Galbraith, Shaun Colin (2011) A study of struvite nucleation, crystal growth and aggregation. PhD thesis, James Cook University.

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Abstract

This research focused on the development of a rigorous process model of struvite crystallisation in order to address the issue of nutrient removal and recovery from wastewater. The model is based on a dynamic mass balance, coupled to a population balance, incorporating nucleation, crystal growth and aggregation rate mechanisms. These rate mechanisms were dependent on the thermodynamic driving force known as the saturation index. Non-ideal solution thermodynamics were employed to accurately describe the synthetic solutions employed in the experimental studies, enabling determination of the saturation index. Each rate mechanism had two uncertain parameters, namely the rate coefficient and the order of the power-law relationship. The model incorporated a discretised population balance that enabled the simultaneous coupling of nucleation, growth and aggregation mechanisms to the material balance.

The model equations were solved using gPROMS process simulation software.

A series of seeded, batch experiments covering a saturation index range of 0.75-0.25 was carried out to generate a data set that was used to regress the unknown kinetic rate parameters, thus completing the model description. The regressed parameters are presented below. Where, kᵢ is the rate coefficient for mechanism i and nᵢ is the order of the power-law relationship to saturation index. The parameters apply to atmospheric pressure and the temperature at which the experiments were conducted, being 29.3±0.6°C.

Aggregation: kᵢ = (3.72±0.014)×10⁻⁷ L/min; nᵢ = 5.26±0.004

Crystal Growth: kᵢ = 12.49±0.061 μm/min; nᵢ = 5.06±0.005

Nucleation: kᵢ = (8.50±0.076)×10⁷ 1/L.min; nᵢ = 1.68±0.014

A number of innovations and novel contributions resulted from this work. Innovations:

• Application of rigorous process modelling to the issue of nutrient recovery, leading to;

•• embedded rigorous thermodynamics within a process modelling framework

•• improved understanding of the dynamic performance of nutrient recovery systems, based on struvite precipitation

•• a better understanding of the interplay of the mechanisms of nucleation, crystal growth and aggregation

•• performance assessment of nutrient recovery

• A framework is now in place to refine the model, through the addition of extra experimental data and/or to consider additional effects, such as hydrodynamics

Novelty:

• A hybridisation of the two-term and three-term discretised growth rate equation used in the DPB (Discretised Population Balance), leading to;

•• a solution method for a dynamic MSMPR (Mixed Suspension Mixed Product Removal) crystalliser that conserves particle volume without requiring an adjustable discretisation scheme

•• a description of the true nucleation rate, removing the need to guess a source function.

This research provides a modelling framework for designing struvite crystallisation systems from the position of understanding the kinetic mechanisms responsible for producing the crystal product. Using this approach will result in a better understanding of currently employed struvite crystallisers so that improvements can be made. More importantly, new designs can be tested and optimised through process modelling, instead of through the construction and operation of costly pilot plants.

Item ID:	39332
Item Type:	Thesis (PhD)
Keywords:	aggregates; aggregation; crystal growth; nucleation; nutrient recovery; parameter regression; process modeling; process modelling; struvite; waste water; wastewater
Date Deposited:	05 Aug 2015 23:58
FoR Codes:	09 ENGINEERING > 0904 Chemical Engineering > 090409 Wastewater Treatment Processes @ 34% 09 ENGINEERING > 0907 Environmental Engineering > 090701 Environmental Engineering Design @ 33% 09 ENGINEERING > 0915 Interdisciplinary Engineering > 091506 Nuclear Engineering (incl Fuel Enrichment and Waste Processing and Storage) @ 33%
SEO Codes:	96 ENVIRONMENT > 9699 Other Environment > 969999 Environment not elsewhere classified @ 100%
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