Browsing by Author "Vernon, D."

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    Short-term memory artificial neural network modelling to predict concrete corrosion in wastewater treatment plant inlet chambers using sulphide sensors
    (Elsevier, 2025-01-01) Mendizabal, J.; Vernon, D.; Martin, Ben; Bajón-Fernández, Yadira; Soares, Ana
    Sulphide accumulation in lengthy rising mains can lead to significant concrete corrosion and odour issues at manholes and wastewater treatment plants (WWTPs). Monitoring dissolved sulphide, typically relies on auto-sampling or grab samples followed by laboratory analysis, remains underdeveloped. This study aimed to identify sources of concrete corrosion sources at a WWTP inlet chamber and develop a sulphide prediction model using artificial intelligence (AI). A dissolved sulphide sensor was installed at three rising mains (RM1 to RM3) and the combined inlet at a full-scale WWTP, providing a 5-minute resolution data that revealed a daily hydrogen sulphide (H2S) pattern that inversely correlated with the flow rate. RM1 exhibited the highest sulphide load, peaking at 3.6 kg/d during cold months and 4.2 kg/d during warm months. RM3 and RM2 recorded loads of 2.96 kg/d and 0.98 kg/d, respectively, during cold months. A long short-term memory (LSTM) artificial neural network (ANN) model was developed to predict H₂S concentrations at RM1, using flow rate, temperature, and time of day as inputs. The model achieved a root mean square error (RMSE) of 0.34 and a Nash-Sutcliffe efficiency (NSE) of 0.57, accurately predicting the daily H2S pattern. This study's main contributions include insights into sulphide dynamics from high-resolution sensor data, which could support corrosion management as part of a septicity warning system or feedforward control for sulphide treatment. Additionally, the AI-based prediction model offers potential for sensor repurposing, saving both capital and operational costs.