DoctorLeo Lebanov

(He/Him)

HYTECH ARC Training Centre Research Fellow in Analytical Chemistry

Chemistry

  • HYTECH ARC Training Centre Research Fellow in Analytical Chemistry
    Chemistry
  • University of Tasmania, Australian Centre for Research on Separation Science (ACROSS), Dobson Road, PO Box 845, Sandy Bay, Hobart, Tasmania, 7006, Australia

RESEARCH SUMMARy

Artificial intelligence and machine learning in analytical chemistry, with a focus on advanced data‑driven methods for gas chromatography–mass spectrometry (GC‑MS).
 
Hyphenated analytical techniques, enabling improved characterisation of complex environmental, natural product, and industrial process matrices.
 
Multivariate statistical modelling for extracting meaningful chemical insight from highly complex and convoluted spectroscopic datasets.
 
Process analytical technology (PAT) development, integrating chemometrics, machine learning, and real‑time measurement strategies to support smart manufacturing and process optimisation.

FUNDED PROJECTS

  • GRANT
    On-line monitoring system for Furacell tm process control
    Australian Institute of Mathematics & Science Pty Ltd9 Oct 2019 - 9 Mar 2020
    People funded by this grant:
    • Paull B,
    • Lebanov L
    Project Total: $20,500; The patented Furacell tm process is a continuous production process producing the Green Chemical Cyrene tm and consists of several stages. Current quality control is based on GC-FID analysis of critical steps in the production. Due to human involvement in the sample preparation, and time taken to do the analysis, only a limited number of samples can be analysed within each production shift. This often means that any off-specification product can be only corrected through adjusting the process parameters for the next shift. On-line analysis gives the opportunity to measure analytes directly in the process environment, and to access results in real-time for controlling and operating purposes. In this way, according to the results in different process points, corrections and adjustments to the operating parameters of the monitored process can be don equickly. This has immense importance in an industrial process. Ability to quickly detect factors which produce low-quality product, process parameters can be immediately adjusted. This can save millions of dollars in a commercial scale plant. Funded by: Australian Institute of Mathematics & Science Pty Ltd - APR Intern ($20,500); University of Tasmania.