Model-driven experimental evaluation of struvite nucleation, growth and aggregation kinetics

Area of Science:

• Environmental Science
• Chemical Engineering
• Materials Science

Background:

• Nutrient stewardship is a growing global concern, necessitating advancements in nutrient recovery.
• Wastewater treatment, environmental protection, agriculture, and food security are significantly impacted by nutrient recovery.
• Struvite precipitation is a promising pathway for nutrient recovery with commercial potential.

Purpose of the Study:

• To develop a dynamic modelling framework for precipitation-based nutrient recovery systems.
• To incorporate non-ideal solution thermodynamics, mass balance, and population balance for particle development tracking.
• To enable direct regression of kinetic parameters for nucleation, growth, and aggregation.

Main Methods:

• A modelling framework was developed integrating thermodynamics, dynamic mass balance, and dynamic population balance.
• The model considers crystal nucleation, growth, and aggregation mechanisms.
• Kinetic parameters for struvite precipitation were regressed from 14 laboratory batch experiments using synthetic solutions.

Main Results:

• The model successfully describes dynamic responses of solution pH, particle size distribution, and aqueous magnesium concentration.
• Regressed power law kinetic parameters for nucleation, crystal growth, and aggregation were obtained with high repeatability.
• The novel population balance approach allowed direct regression of nucleation rate parameters.

Conclusions:

• The developed modelling framework accurately simulates struvite precipitation dynamics.
• This framework enhances understanding and confidence in designing and optimizing large-scale nutrient recovery processes.
• The approach can be extended to other complex nutrient recovery systems from various effluents.