Survey of schools of different education levels (primary, intermediate and secondary) in Kuwait showed an average greywater generation rate of 7.3 L/p/d and varied in the range of 2.9-16 l/p/d, reflecting the school level of education (i.e. student age). The highest rates were observed for primary schools while the lowest rates were observed in secondary schools where students are more mature and use the water more wisely. The greywater characteristics indicated waste with low chemical oxygen demand (COD) and 5-day biochemical oxygen demand (BOD5) values but relatively high solids, conductivity, and sodium content due to excessive use of hand soap. Total coliform values ranged between 89 and 352 most probable number (MPN)/mL with an average of 196 MPN/mL while no fecal coliform values were detected. Greywater collected from schools is classified as light greywater and contains much lower levels of organic matter and nutrients compared to residential greywater and domestic wastewater. It is suitable for non-potable reuse after minimal treatment since microbial contamination may pose a serious threat to health if greywater comes into contact with humans. It also provides a good opportunity for reuse in toilet flushing since it can be easily collected from wash sinks and fountains, as major sources, and recycled.

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The measurement of the wastewater BOD5 level requires five days, and the use of a prediction model to estimate BOD­5 saves time and enables the adoption of an online control system. This study investigates the application of artificial neural networks (ANNs) in predicting the influent BOD5 concentration and the performance of WWTPs. The WWTP performance was defined in terms of the COD, BOD, and TSS concentrations in the effluent. Sensitivity analysis was performed to identify the best-performing ANN network structure and configuration. The results showed that the ANN model developed to predict the BOD concentration performed the best among the three outputs. The top-performing ANN models yielded R2 values of 0.752, 0.612, and 0.631 for the prediction of the BOD, COD, and TSS concentrations, respectively. The optimal performing models were obtained (three inputs – one output), which indicated that the influent temperature and conductivity greatly affect the WWTP performance as inputs in all models. The developed prediction model for the influent BOD5 concentration attained a high accuracy, i.e., R2 = 0.754, which implies that the model is viable as a soft sensor for online control and management systems for WWTPs. Overall, the ANN model provides a simple approach for the prediction of the complex processes of WWTPs.

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