Trends that are shaping the future of process automation Ian Craig Department of Electrical, Electronic and Computer Engineering University of Pretoria South Africa 1
Contents Trends Impact of the second machine age Digitization of the process industry Industries becoming democratized Impact of the second machine age Commoditization of (advanced) process control Lights-out process control Digitization of the process industry Digital twins of real world processes Industries becoming democratized The rise of freelancer Concluding thoughts 2
First machine age Human muscle power replaced by machine power... Animal muscle power replaced by machine power... 3
Second machine age Human brain power being replaced by digital tecnologies... 4
Second machine age: exponential growth in computing Moore s law is only one example 5
Second machine age: networking computing devices Number of devices 20B> 10B 200M Fixed computing (You go to device) Doubled every 13 years Mobility (Device goes with you) Doubled every 1.4 years Internet of things (Age of devices) Doubles every? years 1995 2000 2011 2020 Time Source: http://www.ciscopress.com/articles/article.asp?p=2158215&seqnum=4 6
The commoditization of advanced process control (APC) Use Model Predictive Control (MPC) as an example most successful advanced process control (APC) technology MPC technology is today routinely implemented on thousands of processing plants MPC is one of the technologies most often used for improving economic performance Consider the steps taken to establish and maintain a successful MPC controller for an industrial process 7
Steps to establish and maintain a successful MPC controller* 1. Economic motivation of MPC motivate the funding required to initiate an MPC project 2. Pretest and preliminary MPC configuration ensure that the base layer is functional - could include the re-identifying and retuning of PID loops and the maintenance and repair of actuators and sensors 3. Plant testing or plant model identification automated and remote testing now possible 4. Model and controller development 5. Controller commissioning and training Vendors tools merge steps 3, 4 and parts of step 5, into one, resulting in very little disruption to production 6. Controller monitoring and maintenance Automated controller maintenance tools essential for autonomous MPC more research required * Source: adapted from Darby & Nikolaou, MPC: Current practice and challenges, Control Engineering Practice, 2012, pp. 328-342. 8
Lights-out process control Definition: Fully automated process plant in which no human intervention or supervision is needed Operation without a human operator Not a new concept in control Feedback control for engineered systems is essentially about taking the person out of the loop and replacing her or him with an algorithm People are however still involved in designing, implementing, commissioning, maintaining, and updating the controllers 9
Rank Occupation Probability of Computerisation 702. Telemarketers 0.99 614. Nuclear Power Reactor Operators 0.95 491. Plant and System Operators, all other 0.86 487. Chemical Plant and Systems Operators 0.85 486. Power Plant Operators 0.85 429. Gas Plant Operators 0.78 389. Petroleum Pump System Operators, Refinery Operators 0.71 339. Wastewater Treatment Plant and System Operators 0.61 314. Commercial Pilots 0.55 172. Electrical Engineers 0.1 98. Electronic Engineers 0.025 77. Chemical Engineers 0.017 1. Recreational Therapists 0.0028 "47 % of total US employment is in the high risk category, meaning that associated occupations are potentially automatable over... perhaps a decade or two" (from 2013) *Source: http://www.futuretech.ox.ac.uk/sites/futuretech.ox.ac.uk/files/the_future_of_empl oyment_oms_working_paper_0.pdf 10
Jobs Involving Routine Tasks Source: https://www.stlouisfed.org/on-the-economy/2016/january/jobsinvolving-routine-tasks-arent-growing 11
Production per operator Emphasis on reducing operators in processing plants A telling metric: production per operator United States refining industry data 1980:93,000 operators, 5.3 bbl prod. 1998:60,000 operators, 6.2 bbl prod. 2015:<20,000 operators, 6.6 bbl prod. Adapted from From Samad, T., Honeywell, 2001 American Control Conference (ACC'01). 1980 and 1998 data from U.S. Bureau of Census, 1999 2015 number of operator data adapted from U.S. Bureau of Labor Statistics (employment category 51-8000) 2015 production data adapted from U.S. Energy Administration figures on U.S. Operating Crude Oil Distillation Capacity 12
Lights-out process control Job Description The Remote Operator is responsible for working in a highly functioning team environment, operating up to 6 Air Separation Units (ASU) or CO 2 locations simultaneously. Responsibilities include production changes, efficiency monitoring and response, power management, process disturbance and alarm management, plant shut downs and restarts. Basic Qualifications High School with a minimum 2 years' experience in a process plant related operations role 13
Lights-out process control PRO Lab PAT & sampling integration Process monitoring and MVDA modeling Adaptive/feedback unit operation control Robust control strategies 14
Industries becoming democratized With the advances in sensing, digitization, computation, storage, networking and software, all industries are becoming computable When an industry becomes computable, it goes through a series of predictable changes: It moves from being digitized to being disrupted to being democratized Tom Wujec, Autodesk Source: Quoted in Thank you for being late, by Thomas Friedman 15
Digitization of the process industry Digitilization is the key to success 16
Concept of a digital twin A digital twin is a virtual model of a process, product or service A digital twin is a collection of actual physics-based models reflecting the exact operating conditions, such as performance and failure modes, in the real world thanks to the Internet of Things (IoT) that it has become cost-effective to implement 17
Disruption of the process automation industry Disruption in terms of how businesses in this sector operate Cognitive automation results in new category of digital labour Disruption in terms of how the industry operates, enabled by cloud computing, Industry 4.0 technologies, open source automation Industry disruption from e.g. the platform business model Stages of platform business model development Digitizing key interactions in the marketplace Adopt platform-enabled business models Invite third parties to innovate on the platform 18
Democratization of the process automation industry Cloud-based solutions could potentially make users of process automation less dependent on traditional suppliers, and make process automation accessible to all. E.g. advanced process control could be democratized by providing any competent suppliers, based anywhere in the world, with the opportunity to control a particular process Issues to address include: Evaluating competing solutions Infrastructure for data exchange, payment, etc. Cyber security 19
Concluding thoughts Second machine age technologies are turning engineering services, including advanced process control, into a commodity, an making lights-out control possible Digitization of industries is making it possible to create digital twins of the real world Industries are becoming democratized, making it possible for anyone to provide an advanced process control solution, and for more users to benefit from automation Detailed process knowledge and strong customer relationships will provide a competitive edge 20