Carbon Catcher Design Report

Overview. The design of the overall Carbon Catcher project can be separated into four distinct systems, each of which is assigned a specialized committee. The committee names and responsibilities are listed below: Air Mover The overall goal for the Air Mover committee is the design of the turbine assembly. As the overall goal of the project is to collect and separate carbon dioxide from the air, one of the most important parts is to actually get the air to pass through the carbon-catching membrane. Passive air would not give a significant enough yield rate to make the carbon dioxide collection rate impactful, thus air must be sucked through a vacuum/turbine. Membrane The goal of Membrain is to create a membrane that can filter out CO2 through various methods. These methods are limited, due to there being such variety, to certain techniques and membrane material types that have been decided, prior, by the committee. Most membranes will be geared towards utilizing temperature and pressure along with gaseous speed and flow rate. In addition, examining certain treatments, such as regeneration of material, and replacements will be looked into as well, to see how it fares in sustainability. Carbon Storer The Carbon Storer committee will design a store and transport system for fluid CO2 after it is extracted from the atmosphere. Primary considerations include geological solutions, cost-effective materials, and analysis methods to improve overall capacity and efficiency. Additionally, the committee will select an environmentally and economically sustainable method of recycling the captured CO2. PyControl The PyControl committee will design a series of sensors and actuators, which will primarily support the sequestration and pipeline systems present in the Carbon Storer Committee and direct air capture system in Air Mover. The design can be broken into four control layers: Input/Output, Field Controllers, Data, and Supervisory. Goal The overarching goal of Carbon Catcher is to design a cost-effective, scalable atmospheric carbon dioxide removal system that is capable of being deployed in a variety of urban environments and may fit a variety of different customer requirements or requests

Navigation system for a mobile robot incorporating trinocular vision for range imaging

This research focuses on the development of software for the navigation of a mobile robot. The software developed to control the robot uses sensory data obtained from ultra sound, infra red and tactile sensors, along with depth maps using trinocular vision. Robot navigation programs were written to navigate the robot and were tested in a simulated environment as well as the real world. Data from the various sensors was read and successfully utilized in the control of the robot motion. Software was developed to obtain the range and bearing of the closest obstacle in sight using the trinocular vision system. An operator supervised navigation system was also developed that enabled the navigation of the robot based on the inference from the camera images

Minimum-Energy Exploration and Coverage for Robotic Systems

This dissertation is concerned with the question of autonomously and efficiently exploring three-dimensional environments. Hence, three robotics problems are studied in this work: the motion planning problem, the coverage problem and the exploration problem. The work provides a better understanding of motion and exploration problems with regard to their mathematical formulation and computational complexity, and proposes solutions in the form of algorithms capable of being implemented on a wide range of robotic systems.Because robots generally operate on a limited power source, the primary focus is on minimizing energy while moving or navigating in the environment. Many approaches address motion planning in the literature, however few attempt to provide a motion that aims at reducing the amount of energy expended during that process. We present a new approach, we call integral-squared torque approximation, that can be integrated with existing motion planners to find low-energy and collision-free paths in the robot\u27s configuration space.The robotics coverage problem has many real-world applications such as removing landmines or surveilling an area. We prove that this problem is inherently difficult to solve in its general case, and we provide an approach that is shown to be probabilistically complete, and that aims at minimizing a cost function (such as energy.) The remainder of the dissertation focuses on minimum-energy exploration, and offers a novel formulation for the problem. The formulation can be directly applied to compare exploration algorithms. In addition, an approach that aims at reducing energy during the exploration process is presented, and is shown through simulation to perform better than existing algorithms

Usability evaluation of a web-based tool for supporting holistic building energy management

This paper presents the evaluation of the level of usability of an intelligent monitoring and control interface for energy efficient management of public buildings, called BuildVis, which forms part of a Building Energy Management System (BEMS.) The BEMS ‘intelligence’ is derived from an intelligent algorithm component which brings together ANN-GA rule generation, a fuzzy rule selection engine, and a semantic knowledge base. The knowledge base makes use of linked data and an integrated ontology to uplift heterogeneous data sources relevant to building energy consumption. The developed ontology is based upon the Industry Foundation Classes (IFC), which is a Building Information Modelling (BIM) standard and consists of two different types of rule model to control and manage the buildings adaptively. The populated rules are a mix of an intelligent rule generation approach using Artificial Neural Network (ANN) and Genetic Algorithms (GA), and also data mining rules using Decision Tree techniques on historical data. The resulting rules are triggered by the intelligent controller, which processes available sensor measurements in the building. This generates ‘suggestions’ which are presented to the Facility Manager (FM) on the BuildVis web-based interface. BuildVis uses HTML5 innovations to visualise a 3D interactive model of the building that is accessible over a wide range of desktop and mobile platforms. The suggestions are presented on a zone by zone basis, alerting them to potential energy saving actions. As the usability of the system is seen as a key determinate to success, the paper evaluates the level of usability for both a set of technical users and also the FMs for five European buildings, providing analysis and lessons learned from the approach taken

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Product Development Process for Small Unmanned Aerial Systems

The DoD has recognized the need for persistent Intelligence, Surveillance and Reconnaissance (ISR) over the last two decades. Recent developments with commercial drones have changed the market structure; there is now a thriving and extensive market base for drone based remote sensing. This research provides system engineering methods to support the DoD use of this burgeoning market to meet operational ISR needs. The three contributions of this research are: a process to support Small Unmanned Aerial Systems (SUAS) design, tools to support the design process, and tools to support risk assessment and reduction for both design and operations. The process and tools are presented via an exemplar design for an ISR SUAS mission. The exemplar design flows from user needs through to an allocated baseline with an assessment of system reliability based on a compilation of commercial component reliability and failure modes

Intelligent Approaches For Modeling And Optimizing Hvac Systems

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This research will evaluate model-based optimization processes (OP) for HVAC systems utilizing MATLAB, genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) will be tested using data collected from the HVAC system servicing the Academic building on the campus of NC A&T State University. Intelligent approaches for modeling and optimizing HVAC systems are developed and validated in this research. The optimization process (OP) including the STMs with genetic algorithms (GA) enables the ideal operation of the building’s HVAC systems when running in parallel with a building automation system (BAS). Using this proposed optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, reheat (or zone supply air temperature, Tz), and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. HVAC system component models were developed and validated against both simulated and monitored real data of an existing VAV system. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013. The proposed optimization process is validated on an existing VAV system for three summer months (May, June, August). This proposed research deals primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. Recent technological advances in computing power, sensors, and databases will influence the cost savings and scalability of the system. Improved energy efficiencies of existing Variable Air Volume (VAV) HVAC systems can be achieved by optimizing the control sequence leading to advanced BAS programming. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort