Operation Of Wastewater Treatment Plants Research Articles

Reduction of sewage sludge and N2O emissions by an Oxic Settling Anaerobic (OSA) process: The case study of Corleone (Italy) wastewater treatment plant

Biosolid management is becoming one of the most crucial issues for wastewater treatment plant (WWTP) operators. The application of the Oxic Settling Anaerobic (OSA) process allows the minimisation of excess sludge production. This study compares conventional activated sludge (CAS) and OSA layouts in a full-scale WWTP (namely, Corleone - Italy). Extensive monitoring campaigns were conducted to assess treatment performances regarding carbon and nutrient removal, greenhouse gas (GHG) emissions, excess sludge production, and biomass activity (by means of respirometric analysis). Results showed that the effluent quality consistently met the Italian discharge limits. However, with the implementation of the OSA process, there was a decrease in ammonium removal efficiency, which could be attributed to reduced nitrifier activity related to reduced biomass production and extended anaerobic conditions affecting the nitrification process. On the other hand, the OSA configuration significantly increased phosphorus removal, indicating a high phosphorus content in the resulting waste sludge. A worsening of the sludge settling properties was observed with the OSA configuration likely due to decreased EPS concentrations. The sludge production in the OSA configuration decreased by 17.3 % compared to CAS. Nitrous-oxide measurements did not show a variation between CAS and OSA configurations, confirming that the OSA process can be a suitable solution for reducing WWTP's carbon footprint.

Re-evaluation and modification of dehydrogenase activity tests in assessing microbial activity for wastewater treatment plant operation

Reliable and cost-effective methods for monitoring microbial activity are critical for process control in wastewater treatment plants. The dehydrogenase activity (DHA) test has been recognized as an efficient measure of biological activity due to its simplicity and broad applicability. Nevertheless, the existing DHA test methods suffer from imperfections and are difficult to implement as routine monitoring techniques. In this work, an accurate and cost-effective modified DHA approach was developed and the procedure for the DHA test was critically evaluated with respect to the standard construction, sample pretreatment, incubation and extraction conditions. The feasibility of the modified DHA test was demonstrated by comparison with the oxygen uptake rate and adenosine triphosphate in a sequencing batch reactor. The sensitivities of the two typical tetrazolium salts to toxicant inhibition by heavy metals and antibiotics were compared, revealing that 2,3,5-triphenyltetrazolium chloride (TTC) exhibited a higher sensitivity. Furthermore, the sensitivity mechanism of the two DHA tests was elucidated through electrochemical experiments, theoretical analysis and molecular simulations. Both tetrazolium salts were found to be effective artificial electron acceptors due to their low redox potentials. Molecular docking simulations revealed that TTC could outperform other tetrazolium salts in accepting electrons and hydrogens from dehydrogenase. Overall, the modified DHA approach presents an accurate and cost-effective way to measure microbial activity, making it a practical tool for wastewater treatment plants.

Performance evaluation of wastewater treatment plants in Taman’s public health center in Sidoarjo District

Introduction: In general, wastewater produced by Public health centers contains many microorganisms, pathogenic compounds, and hazardous and toxic chemicals that can cause environmental pollution and transmit various diseases.1 The danger posed by the liquid waste produced is quite large, so it is necessary to have a wastewater treatment process before it is disposed of. Wastewater treatment is carried out using a wastewater treatment plant (WWTP). Thus, this study aims to evaluate the performance, environment and economy of WWTPs in Public health centers. Methods: This research uses descriptive quantitative research methods. The inclusion criteria were the laboratory data results in 2018 and 2019. The exclusion criteria were the data needed to be completed. The research variable was the performance of the WWTP on wastewater quality. The data analysis of the laboratory test results is analyzed and compared with the East Java Governor Regulation Number 72 of 2013 concerning Wastewater Quality Standards for Business and Hospital Activities. Results: The results of laboratory tests in 2018 showed the parameters by the requirements according to regulations, such as temperature, pH, BOD5, COD and TSS. At the same time, the parameters were not following requirements according to rules such as free NH3-N, PO4 and MPN-coliform bacteria. Data from laboratory test results in 2019 showed requirements according to regulations: temperature, pH, BOD5, and TSS. The effects on the COD parameter were seen only in July that they did not follow the requirements, while most of the results for the NH3-N, PO4 and MPN-free coli groups did not meet the criteria. Conclusion: Based on the research done in this study, the following conclusions can be drawn: The results of the performance evaluation of the WWTP at the Taman Public health center are included in the category that meets the stipulated regulatory requirements.

