Yokogawa offers OTS software and services capable of creating high fidelity, dynamic simulations of processing plants. The purpose is to provide a replica of the plant they simulate, which can be loaded into pre-defined states and operated without risks or consequences associated with real plant operations. Such set ups have the potential to provide great benefit to process plants. This article intends to outline the potential uses and benefits of utilizing such a simulation across a wide range.

Operator Training Simulators

The most typical use for a high fidelity dynamic simulation is as an Operator Training Simulator, or OTS for short. An OTS is an excellent tool for training new operators by immersing operators in the environment that they are expected to work, allowing for them to develop a familiarity with the control systems, the expected response to manipulation, and the consequences of incorrect operation without putting the operation at risk. The ability to jump to a specific state of operation reduces downtime during training, and allows for the observation of a trained operator at any time instead of waiting for a particular operation to be required within the physical plant.

The utilization of an Operator Training Simulator, therefore, has the potential to reduce training duration for new operators, the man-hours required by the assisting staff and trainer, and improve comprehension by reducing the level of abstraction required to apply the training to its real world application.

Commissioning of DCS

High fidelity dynamic simulations serve a great benefit for the commissioning of Distributed Control Systems, or more commonly referred to as DCS. They provides the opportunity to find and fix issues prior to subjecting the new system to the real plant, reducing the risk of damage and resources required. Combining this with the ability to quickly change the state of the plant, the time saved by utilizing a simulator is a huge benefit to the commissioning process.

Optimization of Procedures

Procedure optimization can also be achieved through the use of a high fidelity dynamic simulation. Different operators throughout a process plant may have different ways of achieving the same end result, each with different advantages and disadvantages. Through the use of a simulator, operators can demonstrate and attempt different procedures which can then be evaluated by comparing the speed, number of alarms, complexity and room for error to determine which procedure to put forward as the recommended for operation.

Unique insight can be uncovered by performance testing different procedures.  For example, you may find that operators of different experience and skill levels may perform better in different procedures and that the recommended procedure can be tailor-made to meets a specific operator in order to maximize efficiency and minimize the impact of that person on operating cost.

Work Load Balancing

In a previous article on the Advanced Solutions Blog (Smart Workload Balancing), a potential issue of automation restricting an operator’s situational awareness was raised. In industries of high automation, an operator may become detached from the process and therefore not be suitably informed if there arises a situation when they need to take action.

A high-fidelity simulator can be designed to trigger equipment malfunctions or alarms instantaneously without warning to the operator and therefore determine the average response time of operators to an alarm for various activity levels. Using this data, points of excessive and insufficient workload can be determined and processes adjusted accordingly; possibly increasing man-power or the level of automation for excessive workload and decreasing automation or adding additional tasks during lulls.

Testing Process Improvements

Another use for a simulator would be to test improvements to the process plant. Consider a power plant that is investigating the use of a real-time energy optimizer, such as Soteica Visual MESA.  A simulator could be configured with costs and resources of a particular day with a known profit then be allowed to run with the optimizer in place. By comparing the difference between the simulated profit and the actual profit over multiple days, an accurate estimate for the expected return on real-time energy optimization can be used to determine if such a set up would be worthwhile in the real plant. Likewise, other improvements, such as alarm consolidation and automation can be tested for their benefits.

Scheduling of Maintenance

Throughout the life of a process plant, production will be impaired or disrupted due to the need for maintenance. Consider a situation where several pieces of equipment are due for non-critical maintenance. Taking any one piece out of service will impact production; the value of a particular piece of equipment varying with the plants current operating mode.

Taking multiple items out of service at the same time may have a lesser impact than doing each individually, and the effects could be reduced further by completing the undertaking during particular stages of operation. Using a high fidelity simulator provides a reasonable estimation as to the impact of a particular maintenance job, allowing for the ideal time to be determined, and the impact of the undertaking upon production to be minimized.

Predictive Modelling

Due to the fact that a simulator does not need to be tied to physical processes, if the control system it utilizes is capable of handling the increased dataflow and computational power of accelerated time, then a high fidelity dynamic simulation can be utilized for predictive modeling.

Consider a situation where significant profit could be made for operating at above normal production. If a simulator was capable of running at 20 times greater than normal speed, you would know within 1 minute if the increased production would trip any alarms during that time period. By the end of that 20 minutes, the simulator would have been able to project the next 6 hours and provide reasonable estimates as to when issues from exceeding standard operating parameters may occur.

Conversely, with an unexpected but non-critical malfunction, such as valve that does not fully open, the losses to production can be projected and an informed decision can be made as to if the affected unit continue as is, operate at reduced capacity or be shut down to prevent excessive resource expenditure.

Conclusion

Typically, a high fidelity dynamic simulator is used for Operator Training. The primary goal of which is cost mitigation by familiarizing future operators with the control scheme utilized on site and the consequences of incorrect operation so as to minimize mistakes. When properly built, these simulators can, as outlined in this article, provide benefits well beyond those of OTS. What other potential solutions could they provide?