The Role of AI-Driven Energy Efficiency Solutions in Sustainable U.S. Medicine Manufacturing

Abstract

In 2021, $580 billion was spent on medicine manufacturing in the United States. As a result, medicine manufacturing is one of the most valuable industrial sectors in the United States, creating a process that serves as the gateway for creating pharmaceuticals that treat wide-ranging diseases, from COVID-19 to cancer to prostate difficulties. In this process, compound molecules are generated through a series of complex chemical reactions that typically consume tremendous amounts of energy. At the same time, compound molecules generally have to go through purification processes to remove impurities. The purification processes target the removal of specific impurities while consuming additional energy. Most pharmaceuticals are produced through large-scale continuous manufacturing processes; therefore, improving the energy efficiency of these generic compounds can have a significant impact on overall energy savings. Recently, machine learning algorithms and problem-solving methods, such as constrained optimization, deep reinforcement learning, and generative adversarial networks, have emerged to augment enterprise energy management, process efficiency, and self-consumption of renewable energy, thus facilitating the development of smart factories [1]. Among these algorithms and methods, reinforcement learning in combination with first-principles models provides a powerful framework to proactively produce timely and knowledgeable control actions under sudden market and process constraints. Since time is limited, thus only a representative subset of AI-driven solutions will be presented.