Optimizing environmental sustainability in process engineering: leveraging quantum neural networks for energy efficient AI

Abstract

The modern process engineering landscape is increasingly reliant on Artificial Intelligence (AI) and Machine Learning (ML) for optimizing complex systems, enhancing predictive maintenance, and enabling autonomous process control [1]. This wave of digitalization is critical for improving efficiency and maintaining a competitive edge. However, it introduces a significant challenge that runs counter to global sustainability mandates. The computational cost associated with training and deploying large-scale AI models is escalating at an unsustainable rate, creating a direct conflict between technological advancement and the decarbonization goals central to "Green IT" and sustainable engineering [1][2][3]. This tension is not merely academic; it represents a strategic risk for industries adopting AI. Data centers, the backbone of modern AI, accounted for approximately 2% of global electricity demand in 2022, a figure projected to grow substantially. Some estimates suggest that US data center consumption alone could exceed 9% by 2030.[4][5] The training of a single large language model can consume millions of kilowatt-hours and emit hundreds of metric tons of $CO_2$, an energy footprint comparable to the annual consumption of thousands of households[5][6] As process industries deploy ever more powerful AI to optimize operations and reduce direct (Scope 1) emissions, they risk simultaneously increasing their indirect (Scope 2) emissions from purchased electricity. This creates a sustainability paradox where operational efficiency is achieved at the expense of a larger environmental footprint.