Optimization has played a fundamental role in the evolution of chemical engineering, enabling systematic decision-making under technical, economic, and environmental constraints. This review presents a structured and comparative analysis of the historical development and current state of optimization methodologies applied to chemical engineering, covering the transition from early linear and nonlinear programming approaches to advanced data-driven and artificial intelligence-based frameworks. A systematic literature review was conducted following the PRISMA guidelines, through which a total of 101 articles were retained for analysis. The results indicate that mixed-integer programming and decomposition-based methods remain widely adopted for structured industrial problems, while metaheuristic and hybrid data-driven approaches have experienced significant growth in recent years. In particular, a clear trend toward the integration of machine learning and surrogate modeling techniques is observed, driven by the need to address large-scale, non-convex, and highly nonlinear systems. The analysis reveals a clear methodological shift from classical linear optimization frameworks toward hybrid optimization strategies capable of addressing large-scale, non-convex, and highly nonlinear problems. Finally, current challenges and future research directions are identified, emphasizing the need for robust hybrid approaches that combine mathematical programming and intelligent algorithms to effectively manage complexity in next-generation chemical systems.

The global energy transition is driving power systems toward lower-carbon electricity generation, requiring sustainability assessments that consider environmental burdens beyond direct carbon emissions. This study evaluates Mexico’s electricity system using life cycle assessment, resource analysis, and energy sustainability indices. The main novelty of this study is the development of four energy sustainability indices derived from EI99H damage results: the Index of Environmental Change per Energy Unit, Relative Environmental Change Index, Per Capita Environmental Impact, and Environmental Intensity Metric. These indices capture temporal environmental change, generation-related variation, population-related burden, and environmental impact per unit of electricity. Results show improvements in fuel oil, water, and biomass performance between 2013 and 2023, whereas natural gas and coal impacts increased. Mexico exhibits a lower per capita environmental burden than Germany and Spain, while France shows the lowest value, largely due to its nuclear-based electricity mix. Human Health damage is 55% higher than Ecosystem Quality, mainly due to fossil fuel combustion. Hydroelectric generation shows substantial water demand, while solar and wind have negligible requirements. Rising natural gas costs constrain competitiveness, whereas renewables maintain low operating costs.

The rapid expansion of artificial intelligence has intensified the demand for high-performance data centers, leading to unprecedented pressures on energy, water, and material resources. This critical review examines the emerging challenges associated with resource management in AI-driven data centers by focusing on the interplay between computational growth and environmental constraints. The analysis integrates recent advances in energy efficiency, cooling technologies, and hardware design while highlighting the increasing water footprint of thermal management systems and the material implications linked to semiconductor manufacturing and infrastructure scaling. Particular attention is given not only to the trade-offs between performance optimization and sustainability but also to the limitations of current metrics used to assess resource efficiency. The review identifies key gaps in the literature, including the lack of integrated frameworks that simultaneously address energy, water, and material flows. Finally, this review provides insights into pathways for a more sustainable AI infrastructure by synthesizing present-day knowledge, critically evaluating existing strategies, and emphasizing the need for systemic approaches that align technological innovation with resource conservation and long-term environmental resilience.