E-learning, 3D and Simulation for Competency Development

E-learning, 3D and Simulation for Competency Development

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  Integrating e-Learning, 3D,Universal Simulations and Customized OTS for Competency Development: A Flip Model [email protected]
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  Competence Development &Assurance (CDA) •  Competence: Knowledge, skills & attitudes (behaviors) (We cannot observe attitude, but we can observe behavior) •  Competent Person: Has competence and demonstrates competence by applying it on the job •  Competence Development (CD): A set of “fit for purpose” learning activities to develop an employee’s competence •  Competence Assurance (CA): Ensures that the employees have required competence to perform at the required standard and in an actual work situation 2
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  How It’s Done • The most knowledgeable and experienced console operator or engineer is also a mentor. •  Like any DCS or software package, a mentor is also a highly valued asset. •  Therefore, a mentor’s time should be optimized for knowledge transfer. 3
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  How It’s Done • Most companies use full-scope operator training simulators (OTS) to teach new console operators and to improve the skills of experienced staff members. 4
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  How It’s Done Mostglobal petroleum plants have 10 to 20 major processes, and it is impossible to have custom OTS due to resource constraints. –  The DCS-based OTS time is limited typically by the hardware and the number of instructors available. –  Before trainees can use the OTS efficiently, they must have basic knowledge of the DCS and process fundamentals, otherwise they are wasting valuable simulator and instructor time. –  Not suitable for field and maintenance operators, process technicians and engineers. –  Initial investment, operational, maintenance and life cycle costs. 5
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  How It’s Done Howdo you ensure: –  Valuable mentoring time is spent transferring critical knowledge and experience? –  Optimized use of high-value assets such as OTS and other T&D resources? 6
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  New Learning Strategies:A Flip Model Khan Academy pilots education tutorial at Cupertino and Los Altos schools The academy uses technology to provide more engagement and progress with different learning stages. 7
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  80% of theLearning, 20% of the Cost TUTORIALS Learn process fundamentals with self-paced tutorials SIMULATIONS Practice operations and troubleshooting with generic dynamic simulations Any new knowledge not utilized within 72 hours is likely to be lost. 8
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  Simulation Configuration: Standalone DualMonitors Simulation – Standalone Dual-Monitor Version 9
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  Simulation in aClassroom Instructor Printer Trainee Trainee Trainee Trainee Trainee Trainee Trainee Trainee 10
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  Matching the Solutionto the Problem Applied Learning Through Generic Simulation Process Fundamentals Real-World Knowledge Transfer Unit-Specific Experiential Learning Mentoring and Hands-On Training Custom Operator Training Simulators Instructor Led Self-Paced Tutorial Process Control Self-Paced Tutorial s s et ing A rain of T Cost 11
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  Optimal Learning Progression Learnthe “why” before the “how” Value of Asset Simulator-Based Fundamentals and Basic Operations Tutorial-Based Fundamentals Mentoring and Knowledge Transfer Plant-Specific Simulation and Experiential Learning Value Training 12
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  The EnVision Advantage Risk Revenue EnVision Agility Cost 13
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  Minimize Risk Improvement incompetency and understanding decreases risk in general Prepare for the unexpected Risk Confident performance Recognize cause and effect 14
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  Create an AgileWorkforce Deliver the same foundation of knowledge to your entire team Transition: outside to inside operator Agility* Shorter learning curve Consistency across the fleet *Agility: The ability to move quickly and easily 15
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  Fundamentals of Upstream • Amine unit •  Glycol contactor and regenerator unit •  Gas, oil separation process •  NGL/LNG feed treatment consisting of: –  Feed filter –  Molecular sieve dehydration unit –  Mercury removal unit •  Propane refrigeration unit •  Multi-component refrigeration unit •  Gas processing plant 16
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  Our Generic/Universal SimulatorsUse High-Fidelity Mathematical Models 17
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  Physical Properties and ComponentDatabase •  Components and properties specific to model •  VLE models –  Ideal mixtures: Composition-independent representation like Antoine’s vapor pressure expression. –  Non-ideal mixtures: •  Equation of state methods •  Composition dependence and binary integration parameters 18
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  Mathematical Objects •  Reusablecomponents •  Both for process and instrumentation •  All objects use unsteady state mass and heat balances (differential equations) •  Multiphase equilibrium (VLE, LLE, VLLE) (mostly algebraic) •  Kinetics and alternative approaches (differential and algebraic) 19
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  Solution Methods: AnInspiration from Jose Maria Ferrer •  Method based on application: –  Nature of the system (stiff, coupled, etc.) –  Accuracy requirement –  Real-time constraints •  Non-linear algebraic systems: –  Newton-Raphson method with improvisation –  Efficient matrix operations (mostly LU decomposition, Gaussian elimination) 20
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  Solution Methods •  Differentialsystems: –  Integration method based on system and accuracy requirement –  Ranging from Euler to multi-step predictorcorrector techniques like fourth-order Runge-Kutta, Gear’s multi-step predictor-corrector method and so on •  Hybrid methods are used commonly 21
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  The Result: AHigh-Fidelity Dynamic Universal Simulation 22
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  For more information: Goto: www.GSES.com Follow us on: Call: 800.638.7912 Twitter @GSESystems Email: [email protected] Facebook.com/GSESystems