Management Of Wastewater Treatment Plants Research Articles

Pre-training enhanced spatio-temporal graph neural network for predicting influent water quality and flow rate of wastewater treatment plant: Improvement of forecast accuracy and analysis of related factors

Efficient management of wastewater treatment plants (WWTPs) necessitates accurate forecasting of influent water quality parameters (WQPs) and flow rate (Q) to reduce energy consumption and mitigate carbon emissions. The time series of WQPs and Q are highly non-linear and influenced by various factors such as temperature (T) and precipitation (Precip). Conventional models often struggle to account for long-term temporal patterns and overlook the complex interactions of parameters within the data, leading to inaccuracies in detecting WQPs and Q. This work introduced the Pre-training enhanced Spatio-Temporal Graph Neural Network (PT-STGNN), a novel methodology for accurately forecasting of influent COD, ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), pH and Q in WWTPs. PT-STGNN utilizes influent data of the WWTP, air quality data and meteorological data from the service area as inputs to enhance prediction accuracy. The model employs unsupervised Transformer blocks for pre-training, with efficient masking strategies to effectively capture long-term historical patterns and contextual information, thereby significantly boosting forecasting accuracy. Furthermore, PT-STGNN integrates a unique graph structure learning mechanism to identify dependencies between parameters, further improving the model's forecasting accuracy and interpretability. Compared with the state-of-the-art models, PT-STGNN demonstrated superior predictive performance, particularly for a longer-term prediction (i.e., 12 h), with MAE, RMSE and MAPE at 12-h prediction horizon of 2.737 ± 0.040, 4.209 ± 0.060 and 13.648 ± 0.151 %, respectively, for the algebraic mean of each parameter. From the results of graph structure learning, it is observed that there are strong dependencies between NH3-N and TN, TP and Q, as well as Precip, etc. This study innovatively applies STGNN, not only offering a novel approach for predicting influent WQPs and Q in WWTPs, but also advances our understanding of the interrelationships among various parameters, significantly enhancing the model's interpretability.

Investigation of organic content and microbial characteristics distribution at the pretreatment units for wastewater treatment plant sustainable upgrading

It is necessary and significant to acquire the influent characteristics in the whole pretreatment units of the activated sludge process in wastewater treatment plants (WWTPs) for the sustainable upgrading. In this work, we drawn the samples and continuously investigated all the structure units of the pretreatment section in a typical wastewater treatment plant including the anaerobic and aerobiotic treatment processes. We monitored the routine environmental parameters in the water, including dissolved oxygen (DO), pH, and suspended solids (SS). At the same time, we investigated the concentration distribution patterns of chemical oxygen demand (COD) and biochemical oxygen demand (BOD5) and found that the degradation of organic matter in the sewage continued in the pretreatment section. And the remarkable decreasing tendency of organic pollution concentration was confirmed along the flow direction in the pretreatment section, suggesting a modest depletion effect on influent organic matter of the pretreatment process. Moreover, to further validate the degradation process of organic compounds in the preprocessing procedure, analysis and detection were conducted on the types of organic compounds and the microbial community structures in the water samples collected at various sampling points through three-dimensional fluorescent spectroscopy and high-throughput sequencing, respectively. Conclusively, the detailed and accurate influent characteristics can support the scientific basis for the WWTP sustainable operation, management, and upgrading, especially for that with the low C/N ratio influent.

Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants

Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants’ operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.

Evaluation of Communal WWTP Management in Supporting the Smart Environment in Mlati District, Sleman Regency

Communal Wastewater Treatment Plant (WWTP) is a solution used in the management and treatment of domestic waste. Mlati District is an area that has 8 active Communal WWTP units spread across four villages. Management by the local community to the type of technology used in each region certainly has differences. This research aims to determine the benchmark for the effectiveness of developing one of the smart city dimensions, that is the smart environment in Mlati District. This research was conducted using qualitative research methods, with data processing and presentation carried out descriptively. The existing conditions of the Communal WWTP were obtained by field observation, the management of the Communal WWTP was then carried out descriptive analysis based on the observations, indepth interviews and secondary data and then continued with descriptive analysis to find out management in supporting the smart environment in the Mlati District. The results of the study show that the management of the Communal WWTP in Mlati District has been going well with minimal risk of damage and obstacles. Communal WWTP is the main infrastructure in domestic waste processing which brings significant changes to the region and society. The existence of a waste water treatment installation unit is considered to be able to meet the community’s needs for waste handling. Although waste treatment is not discussed significantly in the elements of smart city development, centralized domestic waste management in Mlati District has proven to play a role in supporting the realization of a smart environment in the region.

A multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor for global prediction of ammonia-nitrogen concentration in wastewater treatment processes

Ammonia-nitrogen concentration is a key water quality indicator, which reflects changes in pollutant components during wastewater treatment processes. The timely and accurate detection results contribute to optimizing control and operational management of wastewater treatment plants (WWTPs), but current detection methods only focus on the effluent location. This paper proposes a multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor to achieve the global prediction of ammonia-nitrogen concentration. Firstly, the wastewater treatment process is divided into several independent subsystems depending on the reaction mechanism, and the variable selection is performed using mutual information. Subsequently, the bidirectional long short-term memory network (Bi-LSTM) is employed to construct a model for predicting ammonia-nitrogen concentration within each subsystem, and the outputs between neighboring subsystems are incorporated as a set of new variables added into the training dataset to strengthen their connection. Finally, to address performance degradation caused by environmental factors, a probability density function (PDF)-based dynamic moving window method is proposed to enhance the robustness. The effectiveness and superiority of the proposed soft sensor are validated in the Benchmark Simulation Model no. 1 (BSM1). The experimental results demonstrate that the proposed soft sensor can accurately predict the global ammonia-nitrogen concentration in the face of different weather conditions including sunny, rainy, and stormy days. This study contributes to the stable operation of WWTPs with higher treatment efficiency and lower economic costs.

Exploring stochastic differential equation for analyzing uncertainty in wastewater treatment plant-activated sludge modeling

Abstract The management of wastewater treatment plant (WWTP) and the assessment of uncertainty in its design are crucial from an environmental engineering perspective. One of the key mechanisms in WWTP operation is activated sludge, which is related to the biological oxygen demand (BOD) parameter. In the modeling of BOD, the conventional approach utilizing ordinary differential equations (ODEs) fails to incorporate the stochastic nature of this parameter, leading to a considerable level of uncertainty in the design of WWTP. To address this issue, this study proposes a stochastic model that utilizes stochastic differential equations (SDEs) instead of ODE to simulate BOD activities of microorganisms and wastewater flow rate (Q). The SDEs and integral It̂o are solved using the Euler–Maruyama method for a period of 15 sequential days and the timespan of 2019–2020 for a WWTP in Tabriz City. SDE improves the design of WWTP facilities by identifying uncertainties and enhancing reliability. This, in turn, increases the reliability of the technical structures within the WWTP and improves the performance of its biological system. By considering the randomness of the problem, the proposed method significantly improves the results, with an enhancement of 11.47 and 10.11% for the BOD and Q models, respectively.

Nanofibrillated cationic cellulose derivatives as flocculants for domestic wastewater treatment

Natural-based coagulants have emerged as a reliable option to implement more sustainable operations and management of wastewater treatment plants. This work aims at evaluating the use of cationic nanofibrillated celluloses (cNFC) as novel bio-based flocculants to treat domestic wastewaters by the most widely employed treatment process – coagulation/flocculation. Two cNFC samples were prepared with different charge densities and tested as coagulant/flocculants using different water characteristics. The effect of cNFCs was studied by measuring the residual turbidity and dissolved organic carbon. The aggregation mechanism and kinetics of flocculation were also evaluated. Results show that cNFC can be used as an efficient flocculant to treat medium and high DOC waters since they considerably reduce turbidity (turbidity removals varied between 66.0 % and 85.7 % for the waters and cNFCs tested) without increasing dissolved organic carbon. Instead, cNFC removed dissolved organic carbon from domestic wastewaters (between 22.1 % and 65.5 % of DOC removals for the waters and cNFCs tested), which is a novel remarkable finding and a step forward in this knowledge area. High density charged cNFC revealed superior removal capacity at lower doses than the commercial coagulant FeCl3.

