Debashis Neogi

US Patent:

20080109090, May 8, 2008

Filed:

Nov 2, 2007

Appl. No.:

11/934156

Inventors:

Ali Esmaili - Allentown PA, US
Sanjay Mehta - Alburtis PA, US
Debashis Neogi - Emmaus PA, US
Carlos Valenzuela - Bethlehem PA, US

Assignee:

AIR PRODUCTS AND CHEMICALS, INC. - Allentown PA

International Classification:

G05B 13/02
G06F 3/048
G06G 7/48

US Classification:

700029000, 715772000, 703006000

Abstract:

Systems and methods are provided that allow a user to monitor, diagnose, and configure a process. A user interface may be presented that displays data received from process monitors, sensors, and multivariate models. The user interface may be interactive, allowing a user to select which composite and multivariate models are displayed. The user interface and multivariate model may be constructed and updated in real time. The user interface may present various data and interfaces, such as a representation of a composite variable in a multivariate model, a representation of the contribution of process variables to the composite variable, and a representation of a subset of the process variables. The user may select a point of the composite variable for analysis, and the interface may indicate the contribution and values of process variables at the selected point. The interface may be transmitted to a remote user.

US Patent:

20100076785, Mar 25, 2010

Filed:

Sep 25, 2008

Appl. No.:

12/284929

Inventors:

Sanjay Mehta - Alburtis PA, US
Debashis Neogi - Emmaus PA, US

Assignee:

Air Products and Chemicals, Inc. - Allentown PA

International Classification:

G06Q 50/00
G06F 17/30

US Classification:

705 3, 705 2

Abstract:

Systems and methods are provided for predicting rare events, such as hospitalization events. Data related to health and/or healthcare may be compiled from a number of sources and used to construct a predictive model. The predictive model employ Principal Component Analysis (PCA) and Partial Least Squares (PLS). The data may be arranged in a timeline, and formatted in such a way as to provide discrete temporal “batches”. This arrangement may facilitate the PCA and PLS decomposition of the data into predictive models. These models may then be applied to an individual's data, to create a prediction of healthcare related events.

US Patent:

20100076799, Mar 25, 2010

Filed:

Sep 25, 2008

Appl. No.:

12/284943

Inventors:

Michael Andrew Magent - Allentown PA, US
Debashis Neogi - Emmaus PA, US
Sanjay Mehta - Alburtis PA, US
Jean Jenkins - Chicago PA, US
Malcolm Merritt Waring - Shawnee on Delaware PA, US
Charles Roland Lewis - Fogelsville PA, US
Michael S. Toth - Allentown PA, US
Gregory Robert Glick - Allentown PA, US
Robert S. Barbieri - Schnecksville PA, US
Cecilia Anna Paulette Petit - Quakertown PA, US

Assignee:

Air Products and Chemicals, Inc. - Allentown PA

International Classification:

G06Q 10/00
G06Q 50/00

US Classification:

705 7, 705 2

Abstract:

Systems and methods are provided for predicting rare events, such as hospitalization events. A set of data records, each containing multiple attributes with one or more values (which may include an “unknown” value), may represent a root node of a decision tree. This root node may be partitioned based on one of the attributes, such that the concentration (e.g., “purity”) of a relevant outcome (e.g., the rare event) is increased in one node and decreased in another. This process may be repeated until a decision tree with sufficiently pure leaf nodes is created. This “purified” decision tree may then be used to predict one or more rare events.

US Patent:

20140174288, Jun 26, 2014

Filed:

Jan 9, 2013

Appl. No.:

13/737323

Inventors:

- Allentown PA, US
Debashis Neogi - Emmaus PA, US
Xianming Jimmy Li - Orefield PA, US
Pratik Misra - Breinigsville PA, US

Assignee:

Air Products and Chemicals, Inc. - Allentown PA

International Classification:

B01D 53/02

US Classification:

95 1, 96115, 96117, 96109

Abstract:

Apparatus and methods are disclosed that allow for the monitoring and analysis of production process data for a multi-step asynchronous cyclic production process (e.g. pressure swing adsorption) in a steady state plant (such as a steam methane reforming plant). Data collected from cooperating sensors is processed applying a moving window discrete Fourier transform (DFT). The transformed data can be further analyzed in the broader steady-state plant environment to accurately detect any process anomalies and avoid false alarms.