Adding Paper - Applying PySR for biomass pyrolysis prediction

docs/papers.yml

@@ -311,3 +311,20 @@ papers:
     abstract: We study the potential of symbolic regression (SR) to derive compact and precise analytic expressions that can improve the accuracy and simplicity of phenomenological analyses at the Large Hadron Collider (LHC). As a benchmark, we apply SR to equation recovery in quantum electrodynamics (QED), where established analytical results from quantum field theory provide a reliable framework for evaluation. This benchmark serves to validate the performance and reliability of SR before extending its application to structure functions in the Drell-Yan process mediated by virtual photons, which lack analytic representations from first principles. By combining the simplicity of analytic expressions with the predictive power of machine learning techniques, SR offers a useful tool for facilitating phenomenological analyses in high energy physics.
     image: https://raw.githubusercontent.com/MilesCranmer/PySR_Docs/refs/heads/master/images/hep_sr_img.png
     date: 2024-12-10
+  - tittle: Machine learning framework to predict product distribution of lignocellulosic biomass pyrolysis
+    authors:
+      - Leonardo Voltolini (1)
+      - Fernando Arrais Romero Dias Lima (1,3)
+      - Carine Menezes Rebello (3)
+      - Ivaldo Itabaiana Jr. (1)
+      - Idelfonso B.R. Nogueira (3)
+      - Argimiro Resende Secchi (1,2)
+      - Maurício B. de Souza Jr. (1,2)
+    affiliations:
+       1: School of Chemistry, EPQB, Universidade Federal do Rio de Janeiro (UFRJ)
+       2: Chemical Engineering Program, PEQ/COPPE, Universidade Federal do Rio de Janeiro (UFRJ)
+       3: Chemical Engineering Department, Norwegian University of Science and Technology (NTNU)
+    link: https://www.sciencedirect.com/science/article/abs/pii/S0960852425007126
+    abstract: Machine learning methods have become a trend to model distinct chemical processes, as an alternative to complex first-principles models. Given the complexity of biomass pyrolysis mechanisms, these methods offer a promising approach but often face challenges regarding data scarcity and lack of interpretability. This study aims to develop an interpretable framework for modeling biomass pyrolysis using data from fixed-bed lignocellulosic biomass pyrolysis experiments. A mass change basis was proposed to construct machine learning models, including artificial neural network (ANN) and symbolic regression (SR) models. Feature importance was assessed using Shapley Additive Explanations (SHAP) and compared to Partial Least Squares (PLS) regression, with PLS consistently identifying the best features for symbolic regression. Both ANN and SR models showed similar accuracy, achieving coefficient of determination (R$^2$) greater than 0.85 across all phase products in the testing set. Additionally, an uncertainty assessment of SR parameters was conducted to improve model robustness ensuring prediction stability. SR models exhibited superior generalization capacity during extrapolation tests, achieving R$^2$ values above 0.9 for char and gas phases. For oil values exceeding 10 grams, the SR models struggled with generalization. Overall, the proposed framework provides a valuable tool for interpreting and modeling pyrolysis process data, enabling its use in the decision-making process.
+    image: https://github.com/LeoVoltolini/PySR/blob/papers-branch/docs/SR_complete_framework.jpg
+    date: 2025-06-14