PROCESS CONTROL
Operation-driven matrix design A multivariable control matrix has manipulated variables (MVs) on one axis, controlled variables (CVs) on the other axis, and models in the matrix that indicate a relationship between that MV/CV pair. Effective multivariable controllers use the right models for various control and optimiSation needs. Allan Kern reports.
M
atrix design practice refers to how control engineers go about designing the matrix at the heart of any multivariable controller. A multivariable control matrix consists of manipulated variables (MVs) along one axis, controlled variables (CVs) along the other axis, and models at various locations within the matrix, which indicate a relationship between that MV/ CV pair (Figure 1). Designing the matrix consists of selecting the MVs, CVs and models which, for various control and optimization purposes, are wanted in the multivariable controller. ‘Large-matrix’ design practice has been dominant in industrial applications for the last 30 years. In large-matrix practice, a wideranging plant test is used to identify all related process variables and process interactions (models). An underlying principle has been more variables and more models yield a more complete solution, for both control and for optimization purposes. It has been routine for large-matrix practice to result in one large multivariable controller spanning an entire plant unit, with dozens of variables (sometimes more than 100) and hundreds of models (sometimes upwards of 1,000). A side-effect of this approach is that most installed multivariable controllers in the process industry today are large, complex, fragile, costly, and challenging to own, operate and maintain. Moreover, the large-matrix approach has excluded smaller multivariable control applications that may be warranted
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February 2020
based on more basic control and operation improvements, but which do not necessarily justify the high cost threshold of the large-matrix paradigm. In conventional multivariable control technology, the built-in optimizer always has been considered an essential piece, so most involved never questioned it. Instead, the search has looked for better ways to support and maintain the largematrix controllers. However, with insight from decades of experience, it is now possible to see alternative ways to build smaller and more efficient multivariable control applications to address operation and control needs without a built-in optimiser, and therefore without the side-effect of growing the matrix beyond its basic control and operation scope.
Operation-driven matrix design Operation-driven matrix design can reveal important automation opportunities that have previously remained ‘below the radar’ of
large-matrix practices. It differs from optimisation-driven design in key ways. It often results in smaller multivariable controllers, rather than one large controller, with fewer variables and models. When concerns arise regarding a variable’s importance, its efficacy for control, or the reliability of its models, it is often left out of the matrix. Leaving it out errs on the side of reliability. Keeping it errs on the side of optimisation. The overarching goal of operationdriven matrix design is automation, not optimisation, and such a design strives to include only necessary, reliable, and worthwhile parts. In operation-driven matrix design, groups of related single-loop controllers are identified, based on frequent (often highly coincident) changes to setpoints, outputs or modes. This activity represents manual open-loop multivariable control being carried out by the operating team. The objectives are to:
Figure 1: Multivariable control matrix for an atmospheric crude distillation column, based on operation-driven design practice. The matrix is smaller than traditional crude column applications. It addresses primarily column pressure and product inferential quality control, which are often the most valuable objectives of this application – and often the ones found ‘unclamped’ in existing large-matrix applications. Images courtesy: APC Performance LLC
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