True Process Variability Costs, Causes and Cures

While we focus heavily on technical process automation-related subjects here on the blog, financial-related ones are critically important. I mention this because Emerson’s Doug White will be presenting Making Operations Predictable Through Automation: The True Cost, Causes and Cures of Process Variability at this year’s Emerson Exchange event, October 24-28 in Nashville, TN.

True Cost, Causes and Cures of Process VariabilityDoug shared a draft of his presentation and I’ll highlight some of the key points from it. He wants those attending his presentation to recognize the effects of excessive process operational variability in their plants and the value in its reduction. He’ll share ways to reduce this variability through enhanced automation, advanced automation, and asset management.

Process operational variability comes in many forms—high frequency disturbances/noise, operating target changes, process upsets, and more. The high frequency or random variability can be caused by natural variations in materials, equipment, or the process itself. Cyclic variability is often related to the process characteristics or caused by issues associated with control loop performance. Sudden disturbance or process upsets are low frequency in nature and caused by load changes, process setpoint changes, and batch/discontinuous operations such as new batches and grade changes.

Cyclic variability is very common and often induced by interactions between the valve, process, and controller within a control loop or between loops. Often this variability can propagate throughout the process. Doug cites an example where inlet and outlet streams are connected via heat exchangers in order to maximize thermal efficiency. This coupling provides heat interaction between inlet and outlet streams and tends to propagate variability by causing incoming disturbances to impact the operation of the entire unit. He shares an example how this can happen on a natural gas liquids (NGL) fractionation demethanizer. He explores other processes where variability can propagate including process recycle and common headers.

Excessive variability can impact product quality, reduce equipment capacity, increase energy consumption, reduce equipment reliability and lifespan, and cause safety and environmental concerns. He shared some recent examples of excessive variability creating unplanned shutdown in chemical plants, off-spec reblend operations in refineries, delay pharmaceutical plant startups, frequent boiler trips in pulp and paper mills, and paper breaks on newsprint machines.

Reducing variability can occur by looking at ways to eliminate or reduce disturbances, improving control system performance including the controller, final control element and sensing element in the control loop, making process modifications, and if possible, increasing product blending. Reducing this variability allows the plant to run closer to the specification limit. This can mean increased production, increased yield, improved equipment productivity and reliability, and reduced operating costs through fewer unplanned shutdowns, less maintenance, and less energy consumption.

Doug offers some case studies including a fluid catalytic cracking unit and an ammonia process. He also shares a methodology to help you identify and justify variability reduction processes. It starts with identifying the plant incentives, which might include production losses, product quality issues, excessive energy consumption, or excessive maintenance costs. Next, prioritize the loops to address and review their recent performance history. Next, conduct a high-speed, online loop performance and variability audit looking at all elements—sensor, controller, and final element in the loop. Then, determine the causes of variability using spectral analysis techniques to identify variability sources and pathways and review the control configuration and process issues.

After this fact-finding and analysis is performed, recommend changes in field devices, loop tuning, and control configuration. Process simulation can help to model the process and proposed changes and help derive the optimal tuning parameters. Finally, prioritize these recommendations and provide budgetary estimates of the costs as well as the benefits from reduced variability. Once executed, follow up and document changes and performance improvements to help justify additional projects.

If you’ll be joining us in Nashville, Doug’s presentation may be one you’ll want to add to your schedule.

Leave a Reply