Artificial intelligence in sustainable manufacturing and chemical processes

Abstract

Artificial intelligence (AI) is essential for optimizing resource use, reducing waste, and fostering greener manufacturing practices, leading to cost savings and improved environmental performance. This chapter delves into AI-driven process optimization for sustainable manufacturing, focusing on energy efficiency and waste reduction. It also explores green chemical synthesis, sustainable material production, and the integration of AI and internet of things (IoT) in lifecycle assessments, real-time monitoring, and eco-innovation for sustainable design. AI-powered innovations have significantly reduced energy use (10–20%) and waste generation (30–50%), considerably contributing to sustainability. AI models, including convolutional neural networks, graph neural networks, Monte Carlo tree search, and reinforcement learning, help develop eco-friendly chemical processes by predicting environmental impacts, improving designs, and identifying green solvents. Similarly, supervised learning models predict material properties accurately in material design, reducing the need for extensive experimentation. Genetic algorithms have enabled the creation of materials with desired properties using only 0.5% of the initial experimental data. Furthermore, integrating AI into lifecycle assessments enhances sustainability and efficiency, with even small AI-driven improvements leading to energy, fuel, and time savings. For instance, 30% of mobile carriers expect AI to reduce power consumption by 10–15%. Additionally, AI, IoT, and machine learning are transforming sustainable operations, with real-time monitoring becoming central to a greener future. Predictive maintenance improves efficiency, reduces waste, and extends asset life by 20–40%. In Society 5.0, IoT connects people and things, with AI driving new value creation and ensuring the sustainable design of eco-friendly products.