Steady-State and Dynamic Simulation for Wastewater Treatment Plant Management: Case Study of Maghnia City, North-West Algeria

Given the critical importance of addressing effluent quality concerns, the present study was dedicated to developing a dynamic simulation model based on the Activated Sludge Model 1 (ASM1) of a wastewater treatment plant located in Maghnia City, Algeria. The model calibration process involved collecting and analyzing 56 samples from the plant over a period of 18 months (from July 2021 to January 2023). Thirteen physicochemical parameters were analyzed to identify the variations in their water quality over time. Stoichiometric and kinetic parameters were adjusted during the plant calibration process. These modifications resulted in a reasonable alignment with the investigated variables, enabling the accurate prediction of the wastewater treatment plants (WWTPs)’ steady-state behavior regarding the removal measurements of chemical oxygen demand (COD), total suspended solids (TSS), and ammonium (NH4-N). The model was validated using 14-day measurements spanning a 4-month duration, and the results indicated good agreement between the observed and simulated effluent variable of chemical oxygen demand (COD) with a root mean square error (RMSE) of 23%. These findings highlight the utility of the ASM1 Model in comprehending and managing the intricate dynamics of the activated sludge process in wastewater treatment plants.

Optimizing sludge dewatering efficiency with ultrasonic Treatment: Insights into Parameters, Effects, and microstructural changes

Sludge dewatering plays a critical role in the efficient and cost-effective management of wastewater treatment plants. Ultrasonic treatment has emerged as a promising technique for improving dewatering processes. This study aims to evaluate the impact of ultrasonic treatment on sludge dewatering characteristics. A series of experiments were conducted to evaluate the dewatering characteristics of sludge under ultrasonic treatment. Experimental data was collected, and the effects of ultrasonic parameters on dewatering efficiency were analyzed. Ultrasound has the capacity to disintegrate sludge flocs, liberate tightly bound water, and enhance sludge dewatering capabilities. The application of ultrasound leads to the breakdown of sludge flocs, which facilitates a substantial amount of organic acids or carbonates. This, in turn, modifies the pH value of the sludge. Additionally, ultrasound induces instantaneous high temperature and pressure within the liquid phase, consequently elevating the temperature of the sludge slurry. Optimum ultrasound energy density and duration of ultrasound treatment exist. For the sludge samples analyzed in this investigation, it was determined that the optimal ultrasonic energy density is 9.8 W, while the optimal duration of ultrasound treatment is 30 s. Excessively escalating the sound energy density or prolonging the duration of ultrasound may yield unfavorable outcomes in terms of sludge dewatering effectiveness. To enhance sludge dewatering, it is crucial to select appropriate ultrasonic energy density and duration of ultrasonic treatment. This study demonstrates the positive impact of ultrasonic treatment on the dewatering characteristics of sludge. The findings provide valuable insights into the potential of ultrasonic technology for enhancing sludge dewatering.

Conception and evaluation of anomaly detection models for monitoring analytical parameters in wastewater treatment plants

The exponential growth of technology in recent decades has led to the emergence of some challenges inherent to this growth. One of these challenges is the enormous amount of data collected by the different sensors in our society, namely in management processes such as Wastewater Treatment Plants (WWTPs). These infrastructures comprise several processes to treat wastewater and discharge clean water in water courses. Therefore, the concentration of pollutants must be below the allowable emissions limits. In this work, anomaly detection models were conceived, tuned and evaluated to monitor essential parameters such as nitrate and ammonia concentrations and pH to improve WWTP management. Four Machine Learning models were considered, particularly Local Outlier Fraction, Isolation Forest, One-Class Support Vector Machines and Long Short-Term Memory-Autoencoders (LSTM-AE), to detect anomalies in the three parameters mentioned. Through the different experiments, it was possible to verify that, in terms of F1-Score, the best candidate model for the three analyzed parameters was LSTM-AE-based, with a value consistently higher than 97%.

Microplastic removal and management strategies for wastewater treatment plants

Discharging microplastics into the environment with treated wastewater is becoming a major concern around the world. Wastewater treatment plants (WWTPs) release microplastics into terrestrial and aquatic habitats, mostly from textile, laundry, and cosmetic industries. Despite extensive research on microplastics in the environment, their removal, and WWTP management strategies, highlighting their environmental effects, little is known about microplastics' fate and behaviour during various treatment processes. Microplastics interact with treatment technologies differently due to their diverse physical and chemical characteristics, resulting in varying removal efficiency. Microplastics removed from WWTPs may accumulate in soil and harm terrestrial ecosystems. Few studies have examined the cost, energy use, and trade-offs of large-scale implementation of modern treatment methods for the removal of microplastics. To safeguard aquatic and terrestrial habitats from microplastics' contamination, focused and efficient management techniques must bridge these knowledge gaps. This review summarizes microplastic detection, collection, removal and management strategies. A compilation of treatment process studies on microplastics’ removal efficiency and their destiny and transit paths shows recent improvement. Bioremediation, membrane bioreactor (MBR), electrocoagulation, sol-gel technique, flotation, enhanced filtering, and AOPs are evaluated for microplastic removal. The fate and behaviour of microplastics in WWTPs suggest they may be secondary suppliers of microplastics to receiving ecosystems. Innovative microplastic removal strategies and technologies such as nanoparticles, microorganism-based remediation, and tertiary treatment raise issues. These new WWTP technologies are examined for feasibility, limitations, and implementation issues. Pretreatment modifies microplastic size, adsorption potential, and surface morphology to remove microplastics from WWTPs. Membrane bioreactors (MBR) can remove 99.9% of microplastics more efficiently than other approaches. MBR systems require membrane cleaning and fouling control, which raises operational and capital costs. To reduce MPs, plastic alternatives and strict controls, including microplastic waste transformation, should be prioritized. Microplastics must be controlled through monitoring policy execution and awareness.

