Artificial Intelligence: Context of application of AI in Chemicals
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AI is driving economic value across the value chain in the resources industry.
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Artificial Intelligence: Context of application of AI in Chemicals
- 1. CONTEXT OF APPLICATION OF AI IN CHEMICALS, OIL & GAS – 26 OCTOBER 2017 ARTIFICIAL INTELLIGENCE
- 2. Sidharth (Sid) Haralalka Digital Transformation, APAC Saurabh Mangal Data Scientist & Artificial Intelligence Practitioner, APAC SPEAKERS
- 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. 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. 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. 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. 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. AI LIVE IN ACTION Copyright © 2017 Accenture All rights reserved. 9
- 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. 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. 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. 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
- 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
- 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. INSERT VIDEO SCREENSHOT HERE Natural Language Processing (NLP) capabilities enabling smarter business decisions SMART DATA EXTRACTOR UNSTRUCTURED TO STRUCTURED DATA
- 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. THANK YOU
- 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. 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
Editor's Notes
- The backdrop to all of this is that we are in the midst of a massive amount of technological change. We are moving from scarcity to abundance. Cost per million transactions was $222 in 1992 to 6 cents in 2012. Cost for sequencing a genome in 2001 was $100 million … in 2015, it was $1,000 (https://www.genome.gov/images/content/costpergenome2015_4.jpg) Cost for hard drive storage in 1980 $437,000 per gig … now it is .019 cents per gig 2016 (http://www.statisticbrain.com/average-cost-of-hard-drive-storage/) An industrial 3D printer in 1987 was $800,000 … in 2012, it was $15,000 … and in 2013 In 2007 a personal 3D printer was $4,000 and in 2013, it was about $1,000 (and dropping) (Accenture 3DP Article)
- Intelligent Automation: Creates growth through a set of features enhancing traditional automation solutions. Ability to automate complex physical world tasks that require adaptability and agility Ability to learn by experience and improve, enabled by repeatability at scale Based on software robotics + machine learning/deep learning to respond autonomously for it’s defined task. Examples: Customer Service (Answering questions, providing recommendations), Insurance Claims (how to process, adapts the activity based on performance, can be proactive to handle to minimize exceptions/mistakes). It’s base on RPA (robotic process automation) capabilities and then layered with machine vision, self-learning, and adaptive capabilities to enhance the process accuracy, flexibility, and autonomy. Predictive algorithms for supply-chain Automate planning and scheduling of raw material purchase Demand forecasting for more effective pricing strategies Labor and Capital Augmentation: Growth will come from enabling resources to be used much more effectively and valuably Enable humans to focus on parts of their role that add the most value Improve capital efficiency—a crucial factor in Industries where it represents a large sunk cost Using Machine Learning capabilities to provide proactive recommendations, advice, and personalization that enhances performance/productivity. This includes inference engines, predictions, or expert systems. Also AI can be used to optimize the use of raw materials, assets, and capital for an organization – either through analysis and actions or to recommend optimal solutions to buyers, procurement, and operations leads. Innovation Diffusion: New products that are based/rely on AI. This includes autonomous cars, Alexa, and Loon (Alphabet’s system to provide global internet). Optimize operational efficiency Intelligent robots – Reduced worker risks, improve productivity and cost-effectiveness Virtual assistance – Navigates database, and processes general inquiries using natural language InNOVATION & DiFFUSION Ability to propel innovations as AI diffuses through the economy. Innovation begets innovation, the potential impact of an AI solution expands to new products/industries Opens new business models and opportunities Enhancing Product innovation and integrity Modeling oil-refining strategies Compliant Product Lifecycle Management
- CHALLENGES Bottleneck on turbine torque is limiting the compressor capacity to produce more Simulations based first principle is tedious and does not holistically generate insights that are visible using big data Reliability concerns/fear of increasing turbine speed Data Science Led Approach Deploy Data Science coupled with process expertise to derive valuable insights from a tight operating window Visualize analytical outputs, delivering insights to operators upfront, ready for consumption Insights on different operating states / trends and key drivers of Ethylene yield Insights on optimization / improved efficiency of the compressor and production management at given energy levels (set points that need to be adjusted towards improved operations)
- CHALLENGES Bottleneck on turbine torque is limiting the compressor capacity to produce more Simulations based first principle is tedious and does not holistically generate insights that are visible using big data Reliability concerns/fear of increasing turbine speed Data Science Led Approach Deploy Data Science coupled with process expertise to derive valuable insights from a tight operating window Visualize analytical outputs, delivering insights to operators upfront, ready for consumption Insights on different operating states / trends and key drivers of Ethylene yield Insights on optimization / improved efficiency of the compressor and production management at given energy levels (set points that need to be adjusted towards improved operations)
- CHALLENGES Bottleneck on turbine torque is limiting the compressor capacity to produce more Simulations based first principle is tedious and does not holistically generate insights that are visible using big data Reliability concerns/fear of increasing turbine speed Data Science Led Approach Deploy Data Science coupled with process expertise to derive valuable insights from a tight operating window Visualize analytical outputs, delivering insights to operators upfront, ready for consumption Insights on different operating states / trends and key drivers of Ethylene yield Insights on optimization / improved efficiency of the compressor and production management at given energy levels (set points that need to be adjusted towards improved operations)
- Intelligent Automation: Based on software robotics + machine learning/deep learning to respond autonomously for it’s defined task. Examples: Customer Service (Answering questions, providing recommendations), Insurance Claims (how to process, adapts the activity based on performance, can be proactive to handle to minimize exceptions/mistakes). It’s base on RPA (robotic process automation) capabilities and then layered with machine vision, self-learning, and adaptive capabilities to enhance the process accuracy, flexibility, and autonomy. Labor and Capital Augmentation: Using Machine Learning capabilities to provide proactive recommendations, advice, and personalization that enhances performance/productivity. This includes inference engines, predictions, or expert systems. Also AI can be used to optimize the use of raw materials, assets, and capital for an organization – either through analysis and actions or to recommend optimal solutions to buyers, procurement, and operations leads. Innovation Diffusion: New products that are based/rely on AI. This includes autonomous cars, Alexa, and Loon (Alphabet’s system to provide global internet).
- Intelligent Automation: Based on software robotics + machine learning/deep learning to respond autonomously for it’s defined task. Examples: Customer Service (Answering questions, providing recommendations), Insurance Claims (how to process, adapts the activity based on performance, can be proactive to handle to minimize exceptions/mistakes). It’s base on RPA (robotic process automation) capabilities and then layered with machine vision, self-learning, and adaptive capabilities to enhance the process accuracy, flexibility, and autonomy. Labor and Capital Augmentation: Using Machine Learning capabilities to provide proactive recommendations, advice, and personalization that enhances performance/productivity. This includes inference engines, predictions, or expert systems. Also AI can be used to optimize the use of raw materials, assets, and capital for an organization – either through analysis and actions or to recommend optimal solutions to buyers, procurement, and operations leads. Innovation Diffusion: New products that are based/rely on AI. This includes autonomous cars, Alexa, and Loon (Alphabet’s system to provide global internet).