(697e) Monitoring Catalysts Synthesis Using Real-Time Emission Spectroscopy and Advanced Machine Learning Models
AIChE Annual Meeting
2022 Annual Meeting
Materials Engineering and Sciences Division
Data-Driven/Machine Learning-Enabled Design for Nanocomposites
Friday, November 18, 2022 - 9:20am to 9:40am
In this work, a series of Pd/CexZryMnz (where x, y, z = 0,1,2) catalysts were synthesized via FSP. Their LIBS spectra were analyzed using the linear-supervised vector classifier (LSVC) and polynomial-supervised vector regressor (PSVR) to predict phase presence, lattice constant, and oxygen vacancy percentage (OV%). The crystalline structure, lattice constant, and OV% were determined by X-ray Diffraction (XRD) and Raman spectroscopy. Samples can present five different crystal structures, including cubic CeO2, tetragonal ZrO2, tetragonal Mn3O4, tetragonal Î±-MnO2, and tetragonal Î²-MnO2. An average accuracy can reach above 85% over the LSVC algorithm. For lattice constant prediction, the cross-validated R2 and RMSE over the PSVR algorithm are calculated as 0.72 and 0.0462, respectively. For OV% prediction, the PSVR algorithm can lead to the cross-validated R2 and RMSE of 0.71 and 6.924%, respectively. Considering the complexity of LIBS spectra and the small dataset size of 76 samples, both LSVC and PSVR models can reasonably predict phases, lattice contact, and OV% from LIBS patterns. The models can successfully predict the chemical properties of same-type compounds without requiring further post-characterization, which can speed up the discovery of novel functional catalysts.