In order to save this monument urgent conservation works such as documentation and conservation of fort’s main gate and fortification wall needs to be done. In order to help the authorities an immediate Conservative plan and a project is required.

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Paper 5: ANALYSIS OF CHANNEL ESTIMATION TECHNIQUES IN MIMO-OFDM SYSTEM (24-30)

Abstract: The reception of the higher data rates, the better utilization of available bandwidth, and the reliable communication demand a better technology for the end users. The modern wireless MIMO-OFDM system is the answer for such a technology. In order to achieve these benefits, the channels of MIMO-OFDM system have to be estimated for getting the accurate channel state information. For channel estimation, different techniques like Pilot, Blind and Decision Directed based estimation techniques, are used. But the channel estimation with Pilot Symbols is selected for the purpose of analysis for this research paper. Methodology used for the analysis is such that different parameters are initialized first, and then processed on MATLAB. When the transmitter sends the data streams, it gets distorted on its way to the receiver due physical properties of the channel. Pilot symbol based channel estimation techniques like LS and MMSE are used to estimate the channel with the help of BER and SNR functions. These functions are affected by the Pilot Symbols and the Channel Taps. As the size of Pilot Symbols or Channel Taps is increased, the BER is also increased or decreased, respectively. So, it can be concluded that estimation of channels in MIMO-OFDM system is of utmost importance for effective performance of the system.

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Paper 6: NON-INVASIVE EEG BASED FEATURE EXTRACTION FRAMEWORK FOR MAJOR DEPRESSIVE DISORDER ANALYSIS (31-37)

Abstract: Around the world, depression and other behavioral health disorders are serious public health concerns. Persistent behavioral health issues have a wide range of consequences that affect people on a personal, relational, cultural, and social level. Major depressive disorder (MDD) is a mental illness that affects people of all ages around the world. It has grown into a major global health issue as well as an economic burden. Clinicians have traditionally used a variety of medications to limit the growth of this disease at an early stage. The goal of this research is to improve depression diagnosis by altering electroencephalogram (EEG) signals and extracting the Differential Entropy (DE) and Power Spectral Density (PSD), using machine learning and deep learning techniques. This study analyzed the EEG signals of 30 healthy people and 34 people with Major Depressive Disorder (MDD). K-nearest neighbors (KNN) had the highest accuracy among machine learning algorithms of 99%, while Support vector machine (SVM) had achieved 95% accuracy. The developed Deep Learning approach, convolutional neural network (CNN), achieved 99% accuracy. With these promising results, this study establishes the viability of EEG-based diagnosis of MDD.

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Paper 6: FLOW PROCESS ANALYSIS OF TWO TYPE DATA HAVING PRE-EMPTIVE PRIORITY TECHNIQUE (0-000)

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