Handling Constraints and Uncertainty in Chemical Process Operation Using Nonlinear Model Predictive Control

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Control of chemical processes has long grappled with challenges such as the presence of constraints on the inputs (valves and pumps limited in capacity), nonlinearity and uncertainty (disturbances or plant model mismatch). While there has been significant research in control theory over the last several decades, the availability of modern sensing and computational capabilities have opened up the possibilities of control designs that were in the past computationally intractable. ***The notion of use of control strategies to achieve the desired operation of a process is a well established concept. The three `pillars' on which the implementation of control algorithm stands are sensors (such as temperature sensors), a model, and a control actuator or input (such as valves or pumps). The desired operation of various systems is quantified in terms of the measured variables. Connecting these pieces is a model of the process, or essentially an understanding (typically quantitative) of how the control actuators effect the measured process variables. The controller then needs to determine how best to move the control actuators around to achieve the desired behavior. ***Early control approaches often neglected nonlinearity, the effect of other manipulated variables on the process variable in question, and generally favored making less aggressive control actions to avoid the negative impact of these unmeasured effects or disturbances.*The entire field of process automation thus stands to benefit from newer control approaches that fully harness the available computational resources to address the important problems of nonlinearity, constraints, handling of faults, and aided by improved models. The proposed research, by addressing the unsolved problem of determining the `best direction' to push the system to achieve desired operation subject to actuator limitations, will impact every process where automation is involved (from refineries to building temperature control to vehicle control). With a conservative estimate of one percent improvement in efficiency, this readily translates into billions of dollars in savings in Canada, and much more worldwide. The other thrust on improved models will unite statistical modeling tools with state-space based control tools for batch process control saving costs for production processes ranging from bio-pharmaceuticals to specialty chemicals. Finally, the problem of fault-detection and handling will be addressed for large scale chemical processes positively impacting environmental as well as safety issues in Canadian industry. Direct technology transfer will occur through collaboration with the industrial partners of the McMaster Advanced Control Consortium, of which the PI is a member. Given the rapid placement of students trained in the area process control, the 25 HQP resulting from the grant will fulfill current and future need in Canadian industry.*** **