2 research outputs found
PID CONTROLLER TUNING OF 3-PHASE SEPARATOR IN OIL & GAS INDUSTRY USING BACTERIA FORAGING OPTIMIZATION ALGORITHM
In oil and gas industry, one of the most important stages in processing petroleum is separation. It can be classified by operating configuration such as vertical, horizontal and spherical or by its function which is 2-phase or 3-phase. In this paper, vertical 3-phase separator will be chosen and researched. 3-phase separator is used to separate water, oil and gas. Gas will be at the top, oil will be the middle layer and water will be at the bottom due to gravitational force and the density of the substance. The objective is to tune the PID controller controlling the level of the water in the separator. Outflow rate of the water from the bottom of the separator will be used to control the water level. Currently there are controlling methods namely PI control using trial and error method, PI control using Butterworth filter design method and IMC method. These methods were having quite high % overshoot and long settling time. So, this paper will introduce Bacterial Foraging Optimization Algorithm (BFOA) in optimizing the parameters for PI control. BFOA mimics the behaviour of the bacteria in searching for highest food concentration which then modified to search the best parameters for the PID controller. BFOA will be able to find the best parameters compared with the conventional methods and show better performance than PI control using trial and error method, PI control using Butterworth filter design method or IMC method. BFOA will be studied and other existing conventional methods as well. Simulation will be done based on the mathematical model of the 3-phase separator
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Video big data: an agile architecture for systematic exploration and analytics
Video is currently at the forefront of most business and natural environments. In surveillance, it is the most important technology as surveillance systems reveal information and patterns for solving many security problems including crime prevention. This research investigates technologies that currently drive video surveillance systems with a view to optimization and automated decision support.
The investigation reveals some features and properties that can be optimised to improve performance and derive further benefits from surveillance systems. These aspects include system-wide architecture, meta-data generation, meta-data persistence, object identification, object tagging, object tracking, search and querying sub-systems. The current less-than-optimum performance is attributable to many factors, which include massive volume, variety, and velocity (the speed at which streaming video transmit to storage) of video data in surveillance systems.
Research contributions are 2-fold. First, we propose a system-wide architecture for designing and implementing surveillance systems, based on the authorsâ system architecture for generating meta-data. Secondly, we design a simulation model of a multi-view surveillance system from which the researchers generate simulated video streams in large volumes. From each video sequence in the model, the authors extract meta-data and apply a novel algorithm for predicting the location of identifiable objects across a well-connected camera cluster.
This research provide evidence that independent surveillance systems (for example, security cameras) can be unified across a geographical location such as a smart city, where each network is administratively owned and managed independently. Our investigation involved 2 experiments - first, the implementation of a web-based solution where we developed a directory service for managing, cataloguing, and persisting metadata generated by the surveillance networks. The second experiment focused on the set up, configuration and the architecture of the surveillance system. These experiments involved the investigation and demonstration of 3 loosely coupled service-oriented APIs â these services provided the capability to generate the query-able metadata.
The results of our investigations provided answers to our research questions - the main question being âto what degree of accuracy can we predict the location of an object in a connected surveillance networkâ. Our experiment also provided evidence in support of our hypothesis â âit is feasible to âexploreâ unified surveillance data generated from independent surveillance networksâ