From local bacterial communities to energy: application of microbial fuel cells in laboratory experimentation.

It is well known that bacterial communities are an essential component to maintain the balance of terrestrial ecosystems due to the functions and services performed by microorganisms in the environment. The research seeking on alternative energy sources has shown that bacterial communities can bioconvert the chemical energy of an organic substrate into electrical energy, within devices known as microbial fuel cells. For this reason, this class project allows students of Biotechnology, Environmental Science, and Microbiology to apply the appropriate methodology to develop a class project throughout an environmental bacterial community capable of generating electrical energy.

Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parameters.

A predictive model based on artificial neural networks (ANNs) for modeling primary settling tanks' (PSTs) behavior in wastewater treatment plants was developed in this study. Two separate ANNs were built using input data, raw wastewater characteristics, and operating conditions. The output data from the ANNs consisted of the total suspended solids (TSS) concentration and chemical oxygen demand (COD) as predictions of PSTs' typical effluent parameters.