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

A method for operating a smelter to produce a pure base metal, said method comprising producing said pure base metal in pots with the aid of a digital computer and a central database for storing data derived from a metal smelter process to produce a pure base metal, said data comprising process variables comprising power and resistance, noise control, feed control and chemicals received from said pots for said smelter process, comprising:

• means for mining hidden correlations among heterogeneous data within said central database;
• means decomposing instance feature data ({tilde over (X)}ij} into three non-negative matrices;
• means decomposing instance feature data (Yi) into three non-negative matrices;
• means regularizing task relatedness, view (feature type) consistence and label correlations;
• means for determining the objective function iteratively;
• wherein for said task relatedness, the jth view/feature type, said decomposition of {tilde over (X)}ij in different tasks shares the same feature encoding matrix Cj;
• wherein, for said ith task, said decomposition said decomposition of {tilde over (X)}ij in different tasks shares the same feature encoding matrix Ri;
• wherein for said label correlations, said labels share the same label encoding matrix CY across different tasks;
• and instance clusters and label clusters,
• wherein Mij models the correlation between instance clusters and feature clusters, and
• wherein MiY, models the correlation between instance clusters and label clusters;
• and,
• wherein said additive is aluminum fluoride (AlF3) and ranges of data measurements generated in said pots and received in said central database comprise various process control mechanisms comprising power and resistance control, noise control, alumina feed control and chemical combination data, all of which 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 aluminum fluoride (AlF3), the optimal bath temperature and AlF3 concentration ranges for maximum efficiency being located in Box 2.2 of a 3×3, 9-box matrix plotting ideal bath temperature comprising the vertical ordinate axis as a function of AlF3 concentration ranges comprising the horizontal abscissa axis thereof;
• 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.

A non-transitory computer readable storage medium having a computer readable program code stored therein, said computer readable code embodied therein storing a program of instructions adapted to be executed to implement 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 additive concentration obtained from each pot to an advanced process controller and the central database;
• measuring an actual number value of the temperature and additive concentration in at least one of said pots;
• sending said actual number value of the said temperature and 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 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;
• wherein said additive is aluminum fluoride (AlF3) and ranges of data measurements generated in said pots and received in said central database comprise various process control mechanisms comprising power and resistance control, noise control, alumina feed control and chemical combination data, all of which 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 aluminum fluoride (AlF3), the optimal bath temperature and AlF3 concentration ranges for maximum efficiency being located in Box 2.2 of a 3×3, 9-box matrix plotting ideal bath temperature comprising the vertical ordinate axis as a function of AlF3 concentration ranges comprising the horizontal abscissa axis thereof;
• 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.