Advanced steam explosion as thermal hydrolysis process for high solids anaerobic digestion and enhanced methane yield: Proof of concept

Sewage sludge management is one of the major issues faced by wastewater treatment plant (WWTP) operators. Though anaerobic digestion (AD) is one of the most preferred sludge management techniques, the complex microbial floc structure of the sludge creates physical and chemical barriers that limit the rate of microbial degradation resulting in a longer hydraulic retention time (HRT) of 25–30 days. The thermal hydrolysis process (THP) can be used as a pretreatment technique to enhance the rate of biodegradation which results in reduced retention time and increased methane yields. This work investigated the effects of the THP on the semi-continuous anaerobic digestion of high solids sludge (15% total solids, TS) obtained from a municipal wastewater treatment plant (WWTP) operating at higher solids retention time (SRT) of 40 days. The thermal hydrolysis process achieved the effective release of organics into the liquid phase resulting in 37% chemical oxygen demand (COD) solubilization. Two semi-continuous digesters, one fed with untreated sludge (control digester, 5% TS) to mimic the conventional anaerobic digester and the other fed with THP sludge (160 °C, 30 min, 6 bar, 10% TS) were operated under a mesophilic regime and a hydraulic retention time (HRT) of 15 days. The methane yield from the THP digester (0.366 ± 0.010 m3/kg VSadded, 70 ± 1.13% CH4) was 47% higher than the control digester (0.248 ± 0.013 m3/kg VSadded). The volatile solids removal (VSR) was 51 ± 0.6% in the THP digester, i.e., 43% higher over control digester (35.7 ± 1.3%). The fecal coliforms concentration in the digestate of control and THP digesters were 9200 and 200 MPN/g, respectively. Thus, the THP sludge meets the Class A biosolids criteria (<1000MPN/g). THP integrated anaerobic digestion achieved higher solids reduction with enhanced energy recovery, high solids loading with shorter retention time, and generated Class A biosolids.

Modelling greenhouse gas emissions from biological wastewater treatment by GPS-X: The full-scale case study of Corleone (Italy)

Greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs) can affect climate change and must be measured and reduced. Mathematical modelling is an attractive solution to get a tool for GHG mitigation. However, although many efforts have been made to create reliable tools that can simulate “sustainable” full-scale WWTP operation, these studies are not considered complete enough to include GHG emissions and energy consumption of biological processes under long-term dynamic conditions. In this study, activated sludge model no. 1 (ASM1) was modified to model nitrous oxide (N2O) emissions with a plant-wide modelling approach. The model is novel compared to the state of the art since it includes three steps denitrification, all N2O production pathways and its stripping in an ASM1. The model has been calibrated and validated through long-term water quality and short-term N2O emissions data collected from Corleone (Italy) WWTP. Different dissolved oxygen (DO) concentrations and return sludge (RAS) ratios were tested with dynamic simulations to optimise the full-scale WWTP. The scenarios have been compared synergistically with effluent quality, direct GHG emissions, and energy footprint by the water-energy‑carbon coupling index (WECCI). This modelling study is novel as it fully covers long-term calibration/validation of the model with N2O measurements and tests the dynamic optimisation. Decision-makers and operators can use this new model to optimise GHG emissions and treatment costs.

Prediction of Wastewater Treatment Plant Effluent Water Quality Using Recurrent Neural Network (RNN) Models

