Applied Data Science, Big Data and The PI System
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1 Applied Data Science, Big Data and The PI System Teaching the Next Generation of Engineers the Skills of Today Pratt Rogers, PhD University of Utah 10/5/2016
2 Presentation Outline Introduction Digital and data intellectual divide Role of academia in bridging the digital divide
3 Starting thought. The key to good decision making is not knowledge. It is understanding. We are swimming in the former. We are desperately lacking in the latter I have sensed the enormous frustration with the unexpected costs of knowing too much, of being inundated with information. We have come to confuse information with understanding Thinking Fast and Slow Malcom Gladwell
4
5 Atmospheric Sciences Geology & Geophysics Metallurgical Engineering Mining Engineering
6 Celebrating our 125 th Anniversary
7 First job out of school Who, what, when, how? New hire engineer Performance management initiative Glorified data clerk!
8 How to make data more effective Areas of work Data Warehouses Digital Audits Continuous Improvement consulting like any asset, it should create value PI AF modeling and event frames Commodity Coal Metals Industrial (salt, aggregates) Type Surface, Underground Surface, UG, Mill Greenfield Surface, surface, UG, Data Action
9 Why? Sustainable development Macroeconomics: Population growth & Ore Grades Natural Capital: Water & Energy Financial Capital: Operational costs - $per Human Capital: Safety & Health Automation Permitting: Social license
10 Successful academic approach Education Traditional courses Short courses Certificates Not just monetary support - Consulting Industry & Government Research In kind Data Modules Content Implement research Identify needs Prove concepts Drives innovation Step changes Capacity building
11 Common data at mine sites Time series Relational Unstructured Spatial
12 Business intelligence process approach Data Information Knowledge Action Technology systems Business rules Business insight Process change Raw form Common dimensions & location Reporting Continuous improvement Value of data increases substantially
13 Data problem statement Small/medium vs. large ~$370 billion/year Asset Optimization Root Cause Analysis Predictive Maintenance Performance Management... Lost Digital Opportunities
14 Exploring the digital divide Mining Perspective
15 Future? Digital Change Digital Education Approach Why the lost digital opportunity? Digital Innovation Curve Realm of IT, Computer Science, and Information Systems The Great Digital Divide Modeling Surveying/Planning Digital based mining engineering curriculum
16 Implications of digital divide Technology implementation confusion Installation Implementation Site capabilities Lack of strategic plan Corporate or site driven Process changes Collaboration pains Context Business rules & operational standards
17 Technology Implementation Platform Process People Area of emphasis
18 What can be done?
19 Enact change in following areas Education Consulting Research
20 Engineering applications of big data Fall 2016 Objective Infrastructure Data Students Business rules Data characterization Data visualization Excel SQL Server OSI Soft PI Server, AF, Coresight MS Power BI Fleet events and cycles Crush plant Equipment health Mining, chemical, graduate, undergraduate Expand disciplines in future courses
21 Event Year Event Month YYYYMM Rounded Event Timestamp Machine Load Volume Dig Mode Cycle Time (seconds) Spot Duration (seconds) Fill Duration (seconds) Swing Duration (Seconds) Dump Duration (seconds) Swing Angle (Absolute) Return Duration (seconds) TotalCycleTime (Cycle + Dump) / /1/2016 3:00 DL Chop Down / Bench / /15/2016 9:40 DL Chop Down / Bench / /19/2016 3:10 DL Productive Digging / /29/2016 4:20 DL Productive Digging / /25/2016 6:40 DL Productive Digging / /19/2016 5:30 DL Chop Down / Bench / /26/2016 5:30 DL Productive Digging / /29/2016 6:10 DL Productive Digging / /24/ :00 DL Chop Down / Bench / /25/2016 0:40 DL Productive Digging / /11/2016 