Advanced Process Control Modeling from an Advanced Planning and Scheduling Perspective

Further to basic process control strategies, advanced process control (APC) aims to improve the performance of the control by actuating towards input moves against effects of process disturbances. Typically addressing economic-based goals as objectives, APC includes a wide range of techniques embedded in process control systems generally found in continuous-process industries such as processing of food, fuels, bulk chemicals, metals, minerals, pulp and paper, among others. Different from the well-known APC techniques such as feedforward, decoupling, inferential, constraint control, etc., we address a novel approach to the dependent-independent or controlled-manipulated pairing of variables of the model predictive control (MPC), also known as moving horizon control (MHC). We introduce manipulated variables as batch-processes into the MPC formulation to build the dynamic model with their paired controlled variables. The manipulated-controlled MPC formulation is configured as an advanced planning and scheduling (APS) problem as opposed to a traditional APC problem whereby the manipulated-batch-process variables are considered with relative-time yields representing their finite impulse-responses or differenced step-responses. As we shall show, APC problems can be equally formulated as a batch-process in APS perspective. To eliminate the steady-state offset between the actual controlled value and its target or setpoint, it is well-known to apply bias-updating though other forms of parameter-feedback are possible. Typically, APS applications only employ variable-feedback, i.e., opening or starting inventories, properties, etc., but this alone will not alleviate the steady-state offset. A small and representative example highlights the APC as an APS modeling with parameter feedback.