JAN-FEB 2019

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10 INTECH JANUARY/FEBRUARY 2019 WWW.ISA.ORG COVER STORY identification, with the resulting models expected to have an indefinite life expectancy. However, experience has shown that many models change frequently, even dynamically, for a wide variety of reasons. Over the years, the conventional wisdom regarding model life has been reduced to five years, and then to two years. Today industry is pursuing real-time model updates. But even this is unlikely to "square this cir- cle," for the same reasons that derailed autotuning. Model change poses a fundamental conundrum for autotuning and model-based control. To move forward, APC needs to embrace the idea that pro- cess models are basically a moving target. This has always been a fact of life in the single-loop tuning world, where the principles of preserving process stability and respecting a degree of the unknown have always taken precedence over minimizing transient error. In retrospect, there is no reason these principles should not apply to multivariable control, too. Indeed, MPC experience shows that these principles re main universal and indispensable. The same insight can be gleaned from examining how op erators historically carry out manual multivariable con- trol, which they do without relying on detailed models or optimizers. By virtue of their experience and training, op erators know important constraints, optimal targets, and appropriate handles; they make moves that safeguard pro- cess stability and respect the historical degree of uncertainty; and they monitor actual process response—not yesterday's or last year's response—before making further adjustments accordingly (figure 3). The effectiveness of manual multivariable control has al ways been dependent on the amount of time and initiative the operator has available, and upon each operator's individ- ual level of expertise. These—timeliness and consistency— are the hallmarks of automation. Matrix design In the original APC paradigm, where models were assumed to be reliable, having a larger matrix (more variables) and a denser matrix (more models) was considered the best prac- tice, because in principle it resulted in a more complete solu- tion. But in today's world, where models are understood to be variable, more models can translate into more problems, for both control and optimization. Industry has experienced this in the high maintenance and degraded performance of many MPC applications. The extended operating team, especially operators and process engineers, normally know a priori how to effectively manage process constraints and pursue optimization targets, by virtue of their knowledge and experience. This suggests that existing (established and proven) operating practices can pro- vide the best basis for matrix design. It will also normally result in a much smaller and less dense matrix than the traditional plant test paradigm, whose strategy is to cast a wide net. A smaller matrix can be expected to reduce cost and main- tenance proportionately, especially if the remaining variables and models are the essential ones, already proven in use by virtue of actual operation. In the traditional paradigm, the APC project goal is usually "optimization," based on a large-matrix strategy, but in the small-matrix paradigm, the central goal is "automation," based on existing, proven, manual multivari- able control operating practices. This may sound less lofty, but it could be a more effective focus for APC going forward. Lessons from feedforward The primary limitation in figure 3, from a process control standpoint, is the lack of model-predictive feedforward con- trol action, which has always been a cornerstone of the MPC paradigm and a key piece of the expected transformation (of process control into a more exact science). However, feedfor- ward is the single-loop equivalent of model-predictive con- trol, and its long history tells a different story. The potential power of feedforward (to reject disturbances proactively) has always been well known. Feedforward func- tion blocks have been available since industry's first distrib- uted control systems (and in programmable logic controllers, analog, and pneumatic systems before that). Yet, historically, feedforward has found very limited usage, even at the much more manageable and selective single-loop level, due to the complexity, risk, and maintenance a feedforward model adds to any loop. 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