Synapps - platform for estimating, controlling and optimizing wastewater treatment plants

Wastewater generated by the world's vast population is an important source of pollution and can accelerate the loss of biodiversity and impede the achievement of objectives set by the international community regarding the good status of water bodies. In line with the European Union's sustainable development strategy, which foresees the adoption of increasingly demanding environmental control, energy efficiency and rational management of resources measures, a project with high potential for economic valorization is being implemented, focused on design, development and validation in real conditions, to create an innovative platform for estimating, controlling and optimizing wastewater treatment plants (WWTP), called SYNAPPS. Based on the implementation of multiparametric measurement chains and the application of computational intelligence techniques (eg Big Data Analytics, Data Mining and Machine Learning), the SYNAPPS platform should be capable of providing integrated management of the various WWTP treatment processes, ensuring a high environmental, energy and operational performance, and also simplify and relieve the burden of operating this type of technical infrastructure. SYNAPPS is an R&D project approved by PO Centro, through ANI, started in January 2021 and will last for a period of 30 months, with a budget of around one million euros executed under the responsibility of the consortium formed by CTGA, a company with more than 25 years of experience in the operationalization and management of WWTP and leads the consortium, and by the non-business entities of SI&I, namely, ITeCons with relevant experience in the development of automation and dynamic control systems and in the evaluation of energy and environmental performance of processes, and ISR specializes in evaluating the energy performance of complex systems and developing control algorithms based on computational intelligence.

Comprehensive analysis of greenhouse gas emissions and emission factors in a Korean domestic wastewater treatment plant: Insights into mechanisms and pathways for climate change mitigation

This study aimed to estimate greenhouse gas (GHG) emissions from the Daejeon wastewater treatment plant (WWTP) in South Korea and identify strategies for reducing its carbon footprint. Both the worksheet-based Carbon Footprint Calculation Tool and the Massflow3 simulator were used to calculate the total GHG emissions from the “D” WWTP and identify the main sources of emissions. The results revealed that electricity consumption contributed to 45 − 48 % of emissions, while direct emissions from N2O accounted for 35–38 % of the total emissions. Furthermore, it was discovered that the WWTP's GHG emissions were 33–57 % higher compared to European sewage treatment plants that had established GHG reduction programs. To decrease the plant's carbon footprint, recommendations include optimizing nitrogen removal rates while minimizing incomplete nitrification and controlling the air flow rate in the aeration tank. Additionally, there is an emphasize on the importance of establishing a strategy to reduce indirect GHG emissions resulting from power consumption. These findings have important implications for the policy and management of WWTPs, highlighting the potential environmental and economic benefits of GHG emissions reduction.

Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm

Wastewater quality modelling plays a vital role in planning and management of wastewater treatment plants (WWTP). This paper develops a new hybrid machine learning model based on extreme learning machine (ELM) optimized by Bat algorithm (ELM-Bat) for modelling five day effluent biochemical oxygen demand (BOD5). Specifically, this hybrid model combines the Bat algorithm for model parameters optimization and the standalone ELM. The proposed model was developed using historical measured effluents wastewater quality variables, i.e., the chemical oxygen demand (COD), temperature, pH, total suspended solid (TSS), specific conductance (SC) and the wastewater flow (Q). The performances of the hybrid ELM-Bat were compared with those of the multilayer perceptron neural network (MLPNN), the random forest regression (RFR), the Gaussian process regression (GPR), the random vector functional link network (RVFL), and the multiple linear regression (MLR) models. By comparing several input variables combination, the improvement achieved in the accuracy of prediction through the hybrid ELM-Bat was quantified. All models were first calibrated using training dataset and later tested using validation and based on four performances metrics namely, root mean square error (RMSE), mean absolute error (MAE), the correlation coefficient (R), and the Nash-Sutcliffe model efficiency (NSE). In all, it is concluded that the ELM-Bat is the most accurate model when all the six input were included as input variables, and it outperforms all other benchmark models in terms of predictive accuracy, exhibiting RMSE, MAE, R and NSE values of approximately, 0.885, 0.781, 2.621, and 1.989, respectively.

