The purpose of tuning loops is to reduce errors and thus provide more ef cient operation that returns quickly to steady-state ef ciency after upsets, errors or changes in load. State-of-the-art manufacturers in process and discrete industries have invested in advanced control software, manufacturing execution software and modeling software to “tune” everything from control loops to supply chains, thus driving higher quality and productivity.
The “forgotten loop” has been the operator, who is typically trained to “average” parameters to run ad- equately under most steady-state conditions. “Advanced tuning” of the operator could yield even better out- puts, with higher quality, fewer errors and a wider response to uctuating operating conditions. This paper explores the issue of improving operator actions, and a method for doing so.