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Waste biorefining processes face significant challenges related to the variability of feedstocks. The supply and composition of multiple feedstocks in these processes can be uncertain, making it difficult to achieve economically feasible and sustainable waste valorization for large-scale production. Here, we introduce a reinforcement learning-based framework that aims to control these uncertainties and improve the efficiency of the process. The framework is tested on an anaerobic digestion process and is found to perform better than traditional control strategies. In the short term, it achieves faster target tracking with increased precision and accuracy, while in the long term, it shows adaptive and robust behavior even under additional seasonal supply variability, meeting downstream demand with high probability. This reinforcement learning-based framework offers a promising and scalable solution to address uncertainty issues in real-world biorefining processes. If implemented, this framework could contribute to sustainable waste management practices globally, making waste biorefining processes more economically viable and environmentally friendly.

Nanoparticle pollution has been shown to affect various organisms. However, the effects of nanoparticles on species interactions, and the role of species traits, such as body size, in modulating these effects, are not well‐understood. We addressed this issue using competing freshwater phytoplankton species exposed to copper oxide nanoparticles. Increasing nanoparticle concentration resulted in decreased phytoplankton species growth rates and community productivity (both abundance and biomass). Importantly, we consistently found that nanoparticles had greater negative effects on species with smaller cell sizes, such that nanoparticle pollution weakened the competitive dominance of smaller species and promoted species diversity. Moreover, nanoparticles reduced the growth rate differences and competitive ability differences of competing species, while having little effect on species niche differences. Consequently, nanoparticle pollution reduced the selection effect on phytoplankton community abundance, but increased the selection effect on community biomass. Our results suggest cell size as a key functional trait to consider when predicting phytoplankton community structure and ecosystem functioning in the face of increasing nanopollution.