Manufacturing process control with deep learning-based predictive model for hot metal temperature of blast furnace

Patent metadata

Field	Value
Publication number	US-10633716-B2
Application number	US-201715813653-A
Country	US
Kind code	B2
Filing date	Nov 15, 2017
Priority date	Sep 27, 2017
Publication date	Apr 28, 2020
Grant date	Apr 28, 2020

Abstract

A blast furnace control system may include a hardware processor that generates a deep learning based predictive model for forecasting hot metal temperature, where the actual measured HMT data is only available sparsely, and for example, measured at irregular interval of time. HMT data points may be imputed by interpolating the HMT measurement data. HMT gradients are computed and a model is generated to learn a relationship between state variables and the HTM gradients. HMT may be forecasted for a time point, in which no measured HMT data is available. The forecasted HMT may be transmitted to a controller coupled to a blast furnace, to trigger a control action to control a manufacturing process occurring in the blast furnace.

First claim

We claim:

1. A method of controlling a manufacturing process in a blast furnace, the method executed by at least one hardware processor, the method comprising:
receiving manufacturing process data associated with a blast furnace, the manufacturing process data comprising state variables and control variables used in operating the blast furnace, the state variables comprising at least a hot metal temperature (HMT) and other state variables, wherein the manufacturing process data comprises a plurality of measured HMT at different time points, of a product continuously produced in the blast furnace;
generating imputed HMT by interpolating the measured HMT;
generating HMT gradients based on at least the imputed HMT;
defining a causal relationship between the other state variables and the HMT gradients, the relationship generated as a neural network model;
training the neural network model using as training data, a weighted combination of the imputed HMT up to a last known measured HMT and predicted HMT up to the last known measured HMT;
running the trained neural network model to predict a current point in time value for the HMT, in which no measured HMT for the current point in time is available, wherein the trained neural network model predicts the HMT corresponding to a time period starting from the time of the last measure HMT data point for a number of time periods until the number of time periods advances to the current point in time and uses the predicted HMT corresponding to each of the number of time periods to predict the current point in time value for the HMT; and
transmitting the current point in time value for the HMT to a controller to trigger a control action to control a manufacturing process occurring in the blast furnace, the control action including at least selectively opening a conduit to the blast furnace to control content amount of input to the blast furnace.

2. The method of claim 1 , wherein the product comprises pig iron.

3. The method of claim 1 , wherein the neural network model comprises a long short-term memory network.

4. The method of claim 1 , wherein the manufacturing process data is stored as a time series data.

5. The method of claim 1 , further comprising autonomously retraining the neural network model responsive to receiving a new measured HMT, using the new measured HMT as the last known measured HMT.

6. The method of claim 1 , wherein the manufacturing process includes a continuous blast furnace operation.

7. The method of claim 1 , wherein the plurality of measured HMT at different time points comprises a plurality of measured HMT measured at irregular time intervals.

Assignees

• IBM

Inventors

• Lee Young Min
• Yeo Kyong Min

Classifications

• G01K7/04

  the object to be measured not forming one of the thermoelectric materials · CPC title

• G01N25/18

  by investigating thermal conductivity (by calorimetry G01N25/20; by measuring change of resistance of an electrically-heated body G01N27/18) · CPC title

• C21B7/06 Primary

  Linings for furnaces · CPC title

• C21B2300/04

  Modeling of the process, e.g. for control purposes; CII · CPC title

• G06N20/00

  Machine learning · CPC title