Development of a dynamic model for cleaning ultra filtration membranes fouled by surface water

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Abstract

In this paper, a dynamic model for cleaning ultra filtration membranes fouled by surface water is proposed. A model that captures the dynamics well is valuable for the optimization of the cleaning process. The proposed model is based on component balances and contains three parameters that can be determined by a simple experimental protocol that facilitates the possibility of online adaptation of model parameters at frequent intervals. This may be required when process conditions change and/or water quality varies with time. Experiments were performed to test and validate the model.

Introduction

Ultra filtration is increasingly used as a technology to purify surface water. UF membranes have high selectivity and are economically attractive. A major drawback, however, is the limitation in performance as a result of fouling. Consequently, frequent cleaning of the membrane is necessary. In the short term, fouling is controlled by means of hydraulic cleanings (backwashes), but in the long term, fouling that cannot be removed by means of backwashes builds up, and treatment of the membrane with chemicals is required. It is expected that cleaning costs can be reduced by means of optimization.

The main objective of this paper is the development of a cleaning model that is suitable for (dynamic) optimization of the chemical cleaning procedure. A three parameter model that describes dynamic behavior of a chemical cleaning procedure is proposed. After construction of the model, a protocol for experimental verification of the model is developed and tested. The model parameters are determined by a nonlinear least squares algorithm using experimental runs. The fitted parameters are consequently used to validate the model for additional experimental runs. It was found that the model describes the cleaning process well.

Section snippets

Requirements of the model

Chemical cleaning of membranes is widely studied in a qualitative way [1], [2], [3], [4], [5], [6]. In these studies, the characterization of fouling and the interactions that occur between fouling, cleaning agent and membrane surface are described in detail.

The number of authors who published on the modeling aspects of chemical cleaning of membranes is limited. Bird [7] developed a microscopic cleaning model for the caustic cleaning of ceramic micro-filtration filters fouled by whey

Equipment and material

Fouling and subsequent cleaning experiments were performed with a laboratory scale dead end ultra filtration unit. The membrane used in the experiments is a poly ether sulphone Norit-Xiga RX300 PSU hollow fiber ultra filtration module with a membrane surface of Am=0.07  m2.

The setup consists of the ultra filtration membrane module, a filtration and a backwash pump, flow indicators to monitor fluxes, temperature indicators to correct trans membrane pressure for changes in temperature and

Results

Six experimental cleaning runs were performed at different sodium hydroxide concentrations. Table 1 shows the settings and the effectiveness of the cleaning experiments. Runs 1–4 are used to determine the model parameters, using a nonlinear least squares algorithm. The model parameters were determined to be k=0.93min1, k=2.90min1, nC=2.5×106. The value nC is small, which implies that the cleaning agent is not consumed during the cleaning reaction, which is in agreement with the

Conclusions

A simple cleaning model, based on component balances was proposed. The model predicts the irreversible fouling decay as function of cleaning time and cleaning agent concentration. The model parameters can be determined by a simple experimental protocol, measuring trans membrane pressure, pH and turbidity. The execution of the protocol can be done relatively fast, which facilitates frequent online adaptation of the model parameters, when process conditions and water quality vary with time.

Acknowledgements

The financial support of NWO/STW, Aquacare Europe, Hatenboer-Water, Norit Membrane Technology and Vitens Laboratory & Process Technology is gratefully acknowledged.

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