Chevron U.s.a. Inc.

University of Southern California

Advanced Micro Devices Corporation

Fisher Controls International LLC

City University of Hong Kong

Si-Zhao J Qin

Lisa Ann Brenskelle

Alisha Deshpande

Yingying Zheng

Yining Dong

Christopher Allen Bode

Alexander James Pasadyn

Michael Lee Miller

Robert J Chong

Qingsu Wang

Jin Wang

Terrence J Riley

Glen W Scheid

Minghua Chen

Yanfang Mo

Qiulin Lin

Michael Barham

Guy Thompson Borders

A method of managing real-time electrical energy storage management from an electricity storage device to maximise the reduction of the use of a mains electricity grid during peak cost charging periods on the mains electricity grid, including: calculating an ideal offline use of the electricity storage device using ideal predicted parameters; calculate a plurality of electrical storage device storage and electrical discharge models using a plurality of algorithms based on recorded data; calculate the competitive ratio for each of the algorithms and the ideal offline use; and use power from the electricity storage device based on an optimal competitive ratio.

Methods for online peak demand reduction of large load users with energy storage discharge

Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

Automated model building and updating environment

A method for extracting a set of principal time series data of dynamic latent variables. The method includes detecting, by a plurality of sensors, dynamic samples of data each corresponding to one of a plurality of original variables. The method also includes analyzing, using a controller, the dynamic samples of data to determine a plurality of latent variables that represent variation in the dynamic samples of data. The method also includes selecting, by the controller, at least one inner latent variable that corresponds to at least one of the plurality of original variables. The method also includes estimating an estimated current value of the at least one inner latent variable based on previous values of the at least one inner latent variable.

System and method for extracting principal time series data

Methods and systems for detecting a fault in a data set from an industrial process are disclosed. One method includes forming a first data matrix at a data processing framework from time-series training data, and performing a principal component pursuit on the first data matrix to form an uncorrupted, unscaled matrix and a sparse matrix in the memory, and scaling the uncorrupted, unscaled matrix to form an uncorrupted scaled matrix. The method also includes performing a dynamic principal component analysis (DPCA) on the uncorrupted scaled matrix to form a DPCA model, and determining a squared prediction error from the DPCA model. Based on the squared prediction error, faults are detected in a different data set from operation of the industrial process. At least one of (1) correcting the one or more faults in the different data set or (2) performing a repair operation on a sensor is performed.

Fault detection system utilizing dynamic principal components analysis

A method for monitoring a control of a parameter of one or more devices or systems in an oil or gas production site includes receiving process data, the process data being a result of the control of the parameter of the one or more devices or systems in the production site; smoothing the process data using a polynomial filter while preserving features of the process data to obtain smoothed data; and applying a pattern recognition algorithm to the smoothed data to determine whether there is a malfunction condition in the one or more devices or systems.

System and method for control performance monitoring

A method and an apparatus are provided for applying a self-adaptive filter to a drifting process. The method includes processing a workpiece, measuring an output characteristic of the processed workpiece and modifying a previous estimated process state based at least on the measured output characteristic. The method further includes estimating a next process state based at least on the modified previous estimated process state.

Applying a self-adaptive filter to a drifting process

Methods of optimizing a preheat recipe for rapid thermal processing workpieces are provided. In one aspect, a method of manufacturing is provided that includes preheating a rapid thermal processing chamber according to a preheating recipe and processing a first plurality of workpieces in the rapid thermal processing chamber. Parameter measurements are performed on a first workpiece and a second workpiece of the first plurality of workpieces. The parameter measurements are indicative of processing differences between the first and second workpieces. An output signal is formed corresponding to the parameter measurements and a control signal based on the output signal is used to adjust the preheating recipe for preheating the rapid thermal processing chamber for processing a second plurality of workpieces in the rapid thermal processing chamber to reduce processing differences between first and second workpieces of the second plurality of workpieces.

Lot-to-lot rapid thermal processing (RTP) chamber preheat optimization

A fuzzy control system for industrial process control utilizes an auxiliary process variable to determine which of several regions of different gain a non-linear process is operating. Based upon that determination and fuzzy input signals, the fuzzy controller provides a process control signal which corresponds to that region.

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Los Angeles, CA, United States of America

Si-Zhao J Qin