machine learning applications for process control and

Machine Learning Applications for Process Control and Optimizatioin in SteelmakingESTEP AI & ML Online Workshop

October 15th, 2020

Giovanni Bavestrelli

2019

Machine Learning Simplified

LogicMachine LearningData

Results

Traditional SoftwareData

ResultsLogic

New dataTraining data

13 October 2020Tenova S.p.A. | Confidential & Proprietary 3

Our Connected Products in the FieldPILOT 1: PREDICTIVE MAINTENANCE

Submerged Arc Furnace (SAF)

50Electric Arc

Furnace (EAF)

400Heat treatment

Furnaces

7.000Direct Reduction

Plants

35Burners on Reheating Furnaces

20.000

300 Strip Processing &

Cold rolling mills900 Roll

Grinders

Tenova IIoT Platform

13 ottobre 20204


Tenova Data Science InfrastructurePILOT 2: PLANT OPTIMIZATION

Data Acquisition

Data Visualization

Data Modeling

Data Processing

Data Storage

Tenova WIDE(Tenova Development)

Tenova EDGE(Tenova Development)

U-SQL


If data is the new oil…

…information is the new energy

…and decisions are the new end goal

Three ingredients of successful AI projects



Predicting NOx Emissions with Neural NetworksWalking Beam Furnace, 2008

Walking Beam FurnaceTKS Duisburg


Predicting NOx Emissions with Neural Networks

RMSE Train Set 22.37RMSE Test Set 23.10RMSE Mean 31.48Better than mean % 26.61---------------MAE Train Set 17.09MAE Test Set 17.32MAE Test Mean 25.61Better than mean % 32.34


Predicting Steel Temperature in BOF FurnacePILOT 2: PLANT OPTIMIZATION

Error on the test set:RMSE of new predictive model: 30,6 degFRMSE of deterministic model: 45.5 degFRMSE Average Improvement: 30%

Predictions with error >50 degF reduced by 50% to 10% Benefits:Reduced number of re-blows (improved process yiels)Reduced energy consumption at downstream stations (higher process efficiency)

Static Charge Model for BOF developed by Tenova Canada: Deterministic Mass/Energy Balance model to calculate input materials (HM,SC,..) and blowing Oxygen to reach steel end point Goal to improve final temperature prediction accuracy for better use of fuel/coolant additions

Data for approximately 4000 heats:Prediction using Support Vector Regression and Radial Basis Function Kernel:

OUTPUT


Predicting Metallization and Carbon Content in DRIPILOT 2: PLANT OPTIMIZATION

Metallization (%)

Carbon Content (%)Prediction

Gas flows (m3/h )Gas temperatures (C°)Iron content in pellets (%)Iron ore inlet (Tons/h)Production rate (Tons/h)

Features

Benefits:

• Improve the quality of the steel products made in the EAF• Better use of resources (i.e. spent process gas and oxygen injection)• Save natural gas in the burners and in the cooling section of the reactor

Machine Learning Model

Data:• 1 year• 200 Gb• Lab samples every 2 hours


Predicting Metallization and Carbon Content in DRI

Static charge modelPlant data

Measured Features Domain Features

Feature Engineering and Feature Selection

Model Building

Model EvaluationModel Retraining Model Deployment


Predicting Penetration in TensionlevelerPILOT 2: PLANT OPTIMIZATION

Dataset from customer plant, 2016 (one week of data)

Used Random Forest Regression to predict roller penetration based on:• Coil Thickness• Coil Width• Strip Elongation• Strip Tension

Extremely good results, but based on limited dataset

• Plan to gather more significant data during next commissioning on site

• Approach considered positive, will probably deliver good results

• Considering new applications for machine learning in Tenova

Italimpianti


ORI Martin Lighthouse ProjectORI Martin – Lighthouse

Automatic Classification of ScrapCategory from Images

Using Convolutional Neural NetworksTraining on 33000 images, 7 categories


Scrap Classification SolutionCONVOLUTIONAL NEURAL NETWORK

Image from Sumit SahaTowards Data Science

Convolutional Neural Network12 Layers5.5 Million parametersTraining on an NVIDIA GeForce 1080Ti

Data for Training33.000 classified images7 different categoriesResolution 640 x 360 pixel

ORI MARTIN – LIGHTHOUSE

METAL SCRAP DELIVERY - Automatic Identification and ClassificationUNLOADING IN SCRAP YARDConfirm AutomaticClassification or Reclassify Scrap

ENTRY GATE CAMERA

TRUCK ENTRY Identification of delivered metal scrap, 100 trucks per day

Scrap Classification & Tracking

Sheet Shovelable

STORAGE AND COLLECTION – Material identified and tracked

Tower Cranes with Innovative Weighting System

3D Laser profiling of volumes in storage area to drive loading and unloading operations

Identification of delivered metal scrap, 100 trucks per day

Identification and tracking of scrap through the Consteel®

towards the furnace

17

Scrap Classification ProblemCONVOLUTIONAL NEURAL NETWORK

…and what about:

SHEET 09 SHEET 50 TURNING CAST IRON INGOTS PREREDUCED SHEARED SCRAP PANTOGRAPH

SHEET 50 SHEARED SCRAP PREREDUCED PREREDUCED CAST IRON INGOTS PANTOGRAPH SHEET 09

ORI MARTIN – LIGHTHOUSE

Scrap Classification Results

Convolutional neural network used to find truck load area and classify scrap material

Algorithm learned to classify images by being fed 3000+ images, each with a given category

Algorithm learned to locate truck load rectangle by being fed approx. 300 pictures, each with rectangle drawn by hand


Scrap Classification ResultsCONVOLUTIONAL NEURAL NETWORK

Training on 33.000 images in 7 categories

Naïve Classification Accuracy • Approx. 62%• Top 2 Accuracy 82%

Current Classification Accuracy • Approx. 90% • Top 2 Accuracy 99%

Most Common Error:• Misclassification between subcategories of Sheet

Most Common Challenges:• Lack of lighting• Truck positioning• Trucks covered• Few images for some categories


Lessons Learned

• Good data is better than big data, the quality of data is essential• Don’t stop with data readily available, look for data that is significant• Add features from deterministic models and from domain knowledge• The selection of features is more important than the selection of model• Don’t use all features, select those that improve the model and drop the rest• Get domain experts on board, make sure they have time to contribute• Make sure data scientist and domain experts work well together• Try to reduce noise/errors in the data, there is no way to model noise• Try different models, experimentation is key, there is no substitute yet • Keep it simple, prefer an easier model when possible• Don’t stop once you find a good model, maintain it, keep testing it, try new ones• Look for value, easier to identify than ROI• Share the data• Trust the cloud


Smart MachineVISION FOR THE FUTURE

Live data (sensors)

Historical Data (Analytics)

PredictiveMaintenance

Self-Optimizing Machines

The Future is Digital


LOOKING OUTSIDE

«Every Business is a Digital Business»Satya Nadella, CEO Microsoft

«Go Digital or go Home»Dieter Zetsche, ex CEO Daimler AG

Artificial intelligence is a new form of talent, companies won’t be able to compete without it


Lifelong learning is here to stayLOOKING OUTSIDE

www.tenova.com

Thank You!

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