This work reports
a novel and quick method to estimate
the surface
area of porous materials. Conventionally, surface area measurement
requires the BET method/N2 adsorption experiment which
is time-consuming. In this work, we developed a method based on machine
learning (ML) and the adsorption of a conductive dye on porous materials.
The rate and quantity of dye adsorption, which is characterized by
dynamic measurement of conductivity, provide an indirect measure of
surface area and zeta potential. An ML-based soft sensor is developed
to relate the measured conductivity profiles with surface area and
zeta potential. A phenomenological model on dye adsorption is also
developed, validated, and used to augment experimental data for training
the soft sensor. The developed method was tested for porous silica
particles with a range of surface areas (250–1100 m2/g) and zeta potential (−17 mV: −29 mV). The developed
soft sensor was able to estimate the surface area and zeta potential
quite well. The developed approach and method reduce overall measurement
time for surface area from several hours to a few minutes. The method
can potentially be implemented in continuous plants producing porous
materials like silica.