Helping WWTP managers to address the volatile methylsiloxanes issue–Behaviour and complete mass balance in a conventional plant

Volatile methylsiloxanes (VMSs) are a group of additives employed in different consumer products that can affect the quality of the biogas produced in wastewater treatment plants (WWTPs). The main objective of this study is to understand the fate of different VMSs along the treatment process of a WWTP located in Aveiro (Portugal). Thus, wastewater, sludge, biogas, and air were sampled in different units for two weeks. Subsequently, these samples were extracted and analyzed by different environment-friendly protocols to obtain their VMS (L3-L5, D3-D6) concentrations and profiles. Finally, considering the different matrix flows at every sampling moment, the mass distribution of VMSs within the plant was estimated. The levels of ∑VMSs were similar to those showed in the literature (0.1–50 μg/L in entry wastewater and 1–100 μg/g dw in primary sludge). However, the entry wastewater profile showed higher variability in D3 concentrations (from non detected to 49 μg/L) than found in previous studies (0.10–1.00 μg/L), likely caused by isolated releases of this compound that could be related to industrial sources. Outdoor air samples showed a prevalence of D5, while indoor air locations were characterized by a predominance of D3 and D4. Differences in sources and the presence of an indoor air filtration system may explain this divergence. Biogas was characterized by ∑VMSs concentrations (8.00 ± 0.22 mg/m3) above the limits recommended by some engine manufacturers and mainly composed of D5 (89%). Overall, 81% of the total incoming mass of VMSs is reduced along the WWTP, being the primary decanter and the secondary treatment responsible for the highest decrease (30.6% and 29.4% of the initial mass, respectively). This reduction, however, is congener dependant. The present study demonstrates the importance of extending sampling periods and matrices (i.e., sludge and air) to improve sample representativity, time-sensitivity, and the accuracy of mass balance exercises.

A study on the carbon footprint contributions from a large wastewater treatment plant

The present work analyses aspects of the carbon footprint of a large wastewater treatment plant in central Italy. The plant mainly consists of a traditional activated sludge system along with an anaerobic digester providing a partial contribution of energy to the management. An integrated approach was adopted to evaluate the environmental sustainability of the treatment plant in terms of carbon footprint. For the assessment different sources of greenhouse gas emissions such as nitrous oxide and carbon dioxide were considered: effluent, production and transport of natural gas, energy consumption, boiler, co-generator, substrate and endogenous decays. According to the methodology adopted, energy consumption, production and transport of natural gas and N2O emissions from the effluent were found the most contributing sources of greenhouse gases. Based on this, these sources are suggested as the most relevant ones on which wastewater treatment plant managers should pay more attention when taking actions for carbon footprint mitigation. Considerations on the role of CO2 of biogenic origin (specifically the one in the biogas) in terms of sequestration options demonstrate that the analysis in this field should not be limited to the calculation and comment of non-fossil contributions to the overall balance.

A Novel Current Condition Assessment System for Sustainable Management and Operation of Wastewater Treatment Plants

Wastewater treatment is important in terms of human and environmental health in the urban water management cycle. Appropriate methods should be applied based on accurate data in order to ensure efficiency. This study developed a novel current condition assessment system to evaluate the data quality and the implementation level of wastewater treatment plant (WWTP) management and operations practices. The developed system consists of 48 components including the basic processes used in the operation of WWTPs. A unique scoring system is proposed to evaluate the system. The developed system was tested in three pilot WWTPs. The performance of Utility 2 was better than that of the other utilities in terms of data quality and implementation level of methods. However, the scores of Utilities 1 and 2 generally were close to each other. The weaknesses and strengths were determined within the scope of WWTP management in each utility.

