Kinetic model for selective cultivation of microfungi in a microscreen process for food processing wastewater treatment and biomass production

The research was aimed at developing a technology to combine the production of useful microfungi with the treatment of wastewater from food processing. A recycle bioreactor equipped with a micro-screen was developed as a wastewater treatment system on a laboratory scale to contain a Rhizopus culture and maintain its dominance under non-aseptic conditions. Competitive growth of bacteria was observed, but this was minimised by manipulation of the solids retention time and the hydraulic retention time. Removal of about 90% of the waste organic material (as BOD) from the wastewater was achieved simultaneously. Since essentially all fungi are retained behind the 100 mum aperture screen, the solids retention time could be controlled by the rate of harvesting. The hydraulic retention time was employed to control the bacterial growth as the bacteria were washed through the screen at a short HRT. A steady state model was developed to determine these two parameters. This model predicts the effluent quality. Experimental work is still needed to determine the growth characteristics of the selected fungal species under optimum conditions (pH and temperature).

An evaluation of different models of water recovery in flotation

Water recovery is one of the key parameters in flotation modelling for the purposes of plant design and process control, as it determines the circulating flow and residence time in the individual process units in the plant and has a significant effect on entrainment and froth recovery. This paper reviews some of the water recovery models available in the literature, including both empirical and fundamental models. The selected models are tested using the data obtained from the experimental work conducted in an Outokumpu 3 m(3) tank cell at the Xstrata Mt Isa copper concentrator. It is found that all the models fit the experimental data reasonably well for a given flotation system. However, the empirical models are either unable to distinguish the effect of different cell operating conditions or required to determine the empirical model parameters to be derived in an existing flotation system. The model developed by [Neethling, SJ., Lee, H.T., Cilliers, J.J., 2003, Simple relationships for predicting the recovery of liquid from flowing foams and froths. Minerals Engineering 16, 1123-1130] is based on fundamental understanding of the froth structure and transfer of the water in the froth. It describes the water recovery as a function of the cell operating conditions and the froth properties which can all be determined on-line. Hence, the fundamental model can be used for process control purposes in practice. By incorporating additional models to relate the air recovery and surface bubble size directly to the cell operating conditions, the fundamental model can also be used for prediction purposes. (C) 2005 Elsevier Ltd. All rights reserved.

Assessing the quality of risk factor survey data: lessons from the WHO MONICA Project

Background and purpose Survey data quality is a combination of the representativeness of the sample, the accuracy and precision of measurements, data processing and management with several subcomponents in each. The purpose of this paper is to show how, in the final risk factor surveys of the WHO MONICA Project, information on data quality were obtained, quantified, and used in the analysis. Methods and results In the WHO MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) Project, the information about the data quality components was documented in retrospective quality assessment reports. On the basis of the documented information and the survey data, the quality of each data component was assessed and summarized using quality scores. The quality scores were used in sensitivity testing of the results both by excluding populations with low quality scores and by weighting the data by its quality scores. Conclusions Detailed documentation of all survey procedures with standardized protocols, training, and quality control are steps towards optimizing data quality. Quantifying data quality is a further step. Methods used in the WHO MONICA Project could be adopted to improve quality in other health surveys.

Quality of service in flexible workflows through process constraints

Workflow technology has delivered effectively for a large class of business processes, providing the requisite control and monitoring functions. At the same time, this technology has been the target of much criticism due to its limited ability to cope with dynamically changing business conditions which require business processes to be adapted frequently, and/or its limited ability to model business processes which cannot be entirely predefined. Requirements indicate the need for generic solutions where a balance between process control and flexibility may be achieved. In this paper we present a framework that allows the workflow to execute on the basis of a partially specified model where the full specification of the model is made at runtime, and may be unique to each instance. This framework is based on the notion of process constraints. Where as process constraints may be specified for any aspect of the workflow, such as structural, temporal, etc. our focus in this paper is on a constraint which allows dynamic selection of activities for inclusion in a given instance. We call these cardinality constraints, and this paper will discuss their specification and validation requirements.