Artificial Intelligence (AI) has recently emerged as a powerful tool with versatile applications spanning various domains. AI replicates human intelligence processes through machinery and computer systems, finding utility in expert systems, image and speech recognition, machine vision, and natural language processing (NLP). One notable area with limited exploration pertains to using deep learning models, specifically Recurrent Neural Networks (RNNs), for predicting water quality in wastewater treatment plants (WWTPs). RNNs are purpose-built for handling sequential data, featuring a feedback mechanism. However, standard RNNs may exhibit limitations in accommodating both short-term and long-term dependencies when addressing intricate time series problems. The solution to this challenge lies in adopting Long Short-Term Memory (LSTM) cells, known for their inherent memory management through a ‘forget gate’ mechanism. In general, LSTM architecture demonstrates superior performance. WWTP data represent a historical series influenced by fluctuating environmental conditions. This study employs simple RNNs and LSTM architecture to construct prediction models for effluent parameters, systematically assessing their performance through various training data scenarios and model architectures. The primary objective was to determine the most suitable WWTP dataset model. The study revealed that an epoch setting of 50 and a batch size of 100 yielded the lowest training time and root mean square error (RMSE) values for both RNN and LSTM models. Furthermore, when these models are applied to predict effluent parameters, they exhibit precise RMSE values for all parameters. The study results can be applied to detect potential upsets in WWTP operations.

Effect of co-digestion and hydrodynamic disintegration on the methane potential of sewage sludge and organic fraction of municipal solid waste with consideration of the carbon footprint

The study objective was to investigate the applicability of co-digestion and/or hydrodynamic disintegration as potential methods of methane production intensification from sewage sludge (SS) and the organic fraction of municipal solid waste (OFMSW) with consideration of carbon footprint (CF) assessment. Methane production was determined by means of biochemical methane potential tests. For co-digestion, the highest increase in methane production (53.8 %) was observed for the SS:OFMSW mixing ratio 40:60. Hydrodynamic disintegration at all tested energy densities (10, 30 and 60 kJ/L) caused release of soluble organic compounds from both substrates, although accelerated methane production was only observed at 10 kJ/L. An increase in methane potential for substrates disintegrated separately did not imply analogical results for the disintegration of their mixture. Within the assumed limits, CF per 1 Nm3 of produced methane, CF per 1 t of wet feedstock for the digester, and estimated CF per 1 kWh produced were also calculated. Comparable values of all indicators were obtained for monodigestion and co-digestion. The highest CF indices were achieved for monodigestion of disintegrated SS and increased significantly with applied energy density. The obtained results can serve as valuable material for operators of wastewater treatment plants facilitating the selection of sustainable methods of increasing renewable energy production.

Integrating Reliability and Energy Efficiency Assessments for Pinpointing Actionable Strategies for Enhanced Performance of Urban Wastewater Treatment Plants

By leveraging performance assessment systems (PASs) and incorporating targeted strategies, utilities can enhance the overall effectiveness, reliability, efficiency, and environmental performance of their wastewater treatment facilities. This work presents the results obtained from a comprehensive analysis of treatment reliability and energy performance of three wastewater treatment plants (WWTPs). The results allowed identifying, for each WWTP, improvement needs related to the aeration energy requirements, as well as to determine the effluent concentration targets required to achieve higher reliability levels while potentially reducing running costs. By analysing reliability combined with energy efficiency, this methodology allowed identifying the WWTPs’ performance-limiting stages or dysfunctions that affect both effectiveness and efficiency, to estimate the investment needs and prioritize the rehabilitation or even retrofitting of facilities’ assets.

Strengthen high-loading operation of wastewater treatment plants by composite micron powder carrier: Microscale control of carbon, nitrogen, and sulfur metabolic pathways

The concentration of activated sludge is a crucial factor influencing the capacity and efficiency of sewage wastewater treatment plants (WWTPs). However, high sludge concentrations can lead to sludge loss in the secondary sedimentation tank, resulting in reduced processing capacity, particularly during low-temperature stages and sludge bulking. This study investigated the impact of adding composite micron powder carriers (CMPC) in high-concentration powder carrier biofluidized bed (HPB) technology to the biochemical units of WWTPs on sludge concentration and settling performance. For the traditional activated sludge method (ASM), its hydraulic retention time (HRT) was 8 h, with an average effluent total nitrogen (TN) of 15.14 mg/L. Sludge bulking was prone to occur in low-temperature environments, resulting in a high average sludge volume index (SVI) of 560 mL/g. Conversely, with a CMPC dosage of 4 g/L, the HRT of HPB technology was 4.8 h, and the average effluent TN was 11.40 mg/L, with a removal efficiency of 67.43 %. During operation of HPB technology under high sludge concentration conditions (8 g/L), the average SVI remained at 85 mL/g, indicating excellent settling characteristics. Moreover, in the sequencing batch reactor (SBR), the SVI value of bulking sludge decreased from the original 695 to 111 mL/g by the 9th day of operation with the CMPC dosage of 2 g/L. At the same time, the filamentous bacteria almost disappeared, suggesting that CMPC inhibit the growth of filamentous bacteria. Metagenomic analysis demonstrated that CPMC enhance the utilization of small molecular fatty acids in activated sludge and promote electron transfer between nitrate and nitrite, thereby improving wastewater treatment capacity. Additionally, CMPC enhanced the relative abundance of Saprospiraceae in sludge, which accelerate the degradation of polysaccharides in extracellular polymeric substances, weaken sludge's hydrophilic properties, and improve sludge's settling performance. Overall, these findings suggested that CMPC effectively strengthen the high-loading operation of WWTPs by improving sludge concentration and sedimentation performance.

