Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Artificial Intelligence: Context of application of AI in Chemicals

1,028 views

Published on

AI is driving economic value across the value chain in the resources industry.

Published in: Technology

Comments are closed

  • Be the first to comment

Artificial Intelligence: Context of application of AI in Chemicals

  1. 1. CONTEXT OF APPLICATION OF AI IN CHEMICALS, OIL & GAS – 26 OCTOBER 2017 ARTIFICIAL INTELLIGENCE
  2. 2. Sidharth (Sid) Haralalka Digital Transformation, APAC Saurabh Mangal Data Scientist & Artificial Intelligence Practitioner, APAC SPEAKERS
  3. 3. DEMYSTIFYING AI
  4. 4. WE ARE IN AN UNPRECEDENTED PERIOD OF TECHNOLOGY INNOVATION 1 MAINFRAME 2 CLIENT-SERVER AND PCS 3 WEB 1.0 ECOMMERCE 4 WEB 2.0, CLOUD, MOBILE BIG DATA, ANALYTICS, VISUALIZATION5 IOT AND SMART MACHINE6 ARTIFICIAL INTELLIGENCE7 QUANTUM COMPUTING8 TODAY 1950 1960 1980 1990 20201970 2000 2010 2030 1950 Turing Test 2005: Web 2.0 Quantum 1964: System/360 Server/Host 1969: ARPANET 1990: System/390 1991: Public Internet 1994: Amazon 1977: PC 1999: Salesforce.com 2006: AWS 2008: iPhone 1997: Big Data Public Cloud Mainstream 2010: Sales of PC Peak 2010: Self-driving Car AI 2014, IDC: 4.4 Zettabytes of Data 1972: SAP 1999: IoT, M2M
  5. 5. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? It is the single biggest technology revolution the world has ever seen. SENSOR PROCESSING DEEP LEARNING ROBOTIC PROCESS AUTOMATION EXPERT SYSTEMS INFERENCE ENGINES MACHINE LEARNING COMPUTER VISION KNOWLEDGE REPRESENTATION SENSE COMPREHEND ACT LEARN
  6. 6. AI IS REDEFINING THE WAY THE ENTERPRISE WORKS Copyright © 2017 Accenture All rights reserved. 6 Apply a combination of sense, comprehend, & act AI technologies to drive automation for complex processes Automate core processes where typically human judgment is key, but provide low levels of competitive differentiation Apply automation to routine processes that require no human judgement SAMPLE PROCESS DISTRIBUTION AUTOMATE ROBOTIC PROCESS AUTOMATION (RPA) COGNITIVE AUTOMATION MACHINE- BASED PROCESS EXECUTION AUGMENT ROBOTIC PROCESS AUTOMATION (RPA) COGNITIVE AUTOMATION MACHINE-BASED PROCESS EXECUTION By applying a balance of automation and augmentation.
  7. 7. AI powered service that answers customers’ common lubricant questions in seconds AI robots for ocean exploration to improve natural seep detection capabilities AI to accurately and speedily predict quality of gas products By 2020, 85% of customer Interactions will be managed without a human
  8. 8. AI IS DRIVING ECONOMIC VALUE ACROSS THE VALUE CHAIN Through three channels of AI-led growth that drive increased productivity, satisfaction, and value… R&D / LABORATORY PLANTS, ASSETS, MANUFACTURING & OPERATIONS SUPPLY CHAIN & INVENTORY COMMERCIAL & SALES ENTERPRISE FUNCTIONS INTELLIGENT AUTOMATION Creates growth through a set of features enhancing traditional automation solutions. LABOR & CAPITAL AUGMENTATION Growth will come from enabling resources to be used much more effectively and valuably INNOVATION & DIFFUSION Ability to propel innovations as AI diffuses through the economy.
  9. 9. AI LIVE IN ACTION Copyright © 2017 Accenture All rights reserved. 9
  10. 10. COGNITIVE PLANT OPERATIONS DIGITAL ASSISTANT (CHATBOT) AI FOR DARK DATA
  11. 11. COGNITIVE PLANT OPERATIONS DIGITAL ASSISTANT (CHATBOT) AI FOR DARK DATA
  12. 12. APPLICATIONS • Automobile parts • LCD displays • Extrusion sheets, optical lenses • Tableware • Swimming pools and water feature projects COGNITIVE PLANT OPERATIONS APPLYING AI TO IMPROVE QUALITY… ...by consistently reducing defects through optimal plant conditions ...from data capture to actions taken by operations, maintenance & reliability team Automotive Parts Construction: Stadium Roof PRODUCT
