Volume 4 Issue 4

Authors: De Avik; K. De Asim

Abstract: The degradation of phenolic compounds via hydrogen peroxide (H2O2) was investigated using laboratory scale batch reactors. A bench scale continuously stirred tank reactor (CSTR) was used for characterizing the effects of varying experimental concentrations on the degradation rate of phenol as well as 2- and 4-chlorophenols. Results showed that the conversion attained within 15 minutes of reaction accounted for approximately 90% of the total conversion. The drops in pH for phenol, 2-chlorophenol and 4-chlorophenol were 6.6 to 6.38, 5.8 to 4.45 and 5.7 to 4.40 respectively. Concentration of H2O2 measured at different time intervals remained almost constant during reaction. The optimum conversion of substrates could be achieved by maintaining pH at 6.0, 4.55 and 4.5 for phenol, 2-chlorophenol and 4-chlorophenol, respectively. The conversion was amplified with increasing initial concentration of substrates and this phenomenon was observed only when the substrate to H2O2 molar ratio was kept constant. The total conversion under similar reaction conditions was comparable for all three substrates. Though the conversion was in the order of 4-chlorophenol > 2-chlorophenol > phenol, the conversion varied only within 30% for substrate initial concentration of 500 mg/L. The pH after degradation was such that it did not require neutralization for its disposal in any body of water.

Keywords: Conversion; COD; Phenol; 2-chlorophenol; 4-chlorophenol; H2O2 Oxidation


Authors: Edward McBean; Richard Harvey

Abstract: Aging sewer pipes inevitably deteriorate to a point where raw, untreated wastewater leaks out of the pipe and into the surrounding soil and nearby sources of groundwater. Municipalities can utilize closed-circuit television (CCTV) inspection technology to identify individual pipes within a sewer network in bad structural condition. Although CCTV inspections provide essential information on pipe condition, they are expensive and often limited to small portions of an entire sewer network. Consequently, any threat to the environment posed by uninspected pipes remains unknown. Predictive analytics can leverage existing inspection datasets so that reliable predictions of condition are available for individual pipes not yet inspected. The predictive capability of the “random forests” data mining algorithm is demonstrated using a case study of sanitary sewer pipe condition data collected by a municipality in Ontario, Canada. A comparison of class-imbalance learning strategies (undersampling and threshold adjustment) is carried out to evaluate the potential to increase predictive accuracy for bad condition pipes representing the minority class of interest. Threshold adjustment is found to provide an optimal level of performance for the classification problem – with the trained predictive model achieving a false negative rate of 18%, false positive rate of 29% and an excellent area under the receiver operating characteristic (ROC) curve of 0.81 (considered to be excellent given the nature of the inspection dataset). An analysis of the predictive capabilities of the random forests algorithm trained using a dataset one-third the size of the one originally available to the case study municipality indicates the algorithm has utility for municipalities outside of the case study area. Network spatial analytics are implemented to visualize clusters of bad condition pipes in the sewer system. Visualization of pipe condition in this manner is found to be an effective tool for screening candidate pipes for inspection and for conveying the necessity of inspection to members of a municipality responsible for approving sewer inspection-related budgets.

Keywords: Exfiltration; Inspection; Predictive Analytics; Random Forests; Wastewater