Greg Keir

Evaluating membrane technology for clarification of sugarcane juice

The sugar industry needs to find efficient methods in clarifying the raw sugarcane juice in order to improve the quality of the clarified juice and to reduce or eliminate the usage of chemicals (lime). Conventional clarifiers use heavy equipment which lead to high operating costs and associated environmental problems. In sugar mills, ensuring the production of juice of consistently high clarity and low colour through the clarification process is a challenging task. The variations in the incoming juice characteristics due to differences in cane variety, soil and growing conditions, weather patterns and season make this task even more challenging. Membrane filtration promises superior quality juice with better clarity, much lower viscosity and noticeable colour removal. Ultrafiltration of clarified sugarcane juice can be done through spiral wound or flat sheet filtration systems using polymeric membranes or tubular filtration systems using ceramic membranes. This review evaluates the applications of membrane technology in sugar industry all over the world and the need for it in the Australian sugar industry. This is an important first step to identify the appropriate types and applications of membranes.

A review of computational fluid dynamics applications in pressure-driven membrane filtration

Ongoing advances in computational performance and numerics have led to computational fluid dynamics (CFD) becoming a ubiquitous modelling tool. However, CFD methods have only been adopted to simulate pressure-driven membrane filtration systems relatively recently. This paper reviews various approaches to describing the behaviour of these systems using CFD, beginning with the hydrodynamics of membrane channels, including discussion of laminar, turbulent, and transition flow regimes, with reference to the effects of osmotic pressure, concentration polarisation, and cake formation. The use of CFD in describing mass transfer through the membrane itself is then discussed, followed by some concluding comments on commercial membrane simulation packages and future research directions in membrane CFD.

Removal of ametryn through nanofiltration and reverse osmosis

This study focused on the performance of nanofiltration (NF) and reverse osmosis (RO) in the removal of ametryn. Here, we investigated the effects of the applied pressure and ionic conditions on the removal of ametryn from water. It was found that the adsorption of ametryn onto NF membrane to decrease with the increase in ionic strength. Increased interaction between the NaCl electrolyte and the membrane surface is considered as the reason for the reduction in the adsorption. However, adsorption of ametryn onto the RO membrane did not show any trend. The removal of ametryn by NF and RO found to increase with the increase in the ionic strength. Retention of electrolyte salt (NaCl) on the membrane surface tends to decrease the membrane pore size which in turn increases the removal of ametryn at higher ionic strengths. It was found that up to 92% of ametryn could be removed using RO.

Stochastic modelling of groundwater extractions over a data-sparse region of Australia

Setting limit on groundwater extractions is important to ensure sustainable groundwater management. Lack of extraction data can affect interpretations of historical pressure changes, predictions of future impacts, accuracy of groundwater model calibration, and identification of sustainable management options. Yet, many groundwater extractions are unmetered. Therefore, there is a need for models that estimate extraction rates and quantify model outputs uncertainties arising due to a lack of data. This paper develops such a model within the Generalized Linear Modeling (GLM) framework, using a case study of stock and domestic (SD) extractions in the Surat Cumulative Management Area, a predominantly cattle farming region in eastern Australia. Various types of extraction observations were used, ranging from metering to analytically-derived estimates. GLMs were developed and applied to estimate the property-level extraction amounts, where observation types were weighted by perceived relative accuracy, and well usage status. The primary variables found to affect property-level extraction rates were: yearly average temperature and rainfall, pasture, property area, and number of active wells; while variables most affecting well usage were well water electrical conductivity, spatial coordinates, and well age. Results were compared with analytical estimates of property-level extraction, illustrating uncertainties and potential biases across 20 hydrogeological units. Spatial patterns of mean extraction rates (and standard deviations) are presented. It is concluded that GLMs are well suited to the problem of extraction rate estimation and uncertainty analysis, and are ideal when model verification is supported by measurement of a random sample of properties.