Machine learning-based prediction of a BOS reactor performance from operating parameters

A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of decarburization (dc/dt). Correlation analysis showed, as expected a strong positive correlation between the rate of decarburization (dc/dt) and total oxygen flow. On the other hand, the decarburization rate exhibited a negative correlation with lance height. Less obviously, the decarburization rate, also showed a positive correlation with temperature of the waste gas and CO2 content in the waste gas. The second purpose was to train the pilot-plant dataset and develop a neural network based regression to predict the decarburization rate. This was used to predict the decarburization rate in a BOS furnace in an actual manufacturing plant based on lance height and total oxygen flow. The performance was satisfactory with a coefficient of determination of 0.98, confirming that the trained model can adequately predict the variation in the decarburization rate (dc/dt) within BOS reactors. View Full-Tex

Performance Studies of Bulk Micromegas of Different Design Parameters

The present work involves the comparison of various bulk Micromegas detectors having different design parameters. Six detectors with amplification gaps of $64,~128,~192,~220 ~\mu\mathrm{m}$ and mesh hole pitch of $63,~78 ~\mu\mathrm{m}$ were tested at room temperature and normal gas pressure. Two setups were built to evaluate the effect of the variation of the amplification gap and mesh hole pitch on different detector characteristics. The gain, energy resolution and electron transmission of these Micromegas detectors were measured in Argon-Isobutane (90:10) gas mixture while the measurements of the ion backflow were carried out in P10 gas. These measured characteristics have been compared in detail to the numerical simulations using the Garfield framework that combines packages such as neBEM, Magboltz and Heed.Comment: arXiv admin note: text overlap with arXiv:1605.0289

Human transfer functions used to predict system performance parameters

Automatic, parameter-tracking, model-matching technique compares the responses of a human operator with those of an analog computer model of a human operator to predict and analyze the performance of mechanical or electromechanical systems prior to construction. Transfer functions represent the input-output relation of an operator controlling a closed-loop system

Quantifying "Cliffs" in Design Space

This paper studies the regions of parameter space of engineering design in which performance is sensitive to design parameters. Some of these parameters (for example, the dimensions and compositions of components) constitute the design, but others are intrinsic properties of materials or Nature. The paper is concerned with narrow regions of parameter space, "cliffs", in which performance (some measure of the final state of a system, such as ignition or non-ignition of a flammable gas, or failure or non-failure of a ductile material subject to tension) is a sensitive function of the parameters. In these regions performance is also sensitive to uncertainties in the parameters. This is particularly important for intrinsically indeterminate systems, those whose performance is not predictable from measured initial conditions and is not reproducible.Comment: 23 pp., 5 fig

Effect of atmospheric parameters on silicon cell performance

The effects of changing atmospheric parameters on the performance of a typical silicon solar cell were calculated. The precipitable water vapor content, airmass and turbidity were varied over wide ranges and the normal terrestrial distribution of spectral irradiance was studied. The cell short-circuit current was then computed for each spectral irradiance distribution using the cell spectral response. Data are presented in the form of calibration number (cell current/incident irradiance) vs. water vapor content or turbidity

Achievable Performance in Product-Form Networks

We characterize the achievable range of performance measures in product-form networks where one or more system parameters can be freely set by a network operator. Given a product-form network and a set of configurable parameters, we identify which performance measures can be controlled and which target values can be attained. We also discuss an online optimization algorithm, which allows a network operator to set the system parameters so as to achieve target performance metrics. In some cases, the algorithm can be implemented in a distributed fashion, of which we give several examples. Finally, we give conditions that guarantee convergence of the algorithm, under the assumption that the target performance metrics are within the achievable range.Comment: 50th Annual Allerton Conference on Communication, Control and Computing - 201

FCC-ee accelerator parameters, performance and limitations

CERN has recently launched the Future Circular Collider (FCC) study to deal with all aspects of an ambitious post-LHC possible programme. The emphasis of the study is on a 100 TeV proton collider to be housed in a 80-100 km new ring in the Geneva region. An electron machine will also be considered as a possible intermediate first step (FCC-ee). The study benefits from earlier work done in the context of TLEP and has already published a parameter table, to serve as the basis for the work to be done. The study aims to publish a conceptual design report at around 2018. The recent discovery of a light Higgs boson has opened up considerable interest in circular e+e- Higgs factories around the world. FCC-ee is capable of very high luminosities in a wide centre-of-mass (ECM) spectrum from 90 to 350 GeV. This allows the very precise study of the Z, W and H bosons as well as the top quark, allowing for meaningful precision tests of the closure of the Standard Model.Comment: presented at conference ICHEP2014, 37th conference on High Energy Physics, Valencia, 2-9 July 201