With the advent of computers in process control, it became possible to automate and “close the loop” on multivariable control, with obvious potential to improve the quality of constraint control and optimization. Multivariable control technology that combined mathematical models of process interactions, economic optimization routines, and matrix-based solution techniques soon appeared to accomplish this, and the rest is history. Since the 1980s, model-based predictive multivariable control (MPC) has thoroughly dominated the field of advanced process control (APC). Today, the terms are usually synonymous.
But MPC has not been without difficulties. Although a limited number of applications are delivering high value, and many are delivering partial success, MPC performance levels overall have remained low. “Degraded” MPC performance and MPC applications that have “fallen into disuse” are well-known, if rarely highlighted, industry concerns. Users have assumed this situation would correct itself with time, but today installation costs remain high, a manageable ownership model has not emerged, and performance levels continue to be low. Industry enthusiasm for MPC, once unbridled, has become circumspect, and decision makers are increasingly reluctant to allocate the high levels of financial and human resources that once seemed warranted for MPC.
Industry is thus faced with a question it thought was settled: Is MPC the technology of choice for automated multivariable control going forward, or is a reevaluation indicated at this juncture? This article explores the role of models in traditional MPC, their part in its cost and performance history, the necessity of models going forward, and the viability of an alternative model-less approach to multivariable constraint control and optimization, based on industry’s experiences and lessons of the past 20 years.