Dammika Vitanage

Development of an on-line nitrogen monitoring system using Microdistillation Flow Analysis

Microdistillation Flow Analysis (MDFA) is a procedure to convert dissolved compounds into volatile compounds that can be transferred and isolated into another solution for determination by conductivity. Reagents are added to allow the analyte to form and accumulate in a collector solution prior to conductance detection. Control variables (sample and reagent flow rate, microstill air flow and temperature, and post-still collector volume) were optimised for the analysis. MDFA provided ammonia N data at a rate of 12 samples per hour with 2 % precision for samples containing ammonia or chloramine ranging from 0.03 to 0.6 mg/L ammonia N using a custom made prototype. Chloramine was also measured after in-stream reduction as ammonia N. The MDFA system may thus be useful as a tool in disinfection control and other environmental monitoring applications.

Spatial prediction of hydrogen sulfide in sewers with a modified Gaussian process combined mutual information

This paper proposes a data driven machine learning model for spatial prediction of hydrogen sulfide (H2S) in a gravity sewer system. The gaseous H2S in the overhead of the gravity sewer is modelled using a Gaussian Process with a new covariance function due to constraints of sewer boundaries. The covariance function is proposed based on the distance between two locations computed along the lengths of the sewer network. A mutual information based strategy is used to choose the best k sensor measurements and their locations from among n potential sensor observations and their locations. This provably NP-hard combinatorial sensor selection problem is addressed by maximizing the mutual information between the selected locations and the locations that are not selected or do not have any sensor deployments. A proof-of-concept study was carried out comparing the spatial prediction of H2S with a complex model currently used by Sydney Water. The proposed approach is shown to be effective in both modelling and predicting the H2S spatial concentrations in sewers as well as identifying optimal number of H2S sensors and their locations for a required level of prediction accuracy.

Robust sensing suite for measuring temporal dynamics of surface temperature in sewers

Sewerage systems are paramount underground infrastructure assets for any nation. In most cities, they are old and have been exposed to significant microbial induced corrosion. It is a serious global problem as they pose threats to public health and economic repercussions to water utilities. For managing sewer assets efficaciously, it is vital to predict the rate of corrosion. Predictive models of sewer corrosion incorporate concrete surface temperature measurements as an observation. However, currently, it has not been fully utilized due to unavailability of a proven sensor. This study reports the feasibility of infrared radiometer for measuring the surface temperature dynamics in the aggressive sewer conditions. The infrared sensor was comprehensively evaluated in the laboratory at different environmental conditions. Then, the sensor suite was deployed in a Sydney based sewer for three months to perform continuous measurements of surface temperature variations. The field study revealed the suitability of the developed sensor suite for non-contact surface temperature measurements in hostile sewer conditions. Further, the accuracy of the sensor measurements was improved by calibrating the sensor with emissivity coefficient of the sewer concrete. Overall, this study will ameliorate the present sewer corrosion monitoring capabilities by providing new data to models predicting sewer corrosion.