PNNL

Abstract

Gekko is an optimization suite in Python that solves dynamic optimization problems involving mixed-integer, nonlinear, and differential equations. Codes from other packages, like those with machine learning (ML) algorithms, have not been implemented or interfaced into Gekko. The purpose of this effort is to integrate common ML algorithms like Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Artificial Neural Network (ANN) models into Gekko, allowing it to solve optimization problems where data-based models are used. In addition, the inclusion of uncertainty quantification (UQ) within an optimization framework with ML algorithms has not been thoroughly explored, so compatible models and UQ methods are investigated in this endeavor. These methods include Ensemble methods, model-specific methods, conformal predictions, and the Delta method. A simplified optimization problem involving Nuclear Waste Vitrification at Hanford to maximize waste loading is presented for demonstration and model comparison purposes. ML models are compared against the current Partial Quadratic Mixture (PQM) model in an optimization problem in Gekko, and higher performance is observed in the ML models. The example problem shows that these tools can be used in similar industry settings where easier use and better performance is needed over classical approaches.