Development of dry coal feeders

Design and fabrication of equipment of feed coal into pressurized environments were investigated. Concepts were selected based on feeder system performance and economic projections. These systems include: two approaches using rotating components, a gas or steam driven ejector, and a modified standpipe feeder concept. Results of development testing of critical components, design procedures, and performance prediction techniques are reviewed

Cyber-Virtual Systems: Simulation, Validation & Visualization

We describe our ongoing work and view on simulation, validation and visualization of cyber-physical systems in industrial automation during development, operation and maintenance. System models may represent an existing physical part - for example an existing robot installation - and a software simulated part - for example a possible future extension. We call such systems cyber-virtual systems. In this paper, we present the existing VITELab infrastructure for visualization tasks in industrial automation. The new methodology for simulation and validation motivated in this paper integrates this infrastructure. We are targeting scenarios, where industrial sites which may be in remote locations are modeled and visualized from different sites anywhere in the world. Complementing the visualization work, here, we are also concentrating on software modeling challenges related to cyber-virtual systems and simulation, testing, validation and verification techniques for them. Software models of industrial sites require behavioural models of the components of the industrial sites such as models for tools, robots, workpieces and other machinery as well as communication and sensor facilities. Furthermore, collaboration between sites is an important goal of our work.Comment: Preprint, 9th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2014

Index to 1981 NASA Tech Briefs, volume 6, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1981 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Controlled Ecological Life Support Systems (CELSS) conceptual design option study

Results are given of a study to explore options for the development of a Controlled Ecological Life Support System (CELSS) for a future Space Station. In addition, study results will benefit the design of other facilities such as the Life Sciences Research Facility, a ground-based CELSS demonstrator, and will be useful in planning longer range missions such as a lunar base or manned Mars mission. The objectives were to develop weight and cost estimates for one CELSS module selected from a set of preliminary plant growth unit (PGU) design options. Eleven Space Station CELSS module conceptual PGU designs were reviewed, components and subsystems identified and a sensitivity analysis performed. Areas where insufficient data is available were identified and divided into the categories of biological research, engineering research, and technology development. Topics which receive significant attention are lighting systems for the PGU, the use of automation within the CELSS system, and electric power requirements. Other areas examined include plant harvesting and processing, crop mix analysis, air circulation and atmosphere contaminant flow subsystems, thermal control considerations, utility routing including accessibility and maintenance, and nutrient subsystem design

Contribution to the capacity determination of semi-mobile in-pit crushing and conveying systems

As ore grades decline, waste rock to ore ratios increase and mines become progressively deeper mining operations face challenges in more complex scenarios. Today´s predominant means of material transport in hard-rock surface mines are conventional mining trucks however despite rationalisation efforts material transportation cost increased significantly over the last decades and currently reach up to 60% of overall mining. Thus, considerations and efforts to reduce overall mining costs, promise highest success when focusing on the development of more economic material transport methods. Semi-mobile in-pit crusher and conveyor (SMIPCC) systems represent a viable, safer and less fossil fuel dependent alternative however its viability is still highly argued as inadequate methods for the long term projection of system capacity leads to high uncertainty and consequently higher risk. Therefore, the objective of this thesis is to develop a structured method for the determination of In-pit crusher and conveyor SMIPCC system that incorporates the random behaviour of system elements and their interaction. The method is based on a structured time usage model specific to SMIPCC system supported by a stochastic simulation. The developed method is used in a case study based on a hypothetical mine environment to analyse the system behaviour with regards to time usage model component, system capacity, and cost as a function of truck quantity and stockpile capacity. Furthermore, a comparison between a conventional truck & shovel system and SMIPCC system is provided. Results show that the capacity of a SMIPCC system reaches an optimum in terms of cost per tonne, which is 24% (22 cents per tonne) lower than a truck and shovel system. In addition, the developed method is found to be effective in providing a significantly higher level of information, which can be used in the mining industry to accurately project the economic viability of implementing a SMIPCC system

Deep Learning based Prediction of Clogging Occurrences during Lignocellulosic Biomass Feeding in Screw Conveyors

Over the last decades, there have been substantial government and private sector investments to establish a commercial biorefining industry that uses lignocellulosic biomass as feedstock to produce fuels, chemicals, and other products. However, several biorefining plants experienced material conveyance problems due to the variability and complexity of the biomass feedstock. While the problems were reported in most conveyance unit operations in the biorefining plants, screw conveyors merit special attention because they are the most common conveyors used in biomass conveyance and typically function as the last conveyance unit connected to the conversion reactors. Thus, their operating status affects the plant production rate. Therefore, detecting emerging clogging events and, ultimately, proactively adjusting operating conditions to avoid downtime is crucial to improving overall plant economics. One promising solution is the development of sensor systems to detect clogging to support automated decision-making and process control. In this study, two deep learning based algorithms are developed to detect an imminent clogging event based on the current signature and vibration signals extracted from the sensors connected to the benchtop screw conveyor system. The study focuses on three biomass materials (switchgrass, loblolly pine, and hybrid poplar) and is designed around three research objectives. The first research objective examines the relationship between the occurrence of clogging in a screw conveyor and the current and vibration signals on the different feedstocks to establish the presence of clogging event fingerprint that could be exploited in automated decision-making and process-control. The second research objective applies two deep learning algorithms to the current and vibration signals to detect the imminent occurrence of clogging and its severity for decision making with an optimization procedure. The third objective examines the robustness of the optimized deep learning algorithm to detection imminent clogging events when feedstock properties (size distribution and moisture contents) vary. In the long-term, the early clogging detection methodology developed in this study could be leveraged to develop smart process controls for biomass conveyance