Design of a Soft Sensor Based on Long Short-Term Memory Artificial Neural Network (LSTM) for Wastewater Treatment Plants.

Assessment of wastewater effluent quality in terms of physicochemical and microbial parameters is a difficult task; therefore, an online method which combines the variables and represents a final value as the quality index could be used as a useful management tool for decision makers. However, conventional measurement methods often have limitations, such as time-consuming processes and high associated costs, which hinder efficient and practical monitoring. Therefore, this study presents an approach that underscores the importance of using both short- and long-term memory networks (LSTM) to enhance monitoring capabilities within wastewater treatment plants (WWTPs). The use of LSTM networks for soft sensor design is presented as a promising solution for accurate variable estimation to quantify effluent quality using the total chemical oxygen demand (TCOD) quality index. For the realization of this work, we first generated a dataset that describes the behavior of the activated sludge system in discrete time. Then, we developed a deep LSTM network structure as a basis for formulating the LSTM-based soft sensor model. The results demonstrate that this structure produces high-precision predictions for the concentrations of soluble X1 and solid X2 substrates in the wastewater treatment system. After hyperparameter optimization, the predictive capacity of the proposed model is optimized, with average values of performance metrics, mean square error (MSE), coefficient of determination (R2), and mean absolute percentage error (MAPE), of 23.38, 0.97, and 1.31 for X1, and 9.74, 0.93, and 1.89 for X2, respectively. According to the results, the proposed LSTM-based soft sensor can be a valuable tool for determining effluent quality index in wastewater treatment systems.

An Initial Proteomic Analysis of Biogas-Related Metabolism of Euryarchaeota Consortia in Sediments from the Santiago River, México.

In this paper, sediments from the Santiago River were characterized to look for an alternative source of inoculum for biogas production. A proteomic analysis of methane-processing archaea present in these sediments was carried out. The Euryarchaeota superkingdom of archaea is responsible for methane production and methane assimilation in the environment. The Santiago River is a major river in México with great pollution and exceeded recovery capacity. Its sediments could contain nutrients and the anaerobic conditions for optimal growth of Euryarchaeota consortia. Batch bioreactor experiments were performed, and a proteomic analysis was conducted with current database information. The maximum biogas production was 266 NmL·L-1·g VS-1, with 33.34% of methane, and for proteomics, 3206 proteins were detected from 303 species of 69 genera. Most of them are metabolically versatile members of the genera Methanosarcina and Methanosarcinales, both with 934 and 260 proteins, respectively. These results showed a diverse euryarcheotic species with high potential to methane production. Although related proteins were found and could be feeding this metabolism through the methanol and acetyl-CoA pathways, the quality obtained from the biogas suggests that this metabolism is not the main one in carbon use, possibly the sum of several conditions including growth conditions and the pollution present in these sediments.