Abstract:
In modern engineering, sustainability is a crucial aspect, especially in chemical and biological process systems. This concept has evolved to encompass a holistic approach, emphasizing not only the use of sustainable resources but also the development of systems that are environmentally friendly, smart, and efficient. Such systems aim to minimize waste and optimize resource use. At the heart of this shift is optimal control, a fundamental element in engineering smart, sustainable systems. However, the integration of optimal control technologies into these systems is challenging due to the complexity of managing large-scale, constrained, nonlinear, and interconnected subsystems, particularly under uncertain conditions. In this talk, I will introduce a series of optimal control technologies that contribute to smarter and more efficient systems, enhancing their sustainability. Specifically, I will discuss the development and application of (1) model predictive control and (2) reinforcement learning, which are instrumental in creating systems that use minimal resources and generate less waste. I will also demonstrate how optimal control is pivotal in advancing sustainable food production in urban areas through the integration of renewable energy and efficient resource management. Overall, these advancements in optimal control techniques illustrate their transformative role in shaping the future of intelligent, sustainable systems, underscoring their vital importance in our path toward a more sustainable world.