Adding Paper - Applying PySR for biomass pyrolysis prediction #994
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Adding a new paper for PySR research collection. This paper implements Symbolic Regression to accurate predict the product distribution of lignocellulosic biomass pyrolysis combined with an uncertainty assessment of Symbolic Regression estimated parameters.
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I'm happy to share our latest paper in the field of biomass feedstock & utilization, published in the Bioresource Technology Journal. In this paper, we constructed a complete framework to accurately predict the product distribution from lignocellulosic biomass pyrolysis. The strategy encompasses artificial neural network (ANN) modeling with subsequent feature importance assessment using SHAP and the linear method of partial least squares (PLS) regression. Additionally, the feature importance assessment was used to reduce features and feed the symbolic regression (SR) algorithm as inputs. PySR was used to derive the symbolic regression equations for the three-phase products (biogas, bio-oil and biochar), and uncertainty assessment was conducted over SR parameters.