A Review of Computational Modeling in Wastewater Treatment Processes.

Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.

Enhancing the Performance of a Simulated WWTP: Comparative Analysis of Control Strategies for the BSM2 Model

This study aimed to improve the performance of a wastewater treatment plant (WWTP) simulated with Benchmark Model No. 2 (BSM2). To achieve this objective, three control strategies were implemented and tested. The first control strategy aimed to maintain the concentration of nitrate and nitrite nitrogen (SNO) by controlling the external carbon flowrate (strategy A1), and the second control strategy aimed to maintain the ammonia and ammonium nitrogen (SNH) at a desired level with the use of a cascade controller (strategy A2). The third strategy was applied to control the total suspended solids (TSS) (strategy A3). Combinations of these strategies were considered (B1, B2, and B3 strategies), as well as the use of all three together (strategy C1). The control strategies presented in this paper were compared to the default control strategy of BSM2 to validate and identify the one that provided the best performance. The results revealed that the B1 strategy was the most environmentally friendly, while C1 obtained the highest overall performance. Several Monte Carlo simulations were performed for the validated control strategies, to identify the optimal setpoint values. For the C1 strategy, a second method of optimization regarding polynomial interpolation was considered. The applied optimization methods provided the optimal reference values for the PI (proportional integral) controllers.

Simultaneous sensor fault diagnosis and reconstruction for intelligent monitoring in wastewater treatment plants: An explainable deep multi-task learning model

Sensor malfunctions in wastewater treatment plants (WWTPs) significantly disrupt process control and energy usage, highlighting the critical need for effective sensor fault diagnosis and reconstruction. This study aims to introduce a novel application of a multi-task learning network in WWTPs to address the challenge of sensor malfunction by enabling simultaneous fault diagnosis and reconstruction. The proposed approach introduces an explainable deep multi-task learning autoencoder network (DMTL-UNet), which effectively allows sharing information among tasks through attention gates and residual connections. The effectiveness of the DMTL-UNet model is validated using a real-world dataset from a WWTP in South Korea. The results demonstrate the remarkable capability of the DMTL-UNet model in accurately diagnosing multiple faults (F1-score = 99.08 %) and achieving superior reconstruction performance (RMSE = 31.1175 mg/L) for faulty WWTP sensors. Moreover, implementing the DMTL-UNet model offers significant energy savings of 37.44 %, corresponding to a reduction in the aeriation cost by 1154.91 USD. Therefore, implementing the DMTL-UNet model for calibrating faulty sensors can enhance sensor reliability, improve maintenance practices, and contribute to the sustainable operation of WWTPs.

Natural zeolite enhances anaerobic digestion of waste activated sludge: Insights into the performance and the role of biofilm

Anaerobic digestion is widely employed for the treatment of waste activated sludge (WAS) due to its advantages like simultaneous energy recovery and sludge stabilization, promoting carbon-neutral operation of wastewater treatment plants. Natural zeolite, a low-cost and eco-friendly additive, has the potential to improve methane production from anaerobic digestion. This study investigated the effects of natural zeolite on anaerobic digestion when the substrate was WAS. It was found that methane production potential in response to natural zeolite was dosage-dependent. The optimal dosage was 0.1 g zeolite/g volatile suspended solids (VSS), with a methane yield of 181.89 ± 6.75 mL/g VSS, which increased by 20.1% compared to that of the control. Although the methane yields with other dosages of natural zeolite were higher than that of control, they were lesser than that with 0.1 g zeolite/g VSS. Natural zeolite affected transfer and conversion of proteins much more than polysaccharides in liquid phase and extracellular polymeric substances. In anaerobic digestion, natural zeolite had with little effects on WAS solubilization, while it improved hydrolysis, acidification, and methanogenesis. The dosages of natural zeolite did have significant effects on bacterial communities in biofilm rather than suspension, while the archaeal communities in biofilm and suspension were all greatly related to natural zeolite dosages. The developed biofilms promoted richness and functionality of microbial communities. The syntrophic metabolism relationships between methanogens and bacteria were improved, which was proved by selective enrichment of Methanosarcina, Syntrophomonas, and Petrimonas. The findings of this work provided some new solutions for promoting methane production from WAS, and the roles of natural zeolite in anaerobic digestion.

