Application of a generalized hybrid machine learning model for the prediction of H2S and VOCs removal in a compact trickle bed bioreactor (CTBB).

This study presents a generalized hybrid model for predicting H2S and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odor removal efficiency (K) using a classification model, and (ii) prediction of K <100%, based on inlet concentration, time of day, pH and retention time. Global sensitivity analysis (GSA) was used to test the relationships between the inputs and outputs of the K-NN model. The results from classification model simulation showed high goodness of fit for the classification models to predict the removal of H2S and VOCs (SPEC = 0.94-0.99, SENS = 0.96-0.99). It was shown that the hybrid K-NN model applied for the "Klimzowiec" WWTP, including the pilot plant, can also be applied to the "Urbanowice" WWTP. The hybrid machine learning model enables the development of a universal system for monitoring the removal of H2S and VOCs from WWTP facilities.

Odor and volatile organic compounds biotreatment using compact trickle bed bioreactors (CTBB) in a wastewater treatment plant.

The main aim of this study was to optimize and maximize the impacts of odor and volatile organic compounds (VOCs) biodegradation in a wastewater treatment plant utilizing a pilot-scale compact trickle bed bioreactor (CTBB). A CTBB was built and tested for its long-term performance during which gases were supplied from the tank containing semi-liquid fats, oils, and fat waste. The concentrations of pollutants ranged from 0 to 140.75 mg/m3 H2S, 0 to 2500 mg/m3 VOCs, and 0 to 21.5 mg/m3 NH3. The CTBB was tested at different gas flow rates and at two pH values for the liquid phase: pH = 7.0 and 5.0. In the liquid phase, the pollutant removal efficiency was higher at pH = 7.0 than at pH = 5.0. Overall, the removal efficiency was between 81.5 % and 99.5 % for the VOCs and 87.5 % and 98.9 % for H2S, while NH3 removals were >99 %.

Application of a compact trickle-bed bioreactor for the removal of odor and volatile organic compounds emitted from a wastewater treatment plant.

A compact trickle-bed bioreactor (CTBB) was tested for the removal of volatile organic compounds (VOCs) and hydrogen sulphide (H2S) present in the exhaust air of a wastewater treatment plant. At gas-flow rates varying between 2.0 and 30.0 m3/h and for specific pollutant loads up to 20 g/(m3·h), removal efficiencies for H2S and VOC were >95%. The CTBB was designed for a maximum H2S concentration of ∼200 ppm and removal efficiencies >97% were noticed. VOC concentrations were in the range of 25-240 ppmv and the removal efficiency was in the range of 85-99%. Possible consequences of an excessive pollutant overload and the time required for regenerating the microbial activity and reviving stable process conditions in the CTBB were also investigated. An increase in the H2S concentration from 400 to 600 ppmv for a few hours caused bioreactor poisoning; however, when original H2S concentrations were restored, stable CTBB operation was ascertained within 3 h.