4:30 DL Dipping Mud and Water / /22/ :50 DL Dipping Mud and Water / /1/ :10 DL Productive Digging / /2/ :40 DL Rehandle / /26/2016 4:00 DL Productive Digging / /1/ :20 DL Dipping Mud and Water / /27/2016 4:40 DL Productive Digging / /12/2016 4:00 DL Productive Digging / /19/ :30 DL Productive Digging / /10/ :50 DL Chop Down / Bench / /12/2016 3:40 DL Productive Digging / /12/2016 9:00 DL Rehandle / /16/ :10 DL Chop Down / Bench / /16/ :50 DL Chop Down / Bench / /23/ :40 DL Chop Down / Bench / /11/2016 5:50 DL Dipping Mud and Water / /14/ :00 DL Productive Digging / /16/ :00 DL Chop Down / Bench / /23/ :00 DL Chop Down / Bench / /23/ :20 DL Chop Down / Bench / /26/ :50 DL Productive Digging / /9/ :30 DL Chop Down / Bench / /28/2016 8:00 DL Productive Digging / /2/ :20 DL Productive Digging / /2/ :50 DL Productive Digging / /23/2016 3:30 DL Chop Down / Bench / /23/ :20 DL Dipping Mud and Water / /28/ :40 DL Productive Digging / /9/ :10 DL Chop Down / Bench / /10/ :10 DL Chop Down / Bench / /19/ :00 DL Productive Digging / /19/ :10 DL Productive Digging / /24/2016 8:50 DL Chop Down / Bench / /24/2016 9:20 DL Chop Down / Bench / /2/ :00 DL Dipping Mud and Water / /8/ :50 DL Dipping Mud and Water / /15/2016 3:20 DL Productive Digging / /22/ :20 DL Dipping Mud and Water / /28/ :20 DL Productive Digging / /1/2016 9:10 DL Chop Down / Bench / /2/2016 5:20 DL Productive Digging / /5/ :20 DL Chop Down / Bench / /8/ :00 DL Dipping Mud and Water / /11/ :30 DL Productive Digging / /9/ :30 DL Chop Down / Bench / /11/2016 4:30 DL Dipping Mud and Water / /23/2016 3:30 DL Chop Down / Bench / /23/ :10 DL Dipping Mud and Water / /10/2016 4:50 DL Productive Digging / /25/2016 2:00 DL Productive Digging / /1/ :20 DL Productive Digging / /2/ :50 DL Productive Digging / /8/2016 9:10 DL Productive Digging / /3/2016 8:40 DL Productive Digging / /9/2016 9:00 DL Chop Down / Bench / /18/2016 3:50 DL Productive Digging / /25/ :20 DL Productive Digging / /28/ :50 DL Rehandle / /1/ :30 DL Dipping Mud and Water Connecting standards to business rules Excel Tables & Pivot Business Rules to Engineering Standards Time Usage Model SQL Tables & Views Business Rules to Engineering Standards Material Hierarchy PI AF / Event Frames Business Rules to Engineering & Operational Standards Time Usage Model / Operational Cycles
22 Class Example: Data Visualization Pivot Charts PI Coresight Power BI
23 OSIsoft support
24 Initial lessons learned Introduce concepts gradually I do, we do, you do approach Infrastructure always a challenge Business rules first Visualizations important business rules foundational Students are enjoying it!
25 Class Progression Looking for partners Class 1: Business Rules Foundational piece Data characterization Eng./Oper. Standards Class 2: Advanced Analytics Process improvement Big data analysis Root cause analysis Short Courses Partner with companies Come to site Focus on all data types
26 Utah s Digital Minescape Mining IS/OT Research Lab
27 Workings of lab Platforms OSI Soft PI Server, AF Caterpillar MineStar VIMS Health Joy Global PreVail Power BI.. Control room Mobile computing Dashboards Visualization Business Rules Standards Analytics Business Intelligence Continuous Improvement Operational Excellence Big Data Analysis Predictive Maintenance Machine Learning
28 Research areas Natural Capital: Water & Energy Financial Capital: Operational costs - $per Human Capital: Safety & Health Automation Energy optimization: M2M Mine leadership in digital age Data and Automation
29 Consulting work Implementation Strategy development Execution Business Rules AF Models Event Frames Data characterization Data reporting Balanced Cross platform
30 Conclusions Large opportunity to expand mining curriculum - People Short courses, certificates, etc. Technology collaboration is key - Process Break silos data, process, roles, etc. Looking for support: data, content, modules, etc. Start small and expand Utah uniquely suited geographically, intellectually, to build lab
31 Thank You 31
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