7,572 research outputs found
TIGHTENING THE LOOP ON THE CIRCULAR ECONOMY: DISTRIBUTED PLASTIC RECYCLING WITH AN OPEN SOURCE RECYCLEBOT
Following the goals of a circular economy, the growth of both plastic consumption and prosumer 3-D printing are driving an interest in producing 3-D printer filament from waste plastic. However, traditional recycling can have a significant environmental impact as it demands the collection and transportation of relatively low-density waste plastics to collection centers and reclamation facilities for separation and reconstruction. Compared to the traditional recycling, distributed recycling (where consumers directly recycle their own waste) has the potential to reduce energy consumption because it can save the energy for transportation needed in conventional recycling. A promising method of such distributed plastic recycling is to upcycle plastic waste into 3-D printing filament with a recyclebot, which is an open source waste plastic extruder.
In order to characterize the energy sustainability of this distributed recycling method, this study quantifies the embodied energy of a vertical DC solar-photovoltaic powered recyclebot based on life cycle energy analysis and compares it to horizontal AC recyclebot, conventional recycling and production of virgin 3-D printer filament. The energy payback time (EPBT) is calculated using the embodied energy of the materials making up the recyclebot itself and found to be about 5 days for extrusion of poly lactic acid (PLA) filament or 2.5 days for extrusion of acrylonitrile butadiene styrene (ABS) filament. The EPBT of a mono-crystalline silicon solar photovoltaic system is about 2.6 years alone. However, this can be reduced by over 96% if the solar photovoltaic system powers recyclebot to produce PLA filament from waste plastic (EPBT is only 0.10 year or about a month). Likewise, if ABS filament is produced from a recyclebot powered by solar PV system, the energy saved is 90.6-99.9 MJ/kg and 26.33-29.43 kg of ABS filament needs to be produced in about half a month for the system to pay for itself. The results clearly show that the solar PV system powered recyclebot is already an excellent way to save energy for sustainable development.
If the recyclebot is combined with an open source self-replicating rapid prototyper (RepRap) 3-D printer, then the post-consumer plastics can be turned into useful and more valuable products directly. In order to analyze the impact of combining these two methods, this project also combines the distributed recycling method using a vertical recyclebot to make filament with distributed manufacturing using a delta RepRap to print useful products from post-consumer e-waste. Specifically, this study analyzes the recycling of ABS from computer waste into valuable consumer products pre-designed in the digital commons. The total electrical energy consumption for the combined process is monitored and an economic evaluation is completed. It is clear that using traditional recycling and manufacturing methods to produce an ABS product consumes more than double the energy compared to coupled distributed recycling and manufacturing method for complex products. Even more energy is saved for simple products. Simultaneously products valued in dollars can be made for pennies using the more environmentally-responsible combined processes. It is clear from the results that in the short-to-medium term, waste plastic from discarded e-waste can be significantly upcycled at the individual level using this commons-based approach. This tightening of the loop of the circular economy is a benefit of the environment and sustainability as well as the economic stability of consumers/prosumers
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Signal Processing for Wireless Power and Information Transfer
The rapid development of the Internet of Things (IoT) and wireless sensor network (WSN) technologies enable easy access and control of a variety forms of information and data from numerous number of smart devices, and give rise to many novel applications and research areas such as smart home, machine type communications, etc. However due to the small sizes, sophisticated environment, and large number of devices in network, it is hard to directly power the devices from grid. Hence the power connectivity remains one of the major issues that needs to be addressed for related IoT applications. Wireless power transfer (WPT) and backscatter communications are provisioned to be prominent solutions to overcome the power connectivity challenge, but they suer strong efficiency limitation which becomes the barrier to universally popularize such technologies. On the other hand, network optimization is also a research focus of such applications which significantly affects the performance of the system due to the high volume of connected devices and different features. In this thesis we propose advanced techniques to overcome the challenges on the low efficiency and network design of the wireless information and power transfer systems. The thesis consists of two parts. In the first part we focus on the power transmitter design which addresses the low efficiency issue associated with backscatter communication and WPT. In Chapter 2, we consider a backscatter RFID system with the multi-antenna reader and propose a blind transmit and receive adaptive beamforming algorithm. The interrogation range and data transmission performance are both investigated under such configuration. In Chapter 3 we study wireless power transfer by the beamspace large-scale MIMO system with lens antenna arrays. We first present the WPT model for the beamspace MIMO which is derived from the spatial MIMO model. By constraining on the number of RF chains in the transmitter, we formulate two WPT optimization problems: the sum power transfer problem and the max-min power transfer problem. For both problems we consider two different transmission schemes, the multi-stream and uni-stream transmissions, and we propose different algorithms to solve both problems in both schemes respectively. In the second part we study the network optimization problems in the WPT and backscatter systems. In Chapter 4, we study the resource allocation problem for a RF-powered network, where the objective is to maximize the total data throughput of all sensors. We break the problem into two subproblems: the sensor battery energy utilization problem and the charging power allocation problem of the central node, which is an RF power transmitter that transmits RF power to the sensors. We analyze and show several key properties of both problems, and then propose computationally efficient algorithms to solve both problems optimally. In Chapter 5, we study the time scheduling problem in RF-powered backscatter communication networks, where all transmitters can operates in either backscattering mode or harvest-then-transmit (HTT) mode. The objective is to decide the operating mode of each transmitter and minimize the total transmission time of the network. We also consider both ideal and realistic transmitters based on different internal power consumption models for HTT transmitters. Under both transmitter models we show several key properties, and propose bisection based algorithms which has low computational complexity that solves the problem optimally. The results are then extended to the massive MIMO regime
Particle Adhesion in Cyclone Particle Separators
Cyclone particle separators use centrifugal forces generated by swirling flow to separate particles from particle-laden flow. They are one of the key components of vacuum cleaners. However, fine particles tend to adhere on the internal wall of the cyclone and, in the worst case; this can cause severe blockage of the cyclone exit. Interactions between particles and a solid wall in cyclone are very complex and affected by many factors. Thus, the mechanism of particle adhesion is poorly understood. This book chapter will begin with a brief introduction of the working principle and common configurations of cyclone particle separators. The characteristics of particle adhesion patterns in a cyclone particle separator will then be presented and the mechanisms will be discussed based on our experimental results. After this, an experimental study supplemented by CFD simulations aiming to understand the effect of particle inlet positions on particle adhesion will be reported. Finally, a 2D numerical simulation which models interactions of particles with a solid wall and provides an insight of the key parameters that influence the particle adhesion process will be described. The finding from this work will benefit the design of cyclone particle separators for vacuum cleaners
PIV measurements of the interaction of synthetic jets with a zero pressure gradient laminar boundary layer
Practical Deep Reinforcement Learning Approach for Stock Trading
Stock trading strategy plays a crucial role in investment companies. However,
it is challenging to obtain optimal strategy in the complex and dynamic stock
market. We explore the potential of deep reinforcement learning to optimize
stock trading strategy and thus maximize investment return. 30 stocks are
selected as our trading stocks and their daily prices are used as the training
and trading market environment. We train a deep reinforcement learning agent
and obtain an adaptive trading strategy. The agent's performance is evaluated
and compared with Dow Jones Industrial Average and the traditional min-variance
portfolio allocation strategy. The proposed deep reinforcement learning
approach is shown to outperform the two baselines in terms of both the Sharpe
ratio and cumulative returns
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