Lockheed Martin Energy Systems, Inc.

The United States of America As Represented by the United States

Electric Power Research Institute Incorporated

Univation Technologies, LLC

C Stuart Daw

Charles E A Finney

Lee M Hively

Ned E Clapp

William F Lawkins

Michael E Muhle

Ralph T Bailey

Thomas J Flynn

Timothy A Fuller

Ke Nguyen

James A Hawk

In some embodiments, a method of monitoring a resin-producing polymerization reaction in a fluid bed reactor system to generate reaction parameter data in on-line fashion, determining an indicator of at least one of entropy and complexity (for example, Kolmogorov or Shannon entropy) of each of at least two subsets of the reaction parameter data, and optionally also determining from at least one value of the indicator (for example, from a time series of Kolmogorov or Shannon entropy values) an indication of at least one of degree of resin stickiness, an approach to or imminence of resin stickiness, and an approach to or imminence of an unsafe or undesired reactor operating condition (e.g., that can result in sheeting or chunking). Optionally also, the reaction is controlled in response to at least one value of the indicator, for example, in an effort to prevent the occurrence of sheeting or another discontinuity event or to maintain the reactor in a stable, non-sticking condition.

Method for polymerization reaction monitoring with determination of entropy of monitored data

Methods for cyclone boiler flame diagnostics and control, including methods for monitoring the operating state of a cyclone furnace using linear and nonlinear signal analysis techniques, including temporal irreversibility and symbol sequence. Adjustments may be made in the air flow distribution to optimize performance. Signals for the main flame and lighter scanners are relatively independent, thereby allowing for independent control of the primary air flow to the burner and secondary air flow to the barrel.

Method for controlling air distribution in a cyclone furnace

Methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming.

Epileptic seizure prediction by non-linear methods

Methods and apparatus for automatically detecting epileptic seizures by monitoring and analyzing brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; determining that one or more trends in the nonlinear measures indicate a seizure, and providing notification of seizure occurrence.

Apparatus and method for epileptic seizure detection using non-linear

A control loop and fluidization quality analyzer for a fluidized bed utilizes time varying pressure drop measurements. A fast-response pressure transducer measures the overall bed pressure drop, or over some segment of the bed, and the pressure drop signal is processed to produce an output voltage which changes with the degree of fluidization turbulence.

Fluidization quality analyzer for fluidized beds

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Knoxville, TN, United States of America

C Stuart Daw