Chemometrics

The research contributions include the development of novel methods for targeted metabolite profiling, multimodal analysis of spectroscopic data, and data-driven prediction of crude oil properties, etc.

Relevant publications:

S. Yang, J. Moreira, Z. Li. Predicting Crude Oil Properties Using Fourier-Transform Infrared Spectroscopy (FTIR) and Data-Driven Methods. Digital Chemical Engineering. 2022, 3, 100031.

A. Puliyanda, K. Srinivasan, Z. Li, V. Prasad. Benchmarking chemical neural ordinary differential equations to obtain reaction network-constrained kinetic models from spectroscopic data. Engineering Applications of Artificial Intelligence. 2023, accepted.

A. Puliyanda, Z. Li, V. Prasad Real-time monitoring of reaction mechanisms from spectroscopic data using hidden semi-Markov models for mode identification. Journal of Process Control. 2022, 117, 188-205.

A. Puliyanda, K. Sivaramakrishnan, Z. Li, A. de Klerk, V. Prasad Structure-preserving joint non-negative tensor factorization to identify reaction pathways using Bayesian networks. Journal of Chemical Information and Modeling. 2021, 61, 5747-5762.

A. Puliyanda, K. Sivaramakrishnan, Z. Li, A. de Klerk, V. Prasad. Data Fusion by Joint Non-negative Matrix Factorization for Hypothesizing Pseudo-chemistry Using Bayesian Networks. Reaction Chemistry & Engineering. 2020, 5, 1719-1737.

F. Xu, M. Mantri, Z. Li. A Novel Algorithm for Targeted Metabolite Profiling using NMR Spectrum. 6th International Symposium on Advanced Control of Industrial Processes, Taipei, 2017.