Nuclear energy has undergone a significant transformation over the past decades, driven by technological innovation, shifting safety priorities, and the urgent need to mitigate climate change. This study presents a comprehensive review of the historical evolution, current developments, and future prospects of nuclear energy as a strategic low-carbon resource. A structured literature review was conducted following Kitchenham’s methodology, covering peer-reviewed articles and institutional reports from 2000 to 2025. Key advances examined include the deployment of Small Modular Reactors, Generation IV technologies, and fusion systems, along with progress in safety protocols, waste management, and regulatory frameworks. Comparative environmental data confirm nuclear power’s low life-cycle CO₂ emissions and high energy density relative to other generation sources. However, major challenges remain, including high capital costs, long construction times, complex waste disposal, and issues of public acceptance. The analysis underscores that nuclear energy, while not a standalone solution, is a critical component of a diversified and sustainable energy mix. Its successful integration will depend on adaptive governance, international cooperation, and enhanced social engagement. Overall, the findings support the role of nuclear energy in achieving global decarbonization targets, provided that safety, equity, and environmental responsibility are upheld.

The presence of microplastics in the environment has increased due to anthropogenic activities; it is estimated that 15 million kilograms of plastic waste accumulate in the ocean annually. Pollution permeates every inch of the ocean from microplastics in the food chain to plastic water bottles floating on the surface. This monolith of ocean pollution is made up of all kinds of marine debris and contains 1.8 trillion pieces of plastic, covering an area twice the size of Texas. The objective of this review is to show advances in the study of emerging problems, specifically in the presence of microplastics in water and soil and their potential effects on health. In addition, microplastics have synergy with residual contaminants that exist in the water such as textile waste, organic matter, pathogens, etc. This causes damage to aquatic organisms as it makes nutrient transfer more complex in many of these species. There is a report that estimates that liabilities related to plastic pollution will cost the industry 100,000 million dollars due to lawsuits for damages and losses, of which 20,000 million will occur in the United States. The study of the presence of microplastics in the environment can generate indicators of the current effect to generate public policies that try to control the growth of this pollutant in the environment. It is important to discuss all the routes of generation of microplastics, distribution, and cosmetics involved in fast fashion with glitter and to evaluate the physical, chemical, biological, and toxicological effects on the environment, proposing the path and future to be followed regarding this research topic.

The integration of artificial intelligence (AI) into solar energy systems has emerged as a transformative pathway to enhance efficiency, reliability, and sustainability in renewable energy. This review examines recent advances in AI-driven optimization and integration strategies across photovoltaic and solar thermal technologies with elements of bibliometric analysis to identify trends, methodologies, and research directions. A particular emphasis is placed on machine learning and deep learning techniques applied to solar irradiance forecasting, maximum power point tracking, fault detection, energy management, and predictive maintenance. Unlike earlier reviews that focused on isolated applications, this work highlights the systemic role of AI in enabling smart grids, hybrid systems, and large-scale energy storage integration. The novelty of this contribution lies in mapping the evolution from traditional control methods to intelligent, self-adaptive frameworks that couple physical modeling with data-driven approaches, offering a structured roadmap for future developments. Furthermore, the review identifies challenges such as data scarcity, computational demand, and interpretability of AI models, while outlining opportunities for process intensification, resilience, and techno-economic optimization. By bridging technical progress with implementation prospects, this article provides an updated reference for researchers, policymakers, and industry stakeholders seeking to accelerate the deployment of AI-enhanced solar energy solutions.

Global population growth requires the use of various natural resources to satisfy the basic needs of humanity. Fossil fuels are mainly used to produce electricity, transportation and the artificial air conditioning of habitats. Nevertheless, countries around the world are looking for alternative energy sources due to the decrease in the availability of these fuels and their high environmental impact. The mixture of hydrogen and carbon monoxide (H₂ + CO), commonly called syngas, is a high-value feedstock for various industrial applications. By varying the composition of syngas, especially the H₂/CO molar ratio, it can be used to produce methanol, fuels or synthetic natural gas. However, when this ratio is very low, the separation of this gas usually represents a great problem when making the energy balance, which is why it is proposed to adapt a combustion process in chemical cycles, taking advantage of the energy of this gas, reducing the energy impact of the process. During the present project, mass and energy balances were developed for combustion in chemical cycles, using ilmenite as a carrier, integrating heat exchangers to take advantage of the residual energy at the output of the process, to preheat the inlet current in the regenerator. Here, a comparative was made at different temperatures of the air stream and evaluating the mechanism of the ilmenite when a syngas stream is used as fuel.

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.

Biogas has been identified as a sustainable resource of renewable and clean energy because of its social, economic, and environmental benefits. In this work, the analysis of a biogas combined cycle power plant coupled with a carbon capture and utilization (CCU) technology and an organic Rankine cycle (ORC) was considered. The integrated process was subjected to a multi-objective assessment considering energy, economic, environmental, and safety items. The CCU system was taken to produce syngas as a value-added product, and the use of different working fluids for the ORC, namely, R1234yf, R290, and R717, was also examined. Such working fluids were selected to represent options with varying environmental and inherent safety implications. It was shown that the integration of the CCU and ORC components to the biogas cycle plant can provide significant benefits that include a 48.65 kt/year syngas production, a decrease in carbon capture energy penalty by 33%, and a reduction in e-CO₂ emissions above 80% with respect to the stand-alone power plant. Comparison with conventional technologies also showed important environmental benefits. The analysis of inherent safety showed that the selection of working fluids for the ORC can have a significant impact on the process risk. From the set of working fluids considered in this work, R717 provided the best choice for the integrated system based on its lowest operational risk and the highest electricity production (355 kWe). The multi-objective approach used in this work allowed the quantification of benefits provided by the integration of CCUs and ORCs with respect to the base process within an overall economic, sustainability, and inherent safety assessment.