Manufacturing activity continued to decline at a rapid rate during the month of December. The decline covers the full breadth of manufacturing industries, as none of the industries in the sector report growth at this time.
So if you’re a process or plant or automation engineer, what do you do?
I exchanged emails with Emerson’s James Beall to ask him what changes he’s hearing about from process manufacturers since economic conditions began slowing in the fall. James and the variability management consultants help process manufacturers find ways to optimize their process.
Certainly, the focus has shifted from increased production to decreasing the costs of goods sold. Energy savings have even more emphasis than before. Distillation process are heavy energy users and are often using 5-25% more energy than is required–unless they are using a properly designed Model Predictive Multivariable Controller (MPC) or advanced regulatory control strategy.
Besides lower energy usage, improved control performance reduces variability allowing the process to operate closer to its constraints, which can improve yield.
In an earlier process variability post, James cited a study from the team’s work that showed major causes of variability include control valve performance (30%), improper tuning (30%), and improper process and/or control scheme design (20%). In the ideal world, you could optimize your plant and reap these benefits throughout the plant’s lifecycle. For the 20% process design issues you can. Unfortunately for the rest, the law of entropy being what it is–production processes tend to disorder over time. Valves stick as they wear, sensors plug, vibration on rotating equipment increases, etc.
Where digital instrumentation exists, the devices can report these issues to the operations and maintenance staff. A 4-20mA analog input signal provides a process variable, but not if the measurement signal is good. Likewise, a 4-20mA analog output signal to a control valve without feedback does not let the control algorithms know if the valve has moved to its intended location. These issues have to be uncovered through offline analytical techniques.
These all combine to change process dead times (the time delay from an output change to a change in the process variable) and the control dynamics of the process. The goal is to try to make the process dynamics as linear as possible and minimize dead time.
James recommends that you measure these changes in dynamics, annually at a minimum, and more frequently if the ROI justifies it. He and the team use Emerson’s Entech Toolkit to identify common dynamics such as first order, second order overdamped and integrator+lag. This helps identify the process dynamics so that the control loops can be properly tuned.
With the process dynamics clearly understood, and final elements and measurement devices repaired or replaced, James and the team help plant engineers select the proper control algorithms for the process dynamics and tune the loops for best response without oscillation.
With the process properly lined out, it can now operate closer to operating limits due to reduced variability. Also, waste is reduced and less energy is typically consumed. These all directly impact the bottom line–a very good thing for these economic times.
Well-tuned regulatory control opens up the opportunity to also apply advanced control algorithms like MPC at a process unit level to further improve control performance and reduce operating costs.
(I’m trying a trick from Gary Mintchell to stand to see if that adds more energy to my voice in these podcasts.)
Update: I just saw ARC Advisory Group’s Larry O’Brien reference the ISM report. I was hoping my post was first because I found it by Googling around. Alas, Larry’s post was the day before. Read it for more on the ISM findings and trust that I’d link to his post, if he was my original source!