Knowledge of viscosity of flow streams is essential for the design and operation of production facilities, drilling operations and reservoir engineering calculations. The determination of the viscosity of a reservoir fluid at downhole conditions still remains a complex task due to the difficulty of designing a tool capable of measuring accurate rheological information under harsh operational conditions. This dissertation presents the evaluation of the performance of a novel device designed to measure the viscosity of a fluid at downhole conditions. The design investigated in this study addresses several limitations encountered in previous designs. The prototype was calibrated and tested with fluids with viscosities ranging from 1 to 28 cp under temperatures ranging from 100 to 160oF. Viscosity measurements were validated with independent measurements using a Brookfield viscometer. We proposed a mathematical model to describe the performance of the device for Power-law fluids. This model describes the response of the device as a function of the rheology of the fluid and the physical dimensions of the device. Experimental data suggests the validity of the model to predict the response of the device under expected operating conditions. This model can be used to calculate optimal dimensions of the device for customized target applications.

The production of bio-ethanol from lignocellulosic material requires a more efficient process to be feasible and compete with products from fossil fuels. There is a need to rapidly and nondestructively be able to determine key components during fermentation. Raman spectroscopy is a technique, which can be used to monitor the fermentation process in real-time and provide information about key components which can be accessed immediately, thus facilitating process control. A continuous system with membrane cell recycling was set up and fermentations were performed using Saccharomyces cerevisiae ATCC 96581. Fermentations were performed to test for optimal dilution rates and operating times, the effect of different sugar concentrations in the media feed, and which position in the system was optimal for Raman data collection. Raman data and aliquot samples for HPLC validation were continuously collected throughout the fermentations. Raman data was analysed with PLS models to obtain component concentrations, for which RMSE was calculated in order to compare to HPLC validation set. Fermentations were performed with synthetic glucose media as well as with poplar hydrolysate. It was shown that the continuous system with membrane cell recycling could achieve a glucose-to-ethanol conversion of between 75-100%. The process could be sufficiently monitored by Raman spectroscopy, and predicted concentrations were within the range of the validation set in most cases. However, the error of prediction varied between the different fermentations.

In order to better understand the changes occurring in the internal environment of the pyrolysis process a method of monitoring the internal environment in real time is the key objective of this study. To accomplish this objective four tasks were laid out in order to develop an effective way of monitoring the changes in gases present as pyrolysis is occurring as well as in material activation processing. For all processing the self-activation process was used which combines pyrolysis and thermal activation into a single step process. In the first task 10 hard wood species were activated and the resulting properties were compared to see the impact of wood species on the resulting carbon structures. In order to understand the impact of gas concentration on the resulting carbons the second task developed a gas sensor array which effectiveness was corroborated using GC-MS and then comparisons of the changes in the resulting were made. For the third task the gas sensor array was used to analyze the production of CO2 gas and a triple Gaussian model was developed to model the changes in gas production throughout processing. H2 gas production was modeled in the fourth task using the same Gaussian model as the third, where the results of both gas productions were compared showing the impact of processing parameters on gas production. With these four tasks completed we can see how our processing effects wood species similarly but at different rates, gas concentration was linked to changes in carbon structure, the effectiveness of our sensor was proven, a triple Gaussian model was developed to around gas production, and the impact of processing parameters on gas production was observed. With this Information a link between resulting carbon structure and gas content of the pyrolysis can be done and the changes in the pyrolysis environment were monitored in real time.

As the demand for energy increases, as well as the demand for renewable energy, Vattenfall, as network owner, receives many requests to connect new wind power to the grid. The limiting factor for how much wind power that can be connected to the grid is in this case the maximum current capacity of the overhead lines that is based on a line temperature limit. The temperature limit is set to ensure a safety distance between the lines and the ground. This master thesis project is a part of a research project at Vattenfall Research and Development that is examining the possibilities of increasing the allowed current on overhead lines in order to be able to connect more wind power to the existing network. Measured data from two overhead lines in southern Sweden is analyzed and the internal relations between the measured parameters are examined. The measured parameters are overhead line sag, line temperature, ambient temperature, solar radiation, wind speed and line current. The results indicate that there is a big load margin that could be utilized to increase the maximum current as long as further work could show that low winds at line height correlates with low wind at nacelle height. The results show that the sag versus line temperature is approximately linear within the measured temperature range. This means that a real-time-monitoring system measuring the line temperature should give adequate knowledge of the line position to ensure the safety distance. A model for the line temperature as a function of insolation, current, ambient temperature and wind speed has been estimated for one of the lines. Simulations show that a sudden increase in current at a worst-case scenario would give the operators about ten minutes to react before the line reaches the temperature limit.

