System and predictive modeling method for smelting process control based on multi-source information with heterogeneous relatedness

Patent metadata

Field	Value
Publication number	US-2018081339-A1
Application number	US-201615271367-A
Country	US
Kind code	A1
Filing date	Sep 21, 2016
Priority date	Sep 21, 2016
Publication date	Mar 22, 2018
Grant date	—

Abstract

The present invention is a system and predictive modeling method specially designed to improve process control and energy efficiency for a smelting process used to produce pure metal from an ore containing said metal. Data is collected from various sensor sources in the smeltering process to predict whether an increase or decrease is needed in controlling two variables comprising temperature and an additive that allows the reaction in the electrolytic process to proceed at a lower bath temperature. The invention provides a generalized framework to learn the complex heterogeneity embedded in the collected data, and employs a regularized non-negative matrix factorization problem, which simultaneously decomposes the instance-feature and instance-label matrices, while enforcing task relatedness, feature type consistency and label correlations on the collected data. The predictive modeling method disclosed herein effectively mines the hidden correlation among the heterogeneous data and improves the prediction accuracy.

First claim

What we claim and desire to protect by Letters Patent is:

1 . A method for predicting temperature and additive concentration variables in an electrolytic smelting bath process, said variables being regulated together to provide in combination, optimal improvement in the physico-chemical properties of said bath and to lower the temperature of said bath, said optimal combination resulting in a lowering of the melting temperature of said bath at any given time, comprising:
measuring process variables of each pot used in an electrolytic process of forming a pure metal; sending said process variables to a central database; receiving said process variables in said central database that was generated in each of said pots and receiving therein historical measurements; representing respective pots as quality predictive modeling tasks based on said historical measurements; grouping predictive modeling tasks for said temperature and said additive in said pots according to heterogeneous relatedness of the production equipment, said grouping step being included, for each pot in which a metal ore is simultaneously being processed, grouping said predictive modeling tasks for all of the respective pots, each said pot of the multiple pots being treated as a group of one or more predictive modeling tasks; partitioning said process variables into two sets comprising generating a prediction model that accommodates said grouping of said pots predictive modeling tasks from said grouping step and said partitioned process variables from said partitioning step; predicting a temperature and or a concentration of said additive in individual pots based on said prediction model generated in the generating step; sending predicted values of temperature and/or additive concentration obtained from each pot to an advanced process controller and the central database; measuring an actual number value of the temperature and/or additive concentration in at least one of said pots; sending said actual number value of the said temperature and/or said additive concentration in at least one of said pots to an advanced process controller and a virtual machine; updating said virtual machine with said actual number value of the temperature and/or additive concentration in at least one of said pots; determining a feedback control by said advanced process controller: and processing, by increasing or decreasing said temperature and/or said additive concentration by the production equipment in accordance with the feedback control, wherein the receiving, representing, grouping, partitioning, generating, and predicting steps are executed by a virtual machine implemented on a computer.

2 . The method defined in claim 1 wherein said additive is AlF 3 .

3 . The method defined in claim 2 wherein ranges of data measurements generated in said pots comprising various control mechanisms comprising power and resistance control, noise control, alumina feed control and chemical combination data, are classified in a functional 3×3, 9-box matrix control block that issues a series of control signals based upon a low (L), medium (M) and high (H) range of the functional relationship between bath temperature and AlF 3 , the optimal bath temperature and AlF 3 concentration ranges for maximum efficiency being located in Box 22 of said 3×3, 9-box matrix having the depicted configuration plotting ideal bath temperature as the ordinate as a function of AlF 3 concentration ranges as the abscissa;
said functional 3×3, 9-box matrix being that depicted in FIG. 2 hereof, which is hereby incorporated herein by reference as if set forth identically herein.

4 . The method defined in claim 3 wherein said various process control mechanisms are connected in series to smelter groups 1 through 4 through n, which host the smelting process, and which comprise machine heterogeneity, said smelter groups being connected in series respectively to a sensor type functional block comprising a sensor type 1, a sensor type 2, a sensor type j, a sensor type m, through to sensor n, each said sensor collecting one of the feature type data source measurements selected from the power and resistance variables, noise related process variables, various feeding parameters and various chemical contents; and
the plurality of process variables resulting from the sensor measurement in a feature heterogeneity stage is divided into different types of said variables as taken from different sources;
the data from said feature heterogeneity stage is transmitted to a data warehouse which is a functional block that stores said variable process data, measures said 3×3, 9-box control variables and predicts the 9-box control variables;
the output of the data warehouse is transmitted to a predictive modeler that is a functional block that uses a trained model that processes a plurality of feature types and data sources and, based upon historical process data and measurements of temperature and AlF 3 , predicts control measurements of bath temperature and concentration of AlF 3 ;
the data obtained from said trained model is transmitted to said 3×3, 9-box matrix control that issues a series of control signals based upon the range of temperature and AlF 3 concentration.

5 . The method defined in claim 4 wherein said modeling approach comprises the following steps:
train a random forest three class Model 1 using all training test data developed and stored in said data warehouse;
train a binary classification Model 2 using training data belonging to a class high (H) and medium (M);
apply said 3-class Model 1 to said test data;
if predicted low Box 22 , apply Model 2, output is low or medium;
if predicted medium, output is medium,
if predicted high above Box 22 apply Model 3, output is high or medium.

6 . The method defined in claim 5 wherein triple types of complex heterogeneity embedded in the data using said predictive modeling approach with respect to Model 2 and Model 3 is determined using a regularized non-negative matrix triple factorization operation based upon the depicted matrix in FIG. 6 hereof, which is hereby incorporated herein by reference as if set forth identically herein
wherein instance feature data ({tilde over (X)} ij ) is decomposed into 3 non-negative matrices; and
wherein instance label data (Y i ) is decomposed into 3 non-negative matrices;
the task relatedness, view/feature type, consistence and label correlations are regularized, subject to the limitations wherein:
with respect to task relatedness, the jth view feature type, the decomposition of ({tilde over (X)} ij ) in different tasks shares the same feature encoding matrix C j ;
with respect to view/feature consistence, for the ith task, the decomposition of ({tilde over (X)} ij ) in different views shares the same feature encoding matrix R i ;
with respect to the label correlations, the labels share the same label encoding matrix C Y across different tasks;
M ij models the correlations between instance clusters and feature clusters;
M iY models the correlations between instance clusters and label clusters;
said method simultaneously decomposes the instance-feature and instance label matrices, while concurrently enforcing task relatedness, feature type consistency and label correlations on said data.

7 . The method defined in claim 6 wherein said hidden correlations among said heterogeneous data is governed according to:

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Assignees

• Zhu Yada
• Kalagnanam Jayant R
• IBM

Inventors

• Zhu Yada
• Kalagnanam Jayant R

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G05B2219/41354

  Magnetic, thermal, bimetal peltier effect displacement, positioning · CPC title

• G05B19/041

  Function-oriented details · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title

• G05B19/406 Primary

  characterised by monitoring or safety (G05B19/19 takes precedence) · CPC title