Speaker
Description
Optimizing complex processes is crucial in industries like food production and materials science, where improving product quality and efficiency can drive innovation. Achieving optimal control in these systems requires advanced techniques to handle nonlinearity, uncertainty, and high-dimensionality.
In this talk, we will navigate a diverse landscape of approaches encompassing physical model-based, probabilistic, and data-driven solutions, discussing their advantages and drawbacks in the context of real-world applications. We focus on Nonlinear Model Predictive Control (NMPC) with parameter estimation and Bayesian Optimization (BO), comparing their applications in wine fermentation [1] and materials discovery [2]. For wine fermentation, NMPC uses process models to predict and optimize control actions like temperature to lower energy consumption while maintaining product quality. Parameter estimation adapts the model in real-time to account for uncertainties in the fermentation process. In materials design, BO efficiently explores high-dimensional design spaces by building a probabilistic surrogate model, guiding the optimization of material properties with limited experimental data.
We will discuss the strengths and limitations of both methods, their integration with lab automation, and their impact on accelerating innovation and improving process efficiency. This talk offers insights into how model-based and data-driven control strategies can be applied to real-world optimization challenges in food production and materials engineering.
