Millions of people in rural South Asia are exposed to high levels of arsenic through groundwater used for drinking. Many deployed arsenic remediation technologies quickly fail because they are not maintained, repaired, accepted, or affordable. It is therefore imperative that arsenic remediation technologies be evaluated for their ability to perform within a sustainable and scalable business model that addresses these challenges. We present field trial results of a 600 L Electro-Chemical Arsenic Remediation (ECAR) reactor operating over 3.5 months in West Bengal. These results are evaluated through the lens of a community scale micro-utility business model as a potential sustainable and scalable safe water solution for rural communities in South Asia. We demonstrate ECAR's ability to consistently reduce arsenic concentrations of ~266 μg/L to <5 μg/L in real groundwater, simultaneously meeting the international standards for iron and aluminum in drinking water. ECAR operating costs (amortized capital plus consumables) are estimated as $0.83-$1.04/m(3) under realistic conditions. We discuss the implications of these results against the constraints of a sustainable and scalable business model to argue that ECAR is a promising technology to help provide a clean water solution in arsenic-affected areas of South Asia.

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Slaughterhouse wastewater contains diluted blood, protein, fat, and suspended solids, as a result the organic and nutrient concentration in this wastewater is vary high and the residues are partially solubilized, leading to a highly contaminating effect in riverbeds and other water bodies if the same is let off untreated. The performance of a laboratory-scale Sequencing Batch Reactor (SBR) has been investigated in aerobic-anoxic sequential mode for simultaneous removal of organic carbon and nitrogen from slaughterhouse wastewater. The reactor was operated under three different variations of aerobic-anoxic sequence, namely, (4+4), (5+3), and (3+5) hr. of total react period with two different sets of influent soluble COD (SCOD) and ammonia nitrogen (NH4 +-N) level 1000 ± 50 mg/L, and 90 ± 10 mg/L, 1000 ± 50 mg/L and 180 ± 10 mg/L, respectively. It was observed that from 86 to 95% of SCOD removal is accomplished at the end of 8.0 hr of total react period. In case of (4+4) aerobic-anoxic operating cycle, a reasonable degree of nitrification 90.12 and 74.75% corresponding to initial NH4 +-N value of 96.58 and 176.85 mg/L, respectively, were achieved. The biokinetic coefficients (k, K s, Y, k d) were also determined for performance evaluation of SBR for scaling full-scale reactor in future operation.

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The present paper deals with treatment of slaughterhouse wastewater by conducting a laboratory scale sequencing batch reactor (SBR) with different input characterized samples, and the experimental results are explored for the formulation of feedforward backpropagation artificial neural network (ANN) to predict combined removal efficiency of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N). The reactor was operated under three different combinations of aerobic-anoxic sequence, namely, (4 + 4), (5 + 3), and (5 + 4) hour of total react period with influent COD andNH4+-N level of 2000 ± 100 mg/L and 120 ± 10 mg/L, respectively. ANN modeling was carried out using neural network tools, with Levenberg-Marquardt training algorithm. Various trials were examined for training of three types of ANN models (Models “A,” “B,” and “C”) using number of neurons in the hidden layer varying from 2 to 30. All together 29, data sets were used for each three types of model for which 15 data sets were used for training, 7 data sets for validation, and 7 data sets for testing. The experimental results were used for testing and validation of three types of ANN models. Three ANN models (Models “A,” “B,” and “C”) were trained and tested reasonably well to predict COD andNH4+-N removal efficiently with 3.33% experimental error.