Muttucumaru Sivakumar

Fluoride removal by a continuous flow electrocoagulation reactor

Long-term consumption of water containing excessive fluoride can lead to fluorosis of the teeth and bones. Electrocoagulation (EC) is an electrochemical technique, in which a variety of unwanted dissolved particles and suspended matter can be effectively removed from an aqueous solution by electrolysis. Continuous flow experiments with monopolar aluminium electrodes for fluoride removal were undertaken to investigate the effects of the different parameters such as: current density (12.5-50A/m(2)), flow rate (150-400 mL/min), initial pH (4-8), and initial fluoride concentration (5-25mg/L). The highest treatment efficiency was obtained for the largest current and the removal efficiency was found to be dependent on the current density, the flow rate and the initial fluoride concentration when the final pH ranged between 6 and 8. The composition of the sludge produced was analysed using the X-ray diffraction (XRD) spectrum. The strong presence of the aluminium hydroxide [Al(OH)(3)] in the above pH range, which maximizes the formation of aluminium fluoride hydroxide complex [Al(n)F(m)(OH)(3n-m)], is the main reason for defluoridation by electrocoagulation. The results obtained showed that the continuous flow electrocoagulation technology is an effective process for defluoridation of potable water supplies and could also be utilized for the defluoridation of industrial wastewater.

Effect of mixed liquor pH on the removal of trace organic contaminants in a membrane bioreactor

Experiments were conducted over approximately 7 months to investigate the effects of mixed liquor pH (between pH 5 and 9) on the removal of trace organics by a submerged MBR system. Removal efficiencies of ionisable trace organics (sulfamethoxazole, ibuprofen, ketoprofen, and diclofenac) were strongly pH dependent. However, the underlying removal mechanisms are different for ionisable and non-ionisable compounds. High removal efficiencies of these ionisable trace organics at pH 5 could possibly be attributed to their speciation behaviour. At this pH, these compounds exist predominantly in their hydrophobic form. Consequently, they could readily adsorb to the activated sludge, resulting in higher removal efficiency in comparison to under less acidic conditions in the reactor. Removal efficiencies of the two non-ionisable compounds bisphenol A and carbamazepine were relatively independent of the mixed liquor pH. Results reported here suggest an apparent connection between physicochemical properties of the compounds and their removal efficiencies by MBRs.

Prediction of urban stormwater quality using artificial neural networks

There are a vast number of complex, interrelated processes influencing urban stormwater quality. However, the lack of measured fundamental variables prevents the construction of process-based models. Furthermore, hybrid models such as the buildup-washoff models are generally crude simplifications of reality. This has created the need for statistical models, capable of making use of the readily accessible data. In this paper, artificial neural networks (ANN) were used to predict stormwater quality at urbanized catchments located throughout the United States. Five constituents were analysed: chemical oxygen demand (COD), lead (Pb), suspended solids (SS), total Kjeldhal nitrogen (TKN) and total phosphorus (TP). Multiple linear regression equations were initially constructed upon logarithmically transformed data. Input variables were primarily selected using a stepwise regression approach, combined with process knowledge. Variables found significant in the regression models were then used to construct ANN models. Other important network parameters such as learning rate, momentum and the number of hidden nodes were optimized using a trial and error approach. The final ANN models were then compared with the multiple linear regression models. In summary, ANN models were generally less accurate than the regression models and more time consuming to construct. This infers that ANN models are not more applicable than regression models when predicting urban stormwater quality.

Denitrification using a monopolar electrocoagulation/flotation (ECF) process

Nitrate levels are limited due to health concerns in potable water. Nitrate is a common contaminant in water supplies, and especially prevalent in surface water supplies and shallow wells. Nitrate is a stable and highly soluble ion with low potential for precipitation or adsorption. These properties make it difficult to remove using conventional water treatment methods. A laboratory batch electrocoagulation/flotation (ECF) reactor was designed to investigate the effects of different parameters such as electrolysis time, electrolyte pH, initial nitrate concentration, and current rate on the nitrate removal efficiency. The optimum nitrate removal was observed at a pH range of between 9 and 11. It appeared that the nitrate removal rate was 93% when the initial nitrate concentration and electrolysis time respectively were 100 mg L(-1)-NO(3)(-) and 40 min. The results showed a linear relationship between the electrolysis time for total nitrate removal and the initial nitrate concentration. It is concluded that the electrocoagulation technology for denitrification can be an effective preliminary process when the ammonia byproduct must be effectively removed by the treatment facilities.