  13. 13. CHALLENGES TODAY DRYER Spray Water Overflow Water CONVEYOR WATER Lower Feed Roll EXTRUDER PELLETIZER MMA Powder Additives 1 FEED 2 EXTRUDING 3 STRAND COOLING 4 PELLETIZING 5 PELLET COOLING 6 DRYING Hopper Thermocouple NozzleBarrelScrew Heaters Cutting Rotor Cutting Blade Upper Feed Roll CONVEYOR Operators follow a trial and error approach towards reducing defects Cutting rotor changed without clear reason or justification Golden recipes looking at 70 parameters out of which operators are currently adjusting 7 parameters
  14. 14. APPROACH Engineers / Supervisors NLP, Intelligent Search In History Data Science To Model The Plant Form-less Interaction With Virtual Assistant 2016 2015 2014 2013 2012 2011 0 20 40 0 2 4 6 0 2 4 6 0 2 4 6 0 2 4 6 0 2 4 6 0 2 4 6 week wday crack_category Average Bad Good Calendar Plot for MH 100 Crack Categorically Bad > 15 Defects Categorically Good < 10 Defects Categorically Average 10 – 15 Defects TI2 403.P V_m ed 75 80 85 90 SC2401.PV_med 138 139 140 141 142 143 fittedvalue 8 10 12 14 16 18 Good Bad No. of defects Good quality operating zone
  15. 15. HELPING OPERATORS LEVERAGE DATA SCIENCE & AI TO FIND BEST RECIPE SMART Predict current cutter condition & degradation levels Predict optimal setting for adjustable parameters in near real time 12 variables that impact quality and defects Automatically identify past load changes, quality measurements Apply best in class settings achieved in the past to current situationsAFTER Sub-optimal change of cutter Currently adjusting only 7 variables Trial and Error Approach taking days Memory / experience driven actions BEFORE Hand written operator logs WISE
  16. 16. COGNITIVE PLANT OPERATIONS DIGITAL ASSISTANT (CHATBOT) AI FOR DARK DATA
  17. 17. OBJECTIVE: Use Artificial Intelligence to optimize the operating conditions of a cracked gas compressor for maximizing production benefits. APPROACH CHALLENGES FEEDSTOCK STORAGE PROCESS OPTIMIZATION 1 SCOPE DEFINITION 2 DATA GATHERING & ANALYSIS 3 MODEL DEVELOPMENT & EVALUATION 4 DESIGN THINKING & CHATBOT CREATION 5 VALUE EXPANSION FURNANCE SET UP QUENCHING 5 STAGE COMPRESSOR DISTILLATION INPUT SCOPE INPUT
  18. 18. COGNITIVE PLANT OPERATIONS DIGITAL ASSISTANT (CHATBOT) AI FOR DARK DATA
  19. 19. DARK DATADARK DATA: LOOKING AT THINGS DIFFERENTLY TECHNOLOGY CAN HELP HUMANS SEE MORE IN WHAT THEY HAVE. USING TECHNOLOGY TO BYPASS HUMAN PROCESSING CONSTRAINTS, E.G. NLP, COMPUTER VISION, VIDEO ANALYTICS, DEEP LEARNING… INSIGHTS FROM UNTAPPED DATA Email Correspondences Notes/ Presentations Survey Data Document Versions REPOSITORIES Video Surveillance Access Logs Audit Files Internet Logs MONITORING Employee Data Purchasing History Usage LogsCustomer/Account Information Financial Statements OPERATIONS/ BI STRUCTURED + UNSTRUCTURED LARGE VOLUMES REAL-TIME + HISTORICAL Maintenance Records
  20. 20. INSERT VIDEO SCREENSHOT HERE Natural Language Processing (NLP) capabilities enabling smarter business decisions SMART DATA EXTRACTOR UNSTRUCTURED TO STRUCTURED DATA
  21. 21. IN SUMMARY… AI IS ALREADY QUITE PERVASIVE - THE TIME TO ACT IS NOW! AI DOESN’T REPLACE BUT RATHER AUGMENTS THE WORKFORCE HARNESSING AI WILL ONLY HELP BOOST YOUR BUSINESS CURRENT TRENDS ARE MAKING AI ACCESSIBLE AND EASY TO IMPLEMENT TODAY
  22. 22. THANK YOU
  23. 23. WHAT VALUE DOES AI POTENTIALLY DRIVE? In 2035, AI will ….. DOUBLE ECONOMIC GROWTH 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 US Finland UK Sweden Netherlands Austria Germany France Japan Belgium Spain Italy Baseline AI Steady State Real GVA* Growth in 2035
  24. 24. WHAT VALUE DOES AI POTENTIALLY DRIVE? In 2035, AI will ….. Source: Accenture and Frontier Economics 29% 30% 34% 35% 36% 37% 11% 12% 17% 20% 25% 27% LABOUR PRODUCTIVITY BY 40% Help People be More Productive and Increase Sweden Finland United States Japan Austria Germany Netherlands United Kingdom France Belgium Italy ‘Spain

×