A Priori Calibration of Transient Kinetics Data via Machine Learning
This work addresses the need for more reliable and automated preprocessing in chemical kinetics analysis, offering a domain-specific improvement over traditional methods.
The paper tackles the problem of preprocessing noisy transient kinetics data from TAP reactors, which traditionally requires user discretion and prior experiments, by introducing a machine learning method that automatically calibrates the signal without needing prior calibration experiments or user input.
The temporal analysis of products reactor provides a vast amount of transient kinetic information that may be used to describe a variety of chemical features including the residence time distribution, kinetic coefficients, number of active sites, and the reaction mechanism. However, as with any measurement device, the TAP reactor signal is convoluted with noise. To reduce the uncertainty of the kinetic measurement and any derived parameters or mechanisms, proper preprocessing must be performed prior to any advanced analysis. This preprocessing consists of baseline correction, i.e., a shift in the voltage response, and calibration, i.e., a scaling of the flux response based on prior experiments. The current methodology of preprocessing requires significant user discretion and reliance on previous experiments that may drift over time. Herein we use machine learning techniques combined with physical constraints to convert the raw instrument signal to chemical information. As such, the proposed methodology demonstrates clear benefits over the traditional preprocessing in the calibration of the inert and feed mixture products without need of prior calibration experiments or heuristic input from the user.