Researchers have made significant efforts over the past
few decades
to understand adsorption by developing various simple adsorption isotherm
models. However, though many contaminants usually occur as multicomponent
mixtures in nature, multicomponent adsorption isotherms have received
limited attention and remain an area of inadequate research. We have
presented here in a new multicomponent adsorption isotherm model,
named the Jeppu Amrutha Manipal Multicomponent (JAMM) isotherm, that
can alleviate this problem. We first developed the JAMM multicomponent
isotherm using our experimental data sets of arsenic and fluoride
competitive adsorption on activated carbon. We then tested the JAMM
multicomponent isotherm for a case study of cadmium and zinc competitive
adsorption. Next, we further assessed the JAMM isotherm using another
competitive adsorption case study of copper and chromium. Through
extensive validation studies and error analysis, the JAMM isotherm
was able to demonstrate its efficacy in predicting the adsorption
behavior in several multicomponent adsorption systems accurately.
The main advantage of JAMM isotherm over other multicomponent isotherms
is that it utilizes and leverages the single-component adsorption
parameters to simulate multicomponent isotherms. The proposed JAMM
analytical isotherm model furthermore incorporates the interaction
between the components, a mole fraction parameter, and a heterogeneity
index, providing a more comprehensive modeling framework for multicomponent
adsorption. The mole fraction term was introduced for the distribution
of adsorption sites based on the relative number of molecules of each
component. An additional term for interaction coefficient was introduced
for the representation of interactions. During the validation of JAMM
with three experimental case studies with negligible, small, and high
competition systems of adsorbates, impressive predictions were exhibited,
with the average normalized absolute percentage error as 6.05% and
average
R
2
as 0.86, highlighting the model’s
robustness, versatility, and reliability. We propose that the new
JAMM isotherm modeling framework might profoundly help in chemical
engineering, environmental engineering, and materials science applications
by providing a potent tool for analyzing and predicting multicomponent
adsorption systems.