Green Chemicals Production: Integration of ProcessOptimization, Life Cycle Assessment and Artificial Intelligence

The transition toward low-carbon and resource-efficient chemical manufacturing is accelerating the development of green chemicals based on renewable energy, alternative feedstocks, carbon utilization, and biomass conversion. However, their large-scale deployment involves complex interactions among process performance, economic feasibility, energy requirements, and life-cycle environmental impacts, requiring integrated methodologies for sustainable decision-making. This review provides a comprehensive analysis of green chemical production through the combined perspectives of process optimization, Life Cycle Assessment (LCA), and Artificial Intelligence (AI). Representative pathways for green hydrogen, ammonia, urea, methanol, sustainable fuels, and biomass-derived chemicals are examined together with their technological, economic, and environmental characteristics. The roles of optimization in identifying favorable design and operating conditions, LCA in quantifying multicategory environmental impacts, and AI in prediction, surrogate modeling, monitoring, and decision support are critically analyzed. Recent studies integrating these methodologies demonstrate the potential of AI-based models to accelerate computationally intensive evaluations and multi-objective optimization to systematically explore technical, economic, and environmental trade-offs. Nevertheless, fully integrated frameworks combining process modeling, multicategory LCA, optimization, and AI remain limited. Future research should therefore advance toward dynamic, electrified, and intelligent production systems integrating renewable energy variability, storage, dynamic optimization, time-resolved LCA, AI-assisted prediction, digital twins, and real-time sustainability assessment, supporting flexible and sustainable chemical production aligned with Industry 5.0.