Patricia Denise Mackenzie

Mineral precipitation and porosity losses in granular iron columns.

As permeable reactive barriers containing zero-valent iron are becoming more widely used to remediate contaminated groundwaters, there remains much uncertainty in predicting their long-term performance. This study focuses on two factors affecting performance and lifetime of the granular iron media: plugging at the treatment zone entrance and precipitation in the bulk iron media. Plugging at the system entrance is due principally to mineral precipitation promoted by dissolved oxygen in the influent groundwater and is an issue in aerobic aquifers or in above-ground canister tests. Designs to minimize plugging in field applications where the groundwater is oxygenated include the use of larger iron particles and admixing sand of comparable size with the iron particles. Beyond the entrance zone, the groundwater in anaerobic and mineral precipitation leads to porosity losses in the bulk iron media, potentially reducing flow through the treatment zone. The nature of the mineral precipitation and the factors that affect extent of mineral precipitation have been examined by a variety of tools, including tracer tests, aqueous inorganic profiles, and surface analytical techniques. At short treatment times, porosity losses as measured by tracer tests are due mainly to Fe(OH)(2) precipitates and possible entrapment of a film of hydrogen gas on the iron surfaces. Over longer treatment times, precipitation of Fe(OH)(2) and FeCO(3) in low carbonate waters and of Fe(OH)(2), FeCO(3) and CaCO(3) in higher carbonate waters begin to dominate porosity losses. The control of pH within the iron media by addition of ferrous sulfide was shown not to reduce significantly calcium and carbonate precipitates, indicating that mineral precipitation is controlled by more than simple carbonate equilibrium considerations.

A grid-based clustering method for mining frequent trips from large-scale, event-based telematics datasets

Telematics systems that integrate wireless communications with sensor-based monitoring and location-aware applications have been widely deployed for mobile asset tracking and condition monitoring. In asset tracking field, exploring the data that relate to asset behaviors is critical to understand asset utilization, efficiency, distribution, operation, and many other important aspects in the supply chain. Prior work on analyzing GPS-based patterns has mainly been performed on time-based datasets. In this paper, we describe a scalable clustering algorithm to discover frequently repeated trips from large-scale, event-based telematics datasets collected via a satellite-based tracking system. We first transform GPS traces into a list of trips. Then we present a grid-based hierarchical clustering algorithm to discover frequent spatial patterns among all trips. We evaluate the effectiveness of the proposed algorithm against a large-scale, real-world dataset collected from tracking over a hundred of thousand assets and prove its feasibility. Through these experimental results, we show that the proposed algorithm significantly reduces the computational time needed for clustering as opposed to the traditional hierarchical clustering based on pair-wise comparison.

Toward an Intelligent Brokerage Platform: Mining Backhaul Opportunities in Telematics Data

In commercial transportation operations, one of the largest wasteful expenditures is the movement of tractor trailers with little or no cargo. Analysis of interfleet data shows many lost opportunities for identifying backhauling loads—cargo that could have been moved by an otherwise empty trailer on its return from a delivery point to its home base. Brokerage systems that facilitate matching of load-sharing and backhaul opportunities currently do not incorporate monitoring of real-time, geo-based information, analysis of historical geo-based information, and user-calibrated preferences from all brokerage participants. Future intelligent brokerage systems will need to provide a full range of services including supply chain visibility and automated identification of potential collaborations based on historical trends. In this paper an algorithm is described for identifying load-sharing and backhaul opportunities based on the detection of patterns in large-scale, event-based telematics network data.

Analysis of State Transition Diagrams for RFID-Based Two-Way Access Control

The logic for determining the passing direction of a person through a two-way access control portal is rather complicated. This paper analyzes the state transition diagrams based on sensor fusion data to clarify the confusing decision making logic. An RFID-based access control system, coupled with infrared sensors, is implemented in a two-way portal to verify the proposed technique

Mining trips from telematics dataset for value-added logistics applications in asset tracking systems

With the recent improvements and cost reductions in GPS technology and wireless communication, telematics systems that integrate wireless communications with sensor-based monitoring and location-aware applications have been widely deployed for mobile asset tracking and condition monitoring. In such telematics systems, mining patterns from GPS datasets and exploring the useful data for asset behaviors is vital in order to understand asset utilization, efficiency, distribution, operation, and many other important aspects in the supply chain. In this paper we mine trips based on heuristic rules from a large-scale, real world, event-based telematics dataset that is noisy and sparse in nature. A series of logistics applications built on trip analysis are presented, including ETA prediction, Mileage estimation, Backhaul detection, and Supply chain network analysis.

Cloud Computing-Based Marketplace for Collaborative Design and Manufacturing

This paper introduces an open-source, interoperable platform for real-time collaboration in complex product lifecycle development across multiple companies. Each segment of the lifecycle, including product conception, design, analysis, prototyping, component sourcing, manufacturing and assembly, logistics and delivery, and services from installation to maintenance, repair and overhaul, can benefit from this collaboration through easy access, development, deployment, and integration of heterogeneous models and data. The platform is built on an elastic cloud-computing environment, which provides efficient scaling of computational performance needs to support the collaboration platform. We believe that this platform will enable organizations of all sizes to enter a new digital age of integrated product design, manufacturing and service systems.