Integrating e-Learning, 3D, Universal Simulations and Customized OTS for Competency Development: A Flip Model

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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

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•  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.

How It’s Done

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•  Most companies use full-scope operator training simulators (OTS) to teach new console operators and to improve the skills of experienced staff members.

How It’s Done

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Most global 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.

How It’s Done

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How do 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?

How It’s Done

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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.

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Learn process fundamentals with self-paced tutorials

Practice operations and troubleshooting with generic dynamic simulations

80% of the Learning, 20% of the Cost

Any new knowledge not utilized within 72 hours is likely to be lost.

TUTORIALS SIMULATIONS

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Simulation Configuration: Standalone Dual Monitors

Simulation – Standalone Dual-Monitor Version

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Simulation in a Classroom

Trainee Trainee TraineeTrainee

Instructor

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Trainee Trainee TraineeTrainee

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Applied Learning Through Generic

Simulation Instructor Led Process

Fundamentals Self-Paced Tutorial

Real-World Knowledge

Transfer Mentoring and

Hands-On Training

Unit-Specific Experiential Learning

Custom Operator Training Simulators

Process Control Self-Paced Tutorial

Cost of Training Asset

Matching the Solution to the Problem

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Learn the “why” before the “how”

Optimal Learning Progression

Tutorial-Based Fundamentals

Simulator-Based Fundamentals and Basic Operations

Mentoring and Knowledge Transfer

Plant-Specific Simulation and

Experiential Learning Val

ue

of A

sset

Value Training

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EnVision

Revenue

Cost

Risk

Agility

The EnVision Advantage

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Prepare for the unexpected

Confident performance

Recognize cause and effect

Risk

Improvement in competency and understanding decreases risk in general

Minimize Risk

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Create an Agile Workforce

Transition: outside to inside operator

Shorter learning curve

Consistency across the fleet

Agility*

Deliver the same foundation of knowledge to your entire team

*Agility: The ability to move quickly and easily

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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

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Our Generic/Universal Simulators Use High-Fidelity Mathematical Models

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Physical Properties and Component Database

•  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

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Mathematical Objects

•  Reusable components •  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)

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Solution Methods: An Inspiration 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)

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Solution Methods

•  Differential systems: –  Integration method based on system and

accuracy requirement –  Ranging from Euler to multi-step predictor-

corrector techniques like fourth-order Runge-Kutta, Gear’s multi-step predictor-corrector method and so on

•  Hybrid methods are used commonly

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The Result: A High-Fidelity Dynamic Universal Simulation

For more information:

Go to: www.GSES.com

Call: 800.638.7912

Email: info@gses.com

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