JAN-FEB 2019

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INTECH JANUARY/FEBRUARY 2019 11 COVER STORY model is possible. Using the installed base of control systems throughout industry as a guideline, perhaps one in 10 loops warrant the use of feedforward, and the rest will perform satis- factorily, if not more reliably, based on feedback control alone. This calls into question the MPC paradigm of "wholesale" feedforward—literally hundreds of mass-produced feedfor- ward models—and suggests it might be a source as much as a solution to the persistent maintenance and performance record of MPC. The top priority of APC—as with single-loop control—is to reliably close the loops, and not necessarily to use feedforward in doing so. Classic selective feedforward strategy is implicit in figure 3. Updated paradigm These perspectives point toward an APC paradigm that is more affordable, agile, scalable, and reliable, based on durable qualitative (not detailed) models, sans embedded optimizers, and with more intuitive and succinct matrix designs. Figure 4 compares the traditional and proposed paradigms. In operating facilities, multivariable control applications come in all sizes—from a handful of variables to several doz- ens—so that a smaller footprint solution can bring progress on both fronts. It can provide more appropriate tools for the many applications that have remained below the radar of in- dustry's large-matrix paradigm. And it can provide an alterna- tive reengineering strategy for industry's many high-mainte- nance legacy applications. The proposed paradigm derives from lengthy experiences and lessons under the traditional APC paradigm. To the ex- tent this new paradigm has yet to fully emerge, industry may benefit from adopting it as a working vision going forward, to pursue these insights and lessons, encourage outside-the- paradigm thinking, move APC beyond its original paradigm, and bring about new and more viable and sustainable APC solutions for industry. n ABOUT THE AUTHOR Allan Kern, PE (, has 35 years of industrial process automation experience and has authored dozens of papers on more practical, reliable, and sustainable ad vanced process control solutions. Kern helps companies improve process efficiency, quality, and profits on site or with online consulting complementing in-house resources, helping bridge a skill shortage at many sites. He is the founder of APC Performance LLC and the inventor of patented Rate-Predictive Control (RPC®) and Model-less Multivariable Control (XMC®). For more information, visit View the online version at Figure 3. Historical manual multivariable control method. Notably, this method does not require embedded optimizers or detailed models. Rate-Predictive Control (RPC ® ) and Model-less Multivariable Control (XMC ® ) technology are based on this concept. Figure 4. Compare and contrast the traditional APC paradigm versus a new working paradigm (vision or road map) for APC going for ward. Traditional (largely obsolete) APC paradigm Emergent APC paradigm Incorporates detailed models, embedded optimizers, and large-matrix design practice. Based on simple qualitative models, sans optimizers, and small-matrix design practice. Wholesale use of feedforward model-predictive control. Selective use of feedforward control. Dominant focus on modeling and optimization. Primary focus on multivariable control. High cost and maintenance, limited life cycle. Low cost, long low-maintenance life cycles. Highly specialized, often third-party software and support. Core competency. Locally owned by on-site DCS engineers. Special budgeting and planning activities. Falls within normal operating plans and budgets. Complex DCS integration. Security and reliability issues. Native DCS deployment. "Wide-net" matrix design strategy, based on extensive plant testing. Small-matrix design practice, based on existing operating practices. Low agility, often an impediment to manufacturing flexibility. High agility, complements modern flexible manufacturing criteria. Goal is "optimization," based on large-matrix design strategy Goal is "automation," based on existing proven operating practices. Embedded steady-state optimizer, which is redundant while adding cost and complexity. Uses optimization results from higher layers of pyramid. Embedded path optimization to minimize transient error and cost. "Straight-line" path optimization while observing process speed limits. RESOURCE "The big story behind auto-tuning" Operators know a priori which handles to use to manage constraints and optimize operation. Operators make step moves based on safe operating practice and respecting the degree of process variability. Operators monitor actual process responses and adjust/taper moves accordingly. Determine directional moves Use safe practice move rates Monitor actual process response 5-sec. period (if automated) SP (or Target) PV (or ICV) Prediction

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