First, the problem of the critical low voltage solution identification is discussed.
This study evaluates the impact of behavioural solution identification strategies in GLUE on the quality of model output uncertainty.
The results show that the model output uncertainty varies with the behavioural solution identification strategy, and furthermore, a robust GLUE implementation would require considering multiple behavioural solution identification strategies and choosing the one that generates the desired balance between sharpness and reliability.
This design process may be considered as encompassing problem definition, solution identification, communication of an outcome to satisfy manufacturing requirements and application enabling the required functionality to be achieved.
Likely design changes include the following: Resources and duration needs for the project; methods for developing and producing the proposed project solution; identification of significant work items; contingency plans for specific elements of the project; program optimization elements.
The methodology includes four steps of work: superstructure development and data preparation, synthesis model development and solution identification, heat exchanger network design, and final process validation via rigorous simulations.
The methods studied include a closed-form solution, identification methods based on inverting the matrix of receptances, energy expressions developed from single-frequency excitation and responses as well as first-order perturbation methods.
To demonstrate the importance of behavioural solution identification strategy more appropriately, GLUE is also compared with two other informal multi-criteria calibration and uncertainty estimation methods (Pareto optimization and DDS-AU).
While the used VF2 algorithm was shown to be efficient for first solution identification, other approaches (e.g. CP-based) show better performance for full solution space enumeration [ 46, 47].