The Management of Wastewater Treatment Plants at Bhayangkara Hospital Kendari in 2022

There is efforts to manage wastewater and liquid waste, especially at the Bhayangkara Hospital Kendari, with various treatment processes to ensure it meets the requirements. The health of the hospital environment is essential. Therefore, proper and correct waste management efforts are needed so that its disposal complies with hospital wastewater quality standards requirements. Based on this, the researchers are interested in evaluating the management of the wastewater treatment plants at the Bhayangkara Hospital Kendari. This study aimed to evaluate the effectiveness of wastewater management and quality standards. The parameters of wastewater examined were temperature, TTS (Total Suspended Solids), pH (Degree of Similarity), NH3 (Ammonia), COD (Chemical Oxygen Demand), BOD (Biochemical Oxygen Demand), and Coliform MPN. The method used in this study was descriptive by collecting data through interviews and field observations to get an overview of the evaluation of wastewater treatment at the Bhayangkara Hospital Kendari, which would then be analyzed. The results of this study indicated that the effectiveness of treatment at the Bhayangkara Hospital Kendari had met the requirements according to Decree of the Minister of Health No. 1204/Menkes/SK/X/2004 regarding the process of treating wastewater using an Anaerobic-Aerobic Biofilter system and a chemical wastewater treatment system with an anaerobic-aerobic biofilter system to produce effluent according to quality standards. The measurement results for each waste parameter were by the Decree of the Minister of Environment No. 5 of 2014 concerning hospital wastewater quality standards, namely the temperature value at the outlet (after processing) was 28, TDS at the outlet was 170 mg/L, TSS at the outlet was 45 mg /L, PH at the outlet was 7.89 mg/L, Free NH3 at the outlet was 0.057 mg/L, COD at the outlet was 15.70 mg/L, BOD at the outlet was 3.9 mg/L, and fatty oil at the outlet was <0.002 mg/L. Therefore, Bhayangkara Hospital Kendari already had quality standards and was suitable for disposal into the nearest canal.
 Keywords: Wastewater, Quality Standards, Management of Wastewater Treatment Plants, Bhayangkara Hospital Kendari

Highly efficient carbon assimilation and nitrogen/phosphorus removal facilitated by photosynthetic O2 from algal-bacterial aerobic granular sludge under controlled DO/pH operation

Reducing CO2 emission and energy consumption is crucial for the sustainable management of wastewater treatment plants (WWTPs). In this study, an algal-bacterial aerobic granular sludge (AGS) system was developed for efficient carbon (C) assimilation and nitrogen (N)/phosphorus (P) removal without the need for mechanical aeration. The photosynthetic O2 production by phototrophic organisms maintained the dissolved oxygen (DO) level at 3-4 mg/L in the bulk liquid, and an LED light control system reduced 10–30% of light energy consumption. Results showed that the biomass assimilated 52% of input dissolved total carbon (DTC), and the produced O2 simultaneously facilitated aerobic nitrification and P uptake with the coexisting phototrophs serving as a C fixer and O2 supplier. This resulted in a stably high total N removal of 81 ± 7% and an N assimilation rate of 7.55 mg/(g-MLVSS∙d) with enhanced microbial assimilation and simultaneous nitrification/denitrification. Good P removal of 92–98% was maintained during the test period at a molar ∆P/∆C ratio of 0.36 ± 0.03 and high P release and uptake rates of 10.84 ± 0.41 and 7.18 ± 0.24 mg/(g-MLVSS∙h), respectively. Photosynthetic O2 was more advantageous for N and P removal than mechanical aeration. This proposed system can contribute to a better design and sustainable operation of WWTPs using algal-bacterial AGS.

An online intelligent management method for wastewater treatment supported by coupling data-driven and mechanism models

The management of traditional wastewater treatment plants(WWTPs) mostly rely on human experience, resulting in high cost and low efficiency under conservative operation mode. This study proposed an online intelligent management method based on plan library to improve the wastewater treatment performance. The operation plans in plan library were obtained based on the mechanism model (Activated Sludge Models) of a pilot Anaerobic-Anoxic-Oxic (A2/O) process, and each plan represents the optimal operation strategy under a specific influent condition. Then the plan library was input to train a data driven model, i.e. a Multi-Layer Perceptron (MLP) regression model. The trained MLP model can be used to generate more detailed online operation strategies for wastewater treatment process under different influent conditions. After a 124 days continuous operation of the A2/O process, the results shown, the proposed method can generate online operation strategies according to influent conditions, and the main effluent indicators meet the discharge standards continuously and stably. Meanwhile, compared with the manual operation mode, the aeration energy consumption was saved about 49.4 % by using the proposed method. The mechanism model and data-driven model were combined in this study, and it has scientific value and engineering significance for intelligent management of WWTPs.