Model predictive control systems for process automation plants

A method of determining state parameter clusters for a model predictive control (MPC) device of a process automation plant, the method comprising:

• receiving a specified number of operating point clusters;
• obtaining, via an input interface, at least one operating state parameter associated with at least one component of the process automation plant, the at least one operating state parameter obtained at multiple time points during operation of the process automation plant to define multiple historical operating state parameter values;
• applying a clustering algorithm to the multiple historical operating state parameter values to define a specified number of operating regions, the specified number of operating regions equal to the specified number of operating point clusters;
• generating, for each of the specified number of operating regions, an MPC process model according to parameters associated with the operating region;
• sensing a current state parameter of the at least one component of the process automation plant;
• and
• controlling operation of the at least one component of the process automation plant according to the MPC process model associated with the current state parameter.

A model predictive control (MPC) device, comprising:

• an input interface configured to receive an industrial process input associated with at least one component of a process automation plant;
• an output interface configured to transmit a control instruction to control operation of the at least one component of the process automation plant;
• memory configured to store an online model estimation module, and computer-executable instructions;
• and
• at least one processor configured to execute the computer-executable instructions to:
• identify at least one current manipulated variable and at least one current process variable of the process automation plant;
• generate, using the online model estimation module, an MPC process model according to the at least one current manipulated variable and the at least one current process variable of an industrial process of the process automation plant;
• receive an industrial process input via the input interface;
• predict, using the generated MPC process model, a future industrial process output according to the received industrial process input;
• calculate a target operating point according to the predicted future industrial process output;
• determine a control signal to drive the industrial process to the calculated target operating point;
• output the determined control signal via the output interface to control operation of the at least one component of the process automation plant;
• subsequent to outputting the determined control signal, identify an updated manipulated variable and an updated process variable of the process automation plant;
• and
• generate, using the online model estimation module, an updated MPC process model according to at least the updated manipulated variable and the updated process variable of the process automation plant.