The Impact of Wastewater Quality and Flow Characteristics on H2S Emissions Generation: Statistical Correlations and an Artificial Neural Network Model

Abstract

Hydrogen sulfide (H2S) is a naturally occurring, highly toxic gas that is formed from the decomposition of sulfur compounds. H2S is a common source of concrete and metal corrosion that results in huge economic losses in wastewater collection and treatment plants. Hence, it is necessary to analyze H2S generation and emission. H2S concentrations were measured at the Al-Saad wastewater treatment plant in the United Arab Emirates. Wastewater samples were collected, and water quality parameters were characterized in the laboratory. Simultaneously, flow characteristics, humidity, headspace airflow, and temperature were measured onsite. A neural network model to predict H2S emissions was formulated using significant parameters. It was observed that flowrate, velocity, sulfate, and total sulfur had a similar cyclic pattern throughout the sampling events. The temperature, humidity, total sulfur, and depth of wastewater were identified as the most important parameters influencing H2S emissions through correlation analysis. The neural model validation and testing had an R value of 0.9. The training had an R value of 0.8. The model provided an accuracy of 80% for the prediction of H2S concentration in wastewater treatment plants. The accuracy can be improved by increasing the data. The model is limited to its applicability in the prediction of H2S emissions under conditions similar to the inlet of a wastewater treatment plant.