The semiconductor manufacturing process is a complex process that can consist of hundreds if not thousands of steps. During this process an enormous amount of data is generated and collected by several different systems. Analyzing this data can be complicated and time consuming. But, in order to optimize the manufacturing process, it is important to be able to process data quickly and provide data consumers an easy, meaningful way to view the data. Data consumers at a management level need to view data differently than someone who works in the semiconductor fabrication plant (FAB) operating the manufacturing equipment or a maintenance technician who fixes and maintains the equipment. So, it is important to provide these different data views to the users in a logical, organized way. This paper will discuss what a dashboard is, an overview of the semiconductor manufacturing process, and one implementation of a dashboard for the semiconductor industry, the Semiconductor Manufacturing Dashboard (SMD). An explanation of the systems involved in collecting and loading the data, the database structures, and the web servers used for development and production will also be discussed. / text

© 2016 IEEE. This demo paper describes the architecture and communication protocols - both single hop and multi-hop - for DSRC devices. The paper also describes an Android application that enables visualization of real-time vehicle movements on Google map using DSRC and Bluetooth communication. The application receives information about position, speed and direction of mobility that multiple vehicles obtain through the GPS Receiver attached to their DSRC OBU. The android application communicates with one of the DSRC units through Bluetooth to gather real-time traces collected from all DSRC-equipped vehicles. The application displays live movement of these vehicles on Google map with their path history, speed and direction. The source code and installation files of this application will be released through the Open Source Application Development Portal (OSADP) hosted by the U.S. Department of Transportation.

Yes / Wastewater treatment is an energy-intensive operation. Energy consumption is forecast to increase by 60% in the forthcoming decade due to tightened legislation surrounding the discharge of final effluent to watercourses. Treatment plants rely on the time-consuming and unreliable biochemical oxygen demand to assess the quality of final effluent, leading to process inefficiencies. Here, the authors show that fluorescence spectroscopy is a robust technique for real-time monitoring of changes in effluent quality. Three portable fluorimeters were installed for one month at the final effluent discharge point of a large municipal wastewater treatment plant. The authors show that organic matter composition of the wastewater varies diurnally depending on the flow rate and antecedent rainfall. High fluorescence intensity and ammonia are attributed to sewage sludge liquor, which is regularly discharged to the treatment plant. Moreover, elevated fluorescence intensities were recorded as a result of process failure following a power outage. The study shows that online fluorescence analysis is capable of detecting both minor changes in effluent quality and issues with treatment process performance. / European Commission Framework Programme 7, Marie Curie IEF (PIEF-GA-2012-329962) and the Core Program, ANCS (PN 16.40.01.01).

We develop and test a probabilistic real-time streamflow-forecasting platform, Multimodel and Multiproduct Streamflow Forecasting (MMSF), that uses information provided by a suite of hydrologic models and satellite precipitation products (SPPs). The SPPs are bias-corrected before being used as inputs to the hydrologic models, and model calibration is carried out independently for each of the model-product combinations (MPCs). Forecasts generated from the calibrated models are further bias-corrected to compensate for the deficiencies within the models, and then probabilistically merged using a variety of model averaging techniques. Use of bias-corrected SPPs in streamflow forecasting applications can overcome several issues associated with sparsely gauged basins and enable robust forecasting capabilities. Bias correction of streamflow significantly improves the forecasts in terms of accuracy and precision for all different cases considered. Results show that the merging of individual forecasts from different MPCs provides additional improvements. All the merging techniques applied in this study produce similar results, however, the Inverse Weighted Averaging (IVA) proves to be slightly superior in most cases. We demonstrate the implementation of the MMSF platform for real-time streamflow monitoring and forecasting in the Mara River basin of Africa (Kenya & Tanzania) in order to provide improved monitoring and forecasting tools to inform water management decisions.

