Development of a low-cost electrochemical sensor for monitoring components in wastewater treatment processes.

Anaerobic digestion (AD) is a complex biological process widely used to decompose various types of organic matter, as well as to produce some metabolites and biogas. Diverse microorganism groups cooperate in many intricate metabolic routes so that organic matter can be degraded. However, any imbalance on these routes can lead to process instability or even failure. Therefore, a proper monitoring system, as well as a good understanding of the process, are key steps to improve performance and stability. Several mathematical models have been developed to represent AD. Despite this, process monitoring is mostly conducted by analytical methods, whose equipment is either expensive or the analyses are time-consuming, which may be a hindrance to low-budget developments. The objective of this study was to develop a low-cost electrochemical sensor to monitor components in wastewater treatment plants. Hundreds of synthetically supplemented sugarcane vinasse and synthetic domestic sewage samples were characterised. The obtained signals were used to calibrate principal component regression, partial least square and artificial neural network estimation models. The predictable variables were chemical oxygen demand, volatile fatty acids, sodium bicarbonate, beef extract, and lipids, and their R2 ranged from 0.84 to 0.99, depending on the component.

Modeling anaerobic digestion metabolic pathways for antibiotic-contaminated wastewater treatment.

Anaerobic digestion has been used to treat antibiotic-contaminated wastewaters. However, it is not always effective, since biodegradation is the main removal mechanism and depends on the compound chemical characteristics and on how microbial metabolic pathways are affected by the reactor operational conditions and hydrodynamic characteristics. The aim of this study was to develop a mathematical model to describe 16 metabolic pathways of an anaerobic process treating sulfamethazine-contaminated wastewater. Contois kinetics and a useful reaction volume term were used to represent the biomass concentration impact on bed porosity in a N continuously stirred tank modeling approach. Two sulfamethazine removal hypotheses were evaluated: an apparent enzymatic reaction and a cometabolic degradation. Additionally, long-term modeling was developed to describe how the operational conditions affected the performance of the process. The best degradation correlations were associated with the consumption of carbohydrates, proteins and it was inversely related to acetic acid production during acidogenesis.