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
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
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