Cryosurgery is the destruction of undesired biological tissues by freezing. Modern cryosurgery is frequently performed as a minimally-invasive procedure, where multiple hypodermic, needle-shaped cryoprobes are inserted into the target area to be treated. The aim of the cryosurgeon is to maximize cryoinjury within a target region, while minimizing damage to healthy surrounding tissues. There is an undisputed need for temperature-field reconstruction during minimally invasive cryosurgery to help the cryosurgeon achieve this aim. The work presented in this thesis is a part of ongoing project at the Biothermal Technology Laboratory (BTTL), to develop hardware and software tools to accomplish real-time temperature field reconstruction. The goal in this project is two-fold: (i) to develop the hardware necessary for miniature, wireless, implantable temperature sensors, and (ii) to develop mathematical techniques for temperature-field reconstruction in real time, which is the focus of the work presented in this thesis. To accomplish this goal, this study proposes a computational approach for real-time temperature-field reconstruction, combining data obtained from various sensing modalities such as medical imaging, cryoprobe-embedded sensors, and miniature, wireless, implantable sensors. In practice, the proposed approach aims at solving the inverse bioheat transfer problem during cryosurgery, where spatially distributed input data is used to reconstruct the temperature field. Three numerical methods have been developed and are evaluated in the scope of this thesis. The first is based on a quasi-steady approximation of the transient temperature field, which has been termed Temperature Field Reconstruction Method (TFRM). The second method is based on analogy between the fields of temperature and electrical potential, and is thus termed Potential Field Analogy Method (PFAM). The third method is essentially a hybrid of TFRM and PFAM, which has shown superior results. Each of these methods has been benchmarked against a full-scale finite elements analysis using the commercial code ANSYS. Benchmarking results display an average mismatch of less than 2 mm in 2D cases and less than 3 mm in 3D cases for the location of the clinically significance isotherms of -22°C and -45°C. In an advanced stage of numerical methods evaluation, they have been validated against experimental data, previously obtained at the BTTL. Those experiments were conducted on a gelatin solution, using proprietary liquid-nitrogen cryoprobes and a cryoheater to simulate urethral warming. The design of the experiment was aimed at creating a 2D heat-transfer problem. Validation results against experimental data suggest an average mismatch of less than 2 mm, for the hybrid of TFRM + PFAM method, which is of the order of uncertainty in estimating the freezing front location based on ultrasound imaging.

The scientific studies associated with material engineering and device miniaturization are the core concepts for future technology innovation. The exploring and tailoring of material properties of amorphous magnetic microwires, recently, have revealed remarkable high sensitive magnetic field sensitivity down to the picoTesla regime at room temperature. This superior magnetometer is highly promising for active sensing and real-time monitoring building block for modern industrial devices and healthcare applications. The low-field, high sensitivity regime of the GMI response over a wide frequency range (1 MHz - 1 GHz) in the Co-rich melt-extracted microwires was optimized through novel Joule annealing methods (single- and multi-step current annealing techniques). Optimization of current value through multi-step current annealing (MSA) from 20 mA to 100 mA for 10 minutes is the key to improving the GMI ratio, and its field sensitivity up to 760% and 925%/Oe at f ≈ 20 MHz. The respective GMI ratio and field sensitivity are 1.75 times and 17.92 times higher than those of the as-prepared counterpart. The employment of the MSA technique successfully enhances the surface domain structures of the Co-rich microwires. This alternative tailoring method is suitable for improving the GMI sensitivity for a small field detection. The high sensitive response of the GMI to a weak magnetic field is highly promising for biomedical sensing applications. Real-time monitoring of position, motion, and rotation of a non-stationary object is crucial for product packaging, conveying, tracking, and safety compliance in industrial applications. The effectiveness of current sensing technology is limited by sensing distance and messy environments. A new class of high-frequency GMI-based sensor was designed and fabricated using the optimal Co-rich microwire. The impedance spectrum from the optimal sensing element showed a high GMI ratio and high field sensitivity response at low magnetic fields. The GMI sensor based longitudinal effect was found to be more sensitive than the commercially available Gaussmeters. The practical utility of the high sensitivity of the miniaturized sensor at weak magnetic fields for far-off distance monitoring of position, speed and gear rotating was demonstrated. This GMI-based sensor is highly promising for real-time position detection, oscillatory motion monitoring, and predictive failure of a rotating gear for industrial applications. Monitoring the rate of respiration and its pattern is crucial to assessing an individual’s health or progression of an illness, creating a pressing need for fast, reliable and cost-effective monitors. A new sensor based on a magnetic coil, which is made of Co-rich melt-extracted microwire for the detection of small magnetic fields was fabricated. The 3 mm diameter coil is wound from a Co-rich magnetic microwire. Unlike some typical solenoids, the MMC is sensitive to small magnetic fields due to a significant change in impedance attributed to the high-frequency giant magneto-impedance (GMI) effect. An application of the MMC sensor for the detection of a position-varying source of a small magnetic field (~0.01 – 10 Oe) in real-time bio-mechanical movement monitoring in human was demonstrated. This newly developed MMC magneto-LC resonance technology is highly promising for active respiratory motion monitoring, eye movement detection and other biomedical field sensing applications.