An Integrated Process Analysis for Producing Glycerol Carbonate from CO2 and Glycerol.

Glycerol carbonate (GC) is one of the most attractive green chemicals involved in several applications such as polymer synthesis, e. g., the production of polyurethanes and polycarbonates. This relevant chemical can be produced, in a green way, using CO2 (from carbon capture) and glycerol (a byproduct from biodiesel manufacturing). Therefore, in this work, a comprehensive analysis of the GC production process is conducted based on the following synthesis route: urea-dimethyl carbonate-GC using carbon dioxide and glycerol as the main raw materials where the synthesis pathway was efficiently integrated using Aspen Plus. A techno-economic analysis was performed in order to estimate the required capital investment and operating cost for the whole GC process, providing insights on individual capital cost requirements for the urea, dimethyl carbonate, and GC production sections. A total capital cost of $192.1 MM, and a total operating cost of $225.7 MM/y were estimated for the process. The total annualized cost was estimated as $1,558 USD/t of GC produced, competitive with current market price.

Solar photovoltaic panel production in Mexico: A novel machine learning approach.

This study examines the potential for widespread solar photovoltaic panel production in Mexico and emphasizes the country's unique qualities that position it as a strong manufacturing candidate in this field. An advanced model based on artificial neural networks has been developed to predict solar photovoltaic panel plant metrics. This model integrates a state-of-the-art non-linear programming framework using Pyomo as well as an innovative optimization and machine learning toolkit library. This approach creates surrogate models for individual photovoltaic plants including production timelines. While this research, conducted through extensive simulations and meticulous computations, unveiled that Latin America has been significantly underrepresented in the production of silicon, wafers, cells, and modules within the global market; it also demonstrates the substantial potential of scaling up photovoltaic panel production in Mexico, leading to significant economic, social, and environmental benefits. By hyperparameter optimization, an outstanding and competitive artificial neural network model has been developed with a coefficient of determination values above 0.99 for all output variables. It has been found that water and energy consumption during PV panel production is remarkable. However, water consumption (33.16 × 10-4 m3/kWh) and the emissions generated (1.12 × 10-6 TonCO2/kWh) during energy production are significantly lower than those of conventional power plants. Notably, the results highlight a positive economic trend, with module production plants generating the highest profits (35.7%) among all production stages, while polycrystalline silicon production plants yield comparatively lower earnings (13.0%). Furthermore, this study underscores a critical factor in the photovoltaic panel production process which is that cell production plants contribute the most to energy consumption (39.7%) due to their intricate multi-stage processes. The blending of Machine Learning and optimization models heralds a new era in resource allocation for a more sustainable renewable energy sector, offering a brighter, greener future.

A systematic approach for assessing water, energy and food security during the COVID-19 pandemic: case study of Mexico.

Assessing the security of the water-energy-food nexus is a topic of great importance, which allows determining the situation of each resource to implement actions for sustainable management of these resources in today's society. For this reason, a systematic procedure is proposed to evaluate the synergies of the water-energy-food nexus in a large region that is divided into subregions that allow considering their interactions. The new procedure considers the availability, accessibility and regional interdependence of resources while annexing economic and social aspects. A composite index called the WEF Global Index is developed, which involves the WEF nexus index and has nine indicators that evaluate the availability, accessibility and regional interdependence of each resource in the water-energy-food nexus. This new index considers the Gross Domestic Product per capita and the involved population. As a case study, the 32 states of Mexico were considered to assess the effects of the COVID-19 pandemic on the economy and the security of the water-energy-food nexus at the state level. For this, the composite index was evaluated in the years 2019 and 2020. The results show that from 2019 to 2020, the value of the global index increased in 13 states, in 21 states the security index of the WEF nexus increased, and in 9 states the GDP per capita index increased. On the other hand, the results indicate that in 11 states there was no improvement in the nexus security index due to the increase in water demand, which considerably affected the water availability indicator. Supplementary Information: The online version contains supplementary material available at 10.1007/s10668-022-02671-2.