Abstract: An example system includes a computer program that detects a misalignment for a variable between two layers of multiple layers of optimization. The computer program determines a degree of the misalignment between the two layers of optimization using a dimensionless metric. The computer program ranks the misalignment using a degree of misalignment. The computer program determines diagnostic information using patterns of the ranked misalignment. The system includes a physical plant that includes adjustable components that are adjusted using the diagnostic information according to the ranked misalignment to synchronize the misalignment between the two layers of optimization.

Abstract: Systems and methods are provided for interfacing multiple layers of optimization for a model of one or more processes in a processing environment to achieve increased or maximized stability in the underlying layer. To improve consistency between the solutions at the different model levels, the lower level of optimization can have extra constraints added to the optimization problem which target variables at their unconstrained values in the upper layer of optimization. The systems and methods can facilitate selection of variables to receive an external target such that stability of the solution is improved or maximized. This can be achieved, at least in part, by identifying variables that provide a reduced or minimized condition number for a sub-matrix in the lower level model when an additional external constraint is applied.

Abstract: Systems and methods are provided for interfacing multiple layers of optimization for a model of one or more processes in a processing environment to achieve increased or maximized stability in the underlying layer. To improve consistency between the solutions at the different model levels, the lower level of optimization can have extra constraints added to the optimization problem which target variables at their unconstrained values in the upper layer of optimization. The systems and methods can facilitate selection of variables to receive an external target such that stability of the solution is improved or maximized. This can be achieved, at least in part, by identifying variables that provide a reduced or minimized condition number for a sub-matrix in the lower level model when an additional external constraint is applied.