Implementing a simulation network
Critical steps to implementing a successful simulation network include establishing a simulation policy, defining long-term needs, and training your operations team.
It is a common understanding that manufacturing is one of the most important applications of implementing a simulation network. So, what does this mean to you and your plant? There is a direct overhead cost with simulation as it requires simulation software, license files, hardware, and upkeep. Can you justify the cost offset of a simulator? The answer is yes, it is worth all the products your plant makes if systems go down due to edits or failed startups. It is worth the cost replacement of a destroyed piece of equipment. It is worth the cost of having a simulated production environment that allows for cross training of your operators without the worry of a production near-miss and smoothly implementing expansion growth and production improvements.
Step 1: Establish a simulation policy
The approach to simulation is a life style change if you do not already have it. The best use of a simulation would be a policy that ensures all changes to operations must be made in simulation and tested. It is easy for operations to think that it is just one small change, go ahead, and make the edit. This is a dangerous comment because anyone that has been in the automation industry for a while knows it is not hard to impact another area by that one small change. It is the ripple effect and sometimes nothing happens, but there will be that one instance when problems will materialize. These problems could be circumvented by the use of a simulator. Not only will policy protect you, it will also keep your simulation network as up-to-date as your facility.
Step 2: Define long-term simulation needs
So, we are all sold on simulation—now how much of this simulation do I need? There are several levels of simulation that can be implemented into practice. One type is known as “high fidelity,” which has the ability to actually cause process upsets while running scenarios to test the operators' reactions to issues your plant may have. Another alternative is a simpler model using tie back logic internal to the processor to mimic the process control. This would be the minimum level required to test code and process interaction.
Simulation benefits both operations and engineering, but who are the real end-users? Engineering needs simulation to vet new systems and changes to the existing process. Then once the process changes are proven, the operators will need to be retrained on said changes. The minimum level will support these needs. If policy will be in place that will test operators annually as an internal certification, then a high fidelity system would provide the best benefit. With a high fidelity system, operators will be able to drive the simulator without having engineering evolved in monitoring reactions and variable changes.
Step 3: Put into operations through simulation
How do you measure the success of your next automation project? Is it the cost savings, personnel reduction, efficiency gains, or realignment of standards within the plant? Yes and no! How did the startup go? Was it seamless or was the downtime extended to make the changes work in the new environment. The loss of project budget will happen without the benefits of a seamless startup. Extended 24-hour coverage with process changes is a very costly endeavor. I could not imagine, nor would want to be a part of, a startup without the use of a testing platform.
In closing, there is one last part to make sure the simulated code makes its way into the production environment. That requires a rollout plan. That is a document for another time as it even more critical to success.
This post was written by Dave Cortivo. Dave is a senior engineer at MAVERICK Technologies, a leading automation solutions provider offering industrial automation, strategic manufacturing, and enterprise integration services for the process industries. MAVERICK delivers expertise and consulting in a wide variety of areas including industrial automation controls, distributed control systems, manufacturing execution systems, operational strategy, business process optimization and more.