2 research outputs found

    Polycyclic Aromatic Hydrocarbon Affects Acetic Acid Production during Anaerobic Fermentation of Waste Activated Sludge by Altering Activity and Viability of Acetogen

    No full text
    Till now, almost all the studies on anaerobic fermentation of waste activated sludge (WAS) for bioproducts generation focused on the influences of operating conditions, pretreatment methods and sludge characteristics, and few considered those of widespread persistent organic pollutants (POPs) in sludge, for example, polycyclic aromatic hydrocarbons (PAHs). Herein, phenanthrene, which was a typical PAH and widespread in WAS, was selected as a model compound to investigate its effect on WAS anaerobic fermentation for short-chain fatty acids (SCFAs) accumulation. Experimental results showed that the concentration of SCFAs derived from WAS was increased in the presence of phenanthrene during anaerobic fermentation. The yield of acetic acid which was the predominant SCFA in the fermentation reactor with the concentration of 100 mg/kg dry sludge was 1.8 fold of that in the control. Mechanism exploration revealed that the present phenanthrene mainly affected the acidification process of anaerobic fermentation and caused the shift of the microbial community to benefit the accumulation of acetic acid. Further investigation showed that both the activities of key enzymes (phosphotransacetylase and acetate kinase) involved in acetic acid production and the quantities of their corresponding encoding genes were enhanced in the presence of phenanthrene. Viability tests by determining the adenosine 5′-triphosphate content and membrane potential confirmed that the acetogens were more viable in anaerobic fermentation systems with phenanthrene, which resulted in the increased production of acetic acid

    Prediction and Evaluation of Indirect Carbon Emission from Electrical Consumption in Multiple Full-Scale Wastewater Treatment Plants via Automated Machine Learning-Based Analysis

    No full text
    The indirect carbon emission from electrical consumption of wastewater treatment plants (WWTPs) accounts for large proportions of their total carbon emissions, which deserves intensive attention. This work proposed an automated machine learning (AutoML)-based indirect carbon emission analysis (ACIA) approach and predicted the specific indirect carbon emission from electrical consumption (SEe; kg CO2/m3) successfully in nine full-scale WWTPs (W1–W9) with different treatment configurations based on the historical operational data. The stacked ensemble models generated by the AutoML accurately predicted the SEe (mean absolute error = 0.02232–0.02352, R2 = 0.65107–0.67509). Then, the variable importance and Shapley additive explanations (SHAP) summary plots qualitatively revealed that the influent volume and the types of secondary and tertiary treatment processes were the most important variables associated with SEe prediction. The interpretation results of partial dependence and individual conditional expectation further verified quantitative relationships between input variables and SEe. Also, low energy efficiency with high indirect carbon emission of WWTPs was distinguished. Compared with traditional carbon emission analysis and prediction methods, the ACIA method could accurately evaluate and predict SEe of WWTPs with different treatment scales and processes with easily available variables and reveal qualitative and quantitative relationships inside datasets simultaneously, which is a powerful tool to benefit the “carbon neutrality” of WWTPs
    corecore