Wastewater and stormwater minimisation in a coal mine

Abstract This paper presents a case study on the application of cleaner production principles in the mining industry. The water balance prepared for the case study showed that less than 20% of the wastewater generated by the colliery is discharged off-site. The remaining 80% of the wastewater is recycled back into the colliery. Modeling of the stormwater system showed that 75% of the clean runoff becomes contaminated through poor management practices. It was also found that the present system of stormwater management causes the process wastewater management system to fail in wet weather. Improved process and stormwater management systems are proposed. Relatively simple alterations to the operation of the coal wash filtration dams are expected to reduce the periods of inefficient operation of these dams by 95% and the pumping cost by 30%. The use of stormwater diversion channels and retention basins reduces the overflow volumes of the process wastewater treatment dams in 5 year average recurrence interval (ARI) storms by 100%. The paper also includes several recommendations for reducing the production of process wastewater at source and off-site disposal of wastewater.

Membrane bioreactor technology for decentralised wastewater treatment and reuse

There is a growing interest in utilising non-traditional water resources by means of water reclamation and water recycling for long term sustainability. Amongst the many treatment alternatives, membrane bioreactors (MBRs) have been seen as an effective technology capable of transforming various types of wastewater into high-quality effluent exceeding most discharge requirements and suitable for a variety of reuse applications. To date MBRs are largely restricted to centralised large scale applications, with the most common capacity of 200 ML per day or above. The aim of this paper is to review and discuss the potential and limitations of MBRs for small scale applications. Both technical and economic considerations will be delineated with respect to the future water outlook in Australia. Particular attention is also given to the impact of MBR technology on the removal of micropollutants that are of significant concern in water recycling.

A multiple-criteria decision-making model for evaluating sustainability of business enterprises

This paper proposes an analytical approach to evaluating how businesses respond to the global call for sustainable development. A model is developed using the multiple-criteria decision-making approach. The analytic hierarchy process and the data envelopment analysis, when used conjunctively, serve as a framework for evaluation of sustainability. The use of the model is demonstrated in several business areas illustrated through case studies. The model can be adapted to any evaluation problem concerning sustainability of business enterprises.

Comparison of artificial neural network and regression models in the prediction of urban stormwater quality.

Urban stormwater quality is influenced by many interrelated processes. However, the site-specific nature of these complex processes makes stormwater quality difficult to predict using physically based process models. This has resulted in the need for more empirical techniques. In this study, artificial neural networks (ANN) were used to model urban stormwater quality. A total of 5 different constituents were analyzed-chemical oxygen demand, lead, suspended solids, total Kjeldahl nitrogen, and total phosphorus. Input variables were selected using stepwise linear regression models, calibrated on logarithmically transformed data. Artificial neural networks models were then developed and compared with the regression models. The results from the analyses indicate that multiple linear regression models were more applicable for predicting urban stormwater quality than ANN models.

Optimum number of storms required to derive site mean concentrations at urban catchments

A reiterative analysis was applied to determine the optimum number of storms required to generate site mean concentrations (SMC), using total phosphorus data from 17 urban catchments. For each analysed catchment, event mean concentration data were randomly placed into various calibration set sizes, ranging from 1 to N (where N was equal to the total number of storms available at the given catchment). Geometric mean estimates of SMCs associated with each calibration set size were then calculated, and verified using all available data from the catchment of interest. This process was repeated 10,000 times for each catchment. Average errors associated with each sample size were then plotted and used to estimate the optimum number of storms required to derive SMCs at each catchment. The optimum was derived by evaluating the balance between cost and uncertainty, whereby the minimum number of storms producing a relatively accurate estimate of SMC was accepted. Overall, it was found that between five and seven storm events were sufficient. In addition, it was deduced that sampling only six storm events would be approximately 40% cheaper than sampling 12 events.

Prediction of nutrient concentrations in urban storm water.

Excessive quantities of nutrients in urban storm-water runoff can lead to problems such as eutrophication in receiving water bodies. Accurate process based models are difficult to construct due to the vast array of complex phenomena affecting nutrient concentrations. Furthermore, it is often impossible to successfully apply process based models to catchments with limited or no sampling. This has created the need for simple models capable of predicting nutrient concentrations at unmonitored catchments. In this study, simple statistical models were constructed to predict six different types of nutrients present in urban storm-water runoff: ammonia (NH 3 ), nitrogen oxides (NO x ), total Kjeldahl nitrogen, total nitrogen, dissolved phosphorus, and total phosphorus. Models were constructed using data from the United States, collected as a part of the Nationwide Urban Stormwater Program more than two decades ago. Comparison between the models revealed that regression models were generally more applicable than the simple estimates of mean concentration from homogeneous subsets, separated based upon land use or the metropolitan area. Regression models were generally more accurate and provided valuable insight into the most important processes influencing nutrient concentrations in urban storm-water runoff.