Dynamic calibration of process-wide partial-nitritation modeling with airlift granular for nitrogen removal in a full-scale wastewater treatment plant.

A main challenge in rapid nitrogen removal from rejected water in wastewater treatment plants (WWTPs) is growth of biomass by nitrite-oxidizing bacteria (NOB) and ammonia-oxidizing bacteria (AOB). In this study, partial nitritation (PN) coupled with air-lift granular unit (AGU) technology was applied to enhance nitrogen-removal efficiency in WWTPs. For successful PN process at high-nitrogen-influent conditions, a pH of 7.5-8 for high free-ammonia concentrations and AOB for growth of total bacterial populations are required. The PN process in a sequential batch reactor (SBR) with AGU was modeled as an activated sludge model (ASM), and dynamic calibration using full-scale plant data was performed to enhance aeration in the reactor and improve the nitrite-to-ammonia ratio in the PN effluent. In steady-state and dynamic calibrations, the measured and modeled values of the output were in close agreement. Sensitivity analysis revealed that the kinetic and stoichiometric parameters are associated with growth and decay of heterotrophs, AOB, and NOB microorganisms. Overall, 80% of the calibrated data fit the measured data. Stage 1 of the dynamic calibration showed NO2 and NO3 values close to 240 mg/L and 100 mg/L, respectively. Stage 2 showed NH4 values of 200 mg/L at day 30 with the calibrated effluent NO2 and NO3 value of 250 mg/L. In stage 3, effluent NH4 concentration was 200 mg/L at day 60.

Transfer learning driven sequential forecasting and ventilation control of PM2.5 associated health risk levels in underground public facilities.

Particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) has become a major public concern in closed indoor environments, such as subway stations. Forecasting platform PM2.5 concentrations is significant in developing early warning systems, and regulating ventilation systems to ensure commuter health. However, the performance of existing forecasting approaches relies on a considerable amount of historical sensor data, which is usually not available in practical situations due to hostile monitoring environments or newly installed equipment. Transfer learning (TL) provides a solution to the scant data problem, as it leverages the knowledge learned from well-measured subway stations to facilitate predictions on others. This paper presents a TL-based residual neural network framework for sequential forecast of health risk levels traced by subway platform PM2.5 levels. Experiments are conducted to investigate the potential of the proposed methodology under different data availability scenarios. The TL-framework outperforms the RNN structures with a determination coefficient (R2) improvement of 42.84%, and in comparison, to stand-alone models the prediction errors (RMSE) are reduced up to 40%. Additionally, the forecasted data by TL-framework under limited data scenario allowed the ventilation system to maintain IAQ at healthy levels, and reduced PM2.5 concentrations by 29.21% as compared to stand-alone network.

An autonomous operational trajectory searching system for an economic and environmental membrane bioreactor plant using deep reinforcement learning.

Optimal operation of membrane bioreactor (MBR) plants is crucial to save operational costs while satisfying legal effluent discharge requirements. The aeration process of MBR plants tends to use excessive energy for supplying air to micro-organisms. In the present study, a novel optimal aeration system is proposed for dynamic and robust optimization. Accordingly, a deep reinforcement learning (DRL)-based optimal operating system is proposed, so as to meet stringent discharge qualities while maximizing the system's energy efficiency. Additionally, it is compared with the manual system and conventional reinforcement learning (RL)-based systems. A deep Q-network (DQN) algorithm automatically learns how to operate the plant efficiently by finding an optimal trajectory to reduce the aeration energy without degrading the treated water quality. A full-scale MBR plant with the DQN-based autonomous aeration system can decrease the MBR's aeration energy consumption by 34% compared to other aeration systems while maintaining the treatment efficiency within effluent discharge limits.

Population response modeling and habitat suitability of Cobitis choii fish species in South Korea for climate change adaptation.

