The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber have been employed. However, the evaluation of odor emission involves huge manpower, time, and cost. An artificial neural network (ANN) is recognized as an efficient method to find correlations between nonlinear data and prediction of future data based on these correlations. Due to its usefulness, ANN is used to solve complicated problems in various disciplines of sciences and engineering. In this study, a method to predict the odor concentration in a WWTP using ANN was developed. The odor concentration emitted from a WWTP was predicted by the ANN based on water quality data such as biological oxygen demand, dissolved oxygen, and pH. The water quality and odor concentration data from the WWTP were measured seasonally in spring, summer, and autumn and these were used as input variations to the ANN model. The odor predicted by the ANN model was compared with the measured data and the prediction accuracy was estimated. Suggestions for improving prediction accuracy are presented.

In this study, to simulate a biogas desulfurization process, a modified Monod-Gompertz kinetic model incorporating a dissolved oxygen (DO) effect was proposed for a sulfur-oxidizing bacterial (SOB) strain, Acidithiobacillus thiooxidans, under extremely acidic conditions of pH 2. The kinetic model was calibrated and validated using experimental data obtained from a bubble-column bioreactor. The SOB strain was effective for H₂S degradation, but the H₂S removal efficiency dropped rapidly at DO concentrations less than 2.0 mg/L. A low H₂S loading was effectively treated with oxygen supplied in a range of 2%–6%, but a H₂S guideline of 10 ppm could not be met, even with an oxygen supply greater than 6%, when the H₂S loading was high at a short gas retention time of 1 min and a H₂S inlet concentration of 5000 ppm. The oxygen supply should be increased in the aerobic desulfurization to meet the H₂S guideline; however, the excess oxygen above the optimum was not effective because of the decline in oxygen efficiency. The model estimation indicated that the maximum H₂S removal rate was approximately 400 ppm/%-O₂ at the influent oxygen concentration of 4.9% under the given condition. The kinetic model with a low DO threshold for the interacting substrates was a useful tool to simulate the effect of the oxygen supply on the H₂S removal and to determine the optimal oxygen concentration.

A two-stage hydrogen/methane fermentation process has emerged as a feasible engineering system to recover bio-energy from wastewater. Hydrogen-producing bacteria (HPB) generate hydrogen from readily available carbohydrates, and organic acids produced during the hydrogen fermentation step can be degraded to generate methane in the following step. Three strong acids, HCl, H2SO4, and HNO3, were tested to determine the appropriate pre-treatment method for enhanced hydrogen production. The hydrogen production rates of 230, 290, and 20 L/kg-glucose/day was observed for the sludge treated with HCl, H2SO4, and HNO3, respectively, indicating that the acid pre-treatment using either HCl or H2SO4 resulted in a significant increase in hydrogen production. The fluorescent in situ hybridization method indicated that the acid pre-treatment selectively enriched HPB including Clostridium sp. of cluster I from inoculum sludge. After hydrogen fermentation was terminated, the sludge was introduced to a methane fermentation reactor. This experiment showed methane production rates of 100, 30, and 13 L/kg-glucose/day for the sludge pre-treated with HCl, H2SO4, and HNO3, respectively, implying that both sulfate and nitrate inhibited the activity of methane-producing bacteria. Consequently, the acid pre-treatment might be a feasible option to enhance biogas recovery in the two-stage fermentation process, and HCl was selected as the optimal strong acid for the enrichment of HPB and the continuous production of methane.