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JAN-FEB 2019

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COVER STORY INTECH JANUARY/FEBRUARY 2019 9 depicts the essential difference between manual and automated multivariable control. The most common automated multivariable control technology in use today is model-predic- tive control (MPC). Prominent characteristics of MPC include the use of detailed process models, embedded optimizers, and a generally large-ma- trix approach to application design, i.e., dozens of variables and often hundreds of models. This combination was expected to be transformative for process control, but it has met with unexpect- ed consequences in cost, maintenance, and reli- ability. Industry has so far stood by MPC, so that more agile, affordable, and "owner-friendly" alter- natives have been slow to emerge and evolve. Optimization Within operating facilities, process optimization is carried out by many participants, such as pro- duction planning, process engineering, and op- erations. Together, these groups arrive at current constraint limits and optimization targets, and propagate them to the control systems via com- puter links, operating orders, word of mouth, etc. Most constraint limits and targets rarely change, while a handful change with operating condi- tions, such as feedstocks, equipment out of ser- vice, and time of year. On top of these activities, there may be similar sitewide and enterprisewide optimization layers (figure 2). In this picture, the role of the embedded MPC optimizer comes into question. It may have made sense in 1985, when few other real-time optimization programs existed in industry, but today the entire optimization hierarchy is nearly as automated as it needs to or can be. This makes the embedded MPC steady-state optimizer largely redundant, while it continues to add cost and complexity to the MPC application. MPC also incorporates "path" optimization, whose objective is to minimize transient cost and error as it moves the process from current condi - tions to optimal conditions. However, taking a simple straight-line path, while observing process speed limits along the way, may be a more effec - tive strategy in most cases. As with driving a car, observing speed limits and arriving safely is usual - ly more important than arriving quickly. Industry en dorses this concept whenever it uses approach- es such as move suppression, extended closed- loop response times, soft limits, and reduced op ti- mization speeds. Why not just post a safe speed? The essential role of APC at the control sys- tem layer is control , i.e., to push constraint lim- its and pursue optimization targets in the live process environment, where the related process values—not the limits and targets themselves— are subject to change in real time. Control needs to execute at high frequency, but optimization normally does not. This paradigm has the po- tential to simplify APC technology by eliminat- ing embedded optimizers that are potentially redundant or unnecessary in most applications. Process models Model-based control requires reliable process models. In the original APC paradigm, this need was met by a plant test and subsequent model Efficiency, quality Capacity, reliability Optimal operating target APC Manual multivariable control (no APC) Process constraints $ $ $ $ $ $ $ $ $ $ P o t e n t i a l A P C b e n e f i t s Local Global Enterprise optimization Advanced process control (APC) (sans optimizers) aka RTO Appropriate data links and time frames for each optimization component Site optimization (by operating team) Unit optimization Figure 1. Manual versus automated multivariable control. Automated multivariable control can capture incremental earnings, because it automati - cally backs the process away from encroaching constraints and pursues receding constraints. With manual multivariable control, operators tend to keep the process farther from constraints and make moves less often, typically incurring an associated penalty in capacity, yield, energy, or quality. Figure 2. Updated process optimization paradigm. The essential role of APC is multivariable control (i.e., pushing constraint limits and pursuing optimiza- tion targets at the control system level), where the related process values— not the limits and targets themselves—are subject to change in real time. Updated limits and targets, which are few and infrequent, propagate from the upper layers of the pyramid as appropriate.

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