Endangered species ecosystems require appropriate monitoring for assessing population growth related to the emerging pollutants in their habitat conditions. The response of population growth of Cobitis choii, an endangered fish species, under the exposure to emerging pollutants present in the Geum River Basin of South Korea was studied. Toxicity models of concentration addition (CA), independent action (IA), and concentration addition-independent action (CAIA) were implemented utilizing the concentration of a set of 25 chemicals recorded in the study area. Thus, a population-level response analysis was developed based on the abundance of Cobitis choii for period 2011-2015. The results were compared showing that the CA and IA models were the most conservative approaches for the prediction of growth rate. Further, a standard abnormality index (SAI) and habitat suitability (HS) indicators based on the climate, habitat, and abundance data were presented to completely analyze the population growth of the species. Suitability of the species growth was most probable for year 2015 for the variables of air temperature and land surface temperature. A spatial analysis was complementarily presented to visualize the correlation of variables for the best suitability of the species growth. This study presents a methodology for the analysis of the ecosystem's suitability for Cobitis choii growth and its assessment of the chemicals present in Geum River stream.

A real time QSAR-driven toxicity evaluation and monitoring of iron containing fine particulate matters in indoor subway stations.

A fine particulate matter less than 2.5 µm (PM2.5) in the underground subway system are the cause of many diseases. The iron containing PMs frequently confront in underground stations, which ultimately have an impact on the health of living beings especially in children. Hence, it is necessary to conduct toxicity assessment of chemical species and regularized the indoor air pollutants to ensure the good health of children. Therefore, in this study, a new indoor air quality (IAQ) index is proposed based on toxicity assessment by quantitative structure-activity relationship (QSAR) model. The new indices called comprehensive indoor air toxicity (CIAT) and cumulative comprehensive indoor air toxicity (CCIAT) suggests the new standards based on toxicity assessment of PM2.5. QSAR based deep neural network (DNN) exhibited the best model in predicting the toxicity assessment of chemical species in particulate matters, which yield lowest RMSE and QF32 values of 0.6821 and 0.8346, respectively, in the test phase. After integration with a standard concentration of PM2.5, two health risk indices of CIAT and CCIAT are introduced based on toxicity assessment results, which can be use as the toxicity standard of PM2.5 for detail IAQ management in a subway station. These new health risk indices suggest more sensitive air pollutant level of iron containing fine particulate matters or molecular level contaminants in underground spaces, alerting the health risk of adults and children in "unhealthy for sensitive group".

Quantitative structure-property relationship (QSPR) models for predicting the physicochemical properties of polychlorinated biphenyls (PCBs) using deep belief network.

Octanol/water partition coefficient (log P), octanol/air partition coefficient (log KOA) and bioconcentration factor (log BCF) are important physiochemical properties of organic substances. Quantitative structure-property relationship (QSPR) models are a promising alternative method of reducing and replacing experimental steps in determination of log P, log KOA and log BCF. In the current study, we propose a new QSPR model based on a deep belief network (DBN) to predict the physicochemical properties of polychlorinated biphenyls (PCBs). The prediction accuracy of the proposed model was compared to the results of previous reported models. The predictive ability of the DBN model, validated with a test set, is clearly superior to the other models. All results showed that the proposed model is robust and satisfactory, and can effectively predict the physiochemical properties of PCBs without highly reliable experimental values.

A systematic approach of removal mechanisms, control and optimization of silver nanoparticle in wastewater treatment plants.

The release of silver nanoparticles (AgNPs) to wastewater caused by over-generation and poor treatment of the remaining nanomaterial has raised the interest of researchers. AgNPs can have a negative impact on watersheds and generate degradation of the effluent quality of wastewater treatment plants (WWTPs). The aim of this research is to design and analyze an integrated model system for the removal of AgNPs with high effluent quality in WWTPs using a systematic approach of removal mechanisms modeling, optimization, and control of the removal of silver nanoparticles. The activated sludge model 1 was modified with the inclusion of AgNPs removal mechanisms, such as adsorption/desorption, dissolution, and inhibition of microbial organisms. Response surface methodology was performed to minimize the AgNPs and total nitrogen concentrations in the effluent by optimizing operating conditions of the system. Then, the optimal operating conditions were utilized for the implementation of control strategies into the system for further analysis of enhancement of AgNPs removal efficiency. Thus, the overall AgNP removal efficiency was found to be slightly higher than 80%, which was an improvement of almost 7% compared to the BSM1 reference value. This study provides a systematic approach to find an optimal solution for enhancing AgNP removal efficiency in WWTPs and thereby to prevent pollution in the environment.