Our ecosystem is at risk by many anthropogenic activities, which include the release of
industrial wastewater effluents laden with toxic heavy metals. There is a long history and a
continued demand for proper evaluation and predication of water quality and management, in
order to protect surrounding water resources and all living species. Undeniably, these pollutants
(heavy metallic ions; HMIs) are a detrimental threat, and must be removed by advanced
treatment technology prior to discharge. One such strategy would be by the process of sorption
(adsorption/ion-exchange), which has advanced among researchers. Zeolites in particular have
attracted researchers’ interests, being a naturally abundant, cost-effective mineral, with high
cation exchange capacity and selectivity of certain metals. They are considered as a strong
candidate for the removal of HMIs, and hold the potential for regeneration, recovery and reuse in
pertinent industrial applications.
This study investigates the sorption process by natural zeolite (clinoptilolite) of HMIs that
are commonly found in industrial wastewater effluent, namely lead (Pb2+), copper (Cu2+), iron
(Fe3+), nickel (Ni2+) and zinc (Zn2+). The HMIs are combined in acidic, synthetic simple-solute
solutions of various (single-, dual-, triple-, multi-) component systems, in a controlled
environment for improved quantification and identification of the important trends; in order to
address existing limitations in multi-component system research. The analytical methodology of
ICP-AES was employed for all quantitative detection and analyses.
The project consists of four phases in the analysis of: (1) the effects of preliminary
parameters and operative conditions (particle size, sorbent-to-sorbate dosage, influent
concentration, contact time, set-temperature, and heat pre-treatment), (2) HMIs component
system combinations and selectivity order, (3) kinetic modelling trends, and (4) the design of a
packed, fixed-bed, dual-column sorption treatment system prototype.
Under the testing conditions, this study demonstrates a strong correlation with the pseudosecond-
order kinetic model in batch-mode analysis, as well as a relationship among the empty
bed contact time, breakthrough capacity, and usage rate in continuous-mode investigations. A
key sorption trend among the HMIs selected is well-established in all four phases as
Pb2+>>Fe3+>Cu2+> Zn2+>>Ni2+; providing significant validation of this experimental design. The
system prototype is a platform for the advancement of intelligent process controls. It is envisaged
that this research will provide essential information to the industrial wastewater treatment
industry for the design and implementation of innovative zeolite-based sorption technology.
Keywords: Natural Zeolite; Clinoptilolite; Heavy Metallic Ions; Sorption Capacity;
Adsorption; Ion-Exchange; Removal Efficiency; Operation Parameters; Selectivity; Kinetic
Modelling; Packed Fixed-Bed Columns; ICP-AES; Automated Design; Intelligent Process
Controls Platform.