Predicting effluent quality parameters for wastewater treatment plant: A machine learning-based methodology.

Wastewater Treatment Plants (WWTPs) present complex biochemical processes of high variability and difficult prediction. This study presents an innovative approach using Machine Learning (ML) models to predict wastewater quality parameters. In particular, the models are applied to datasets from both a simulated wastewater treatment plant (WWTP), using DHI WEST software (WEST WWTP), and a real-world WWTP database from Santa Catarina Brewery AMBEV, located in Lages/SC - Brazil (AMBEV WWTP). A distinctive aspect is the evaluation of predictive performance in continuous data scenarios and the impact of changes in WWTP operations on predictive model performance, including changes in plant layout. For both plants, three different scenarios were addressed, and the quality of predictions by random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP) models were evaluated. The prediction quality by the MLP model reached an R2 of 0.72 for TN prediction in the WEST WWTP output, and the RF model better adapted to the real data of the AMBEV WWTP, despite the significant discrepancy observed between the real and the predicted data. Techniques such as Partial Dependence Plots (PDP) and Permutation Importance (PI) were used to assess the importance of features, particularly in the simulated WEST tool scenario, showing a strong correlation of prediction results with influent parameters related to nitrogen content. The results of this study highlight the importance of collecting and storing high-quality data and the need for information on changes in WWTP operation for predictive model performance. These contributions advance the understanding of predictive modeling for wastewater quality and provide valuable insights for future practice in wastewater treatment.

Residual biomass from surfactin production is a source of arginase and adsorbed surfactin that is useful for environmental remediation.

Lipopeptides are important secondary metabolites produced by microbes. They find applications in environmental decontamination and in the chemical, pharmaceutical and food industries. However, their production is expensive. In the present work we propose three strategies to lower the production costs of surfactin. First, the coproduction of surfactin and arginase in a single growth. Second, extract the fraction of surfactin that adsorbs to the biomass and is removed from the growth medium through centrifugation. Third, use microbial biomass for the remediation of organic and inorganic contaminants. The coproduction of surfactin and arginase was evaluated by factorial design experiments using the LB medium supplemented with arginine. The best conditions for surfactin production were 22 h of growth at 37 °C using LB supplemented with arginine 7.3 g/L. Almost similar conditions were found to produce highest levels of arginase, 24 h and 6.45 g/L arginine. Decontamination of phenol and copper from artificial samples was attained by treatment with residues from lipopeptide production. Thus, cell suspensions and wash-waters used to extract surfactin from the biomass. Cell suspensions were used to successfully remove hydroquinone. Cell suspensions and wash-waters containing surfactin were successfully used to recover copper from solution. Specific monitoring methods were used for phenol and metal solutions, respectively a biosensor based on tyrosinase and either atomic absorption flame ionization spectrometry or absorbance coupled to the Arduino™ platform. Therefore, we report three alternative strategies to lower the production costs in lipopeptide production, which include the effective recovery of copper and phenol from contaminated waters using residues from surfactin production. Sustainable and profitable production of surfactin can be achieved by a coproduction strategy of lipopeptides and enzymes. Lipopeptides are collected in the supernatant and enzymes in the biomass. In addition, lipopeptides that coprecipitate with biomass can be recovered by washing. Lipopeptide wash-waters find applications in remediation and cells can also be used for environmental decontamination.