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Hernandez, Borja

Publications and source records attributed to Hernandez, Borja.

2 records
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DOE OSTI2022

Plastic waste upgrade to olefins via mild slurry microwave pyrolysis over solid acids

Chemical upcycling technologies are emerging as the most viable to combat plastic waste accumulation. Among them, catalytic pyrolysis is very promising as it is feedstock agnostic. However, the high energy demand associated with pyrolysis can lead to significant carbon dioxide production. In this work, we demonstrate that coupling microwave heating with suitable solid acid promoters in a slurry reactor coupled with a distillation unit can overcome the energy-related challenge of conventional pyrolysis, by operating at modest temperatures (350–375 °C), enhancing transport, and furnishing high yields of olefins (~88 %) in seconds, with medium-sized olefins (>75 %) composing a significant fraction. Reduced thermal gradients minimize coke formation, further improving the performance. Techno-economic analysis and life cycle assessment indicate the potential of the technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. Furthermore, we discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