Improving wastewater treatment plant performance: an ANN-based predictive model for managing average daily overflow and resource allocation optimization using Tabu search

Improving wastewater treatment plant performance: an ANN-based predictive model for managing average daily overflow and resource allocation optimization using Tabu search

Assessing eco-efficiency of wastewater treatment plants: A cross-evaluation strategy

Evaluating the eco-efficiency of wastewater treatment plants (WWTPs) is crucial for enhancing environmental and economic performance in the water utility sector. Previous studies in this area estimated WWTP eco-efficiency through self-evaluation, which might have led to overestimation and biased policy recommendations. To address these issues, this study applies a cross-evaluation strategy, combining self-evaluation and peer-evaluation, to assess the eco-efficiency of WWTPs. The empirical application focuses on a sample of Spanish WWTPs, yielding the following key findings. Average eco-efficiency scores were 0.353 and 0.230, for self-evaluation and global peer-evaluation approaches, respectively, confirming the overestimation of eco-efficiency scores based on self-evaluation. If WWTPs were eco-efficient, they could potentially reduce greenhouse gas (GHG) emissions by up to 0.39 kg CO2eq/year. The application of reliable methods, such as peer-evaluation, for eco-efficiency assessment of WWTPs provides water regulators with a comprehensive understanding of the environmental and economic performance of WWTPs. This knowledge guides decision-making, policy development, and resource allocation, facilitating sustainable and efficient wastewater management practices.

Full-scale partial nitritation and anammox (PN/A) application in removing nitrogen from industrial wastewater: Performance and life cycle assessment

Anaerobic ammonium oxidation (anammox) is an innovative nitrogen removal process that is widely used in wastewater treatment due to its energy-saving potential and ability to reduce carbon emissions. However, research on the application of anammox in full-scale industrial wastewater treatment plants (WWTPs) is limited, and its environmental impacts are yet to be fully understood. This study evaluates the performance and environmental burdens of one-stage partial nitritation and anammox (PN/A) in a full-scale WWTP treating coal chemical wastewater, with different demonstration periods. Compared with conventional nitrification–denitrification, the energy footprint and carbon footprint of the PN/A process were significantly lower and effluent quality was improved. Based on the four-quadrant clustering method, the overall performance of the studied WWTP achieved the greatest synergy during the stable operation period. Life cycle assessment has shown that the PN/A process significantly reduces the system’s contribution to environmental indicators. According to the sensitivity analysis, the fluctuations in energy consumption and chemical usage have significant environmental impacts, with differential sensitivity in the environmental impacts of two demonstration periods. Therefore, considering the specific situation of the WWTPs, optimizing the parameters/indicators most sensitive to environmental impact is essential to reducing environmental loads. Importantly, this work demonstrated that PN/A can improve the operational performance of full-scale industrial WWTPs with lower environmental burdens, providing valuable guidance for the development of sustainable wastewater treatment processes.

Role of a typical swine liquid manure treatment plant in reducing elements of antibiotic resistance.

Biological treatment of swine liquid manure may be a favorable environment for the enrichment of bacteria carrying antibiotic resistance genes (ARGs), raising the alert about this public health problem. The present work sought to investigate the performance of a swine wastewater treatment plant (SWWTP), composed of a covered lagoon biodigester (CLB) followed by three facultative ponds, in the removal of usual pollutants, antibiotics, ARGs (blaTEM, ermB, qnrB, sul1, and tetA), and intI1. The SWWTP promoted a 70% of organic matter removal, mainly by the digester unit. The facultative ponds stood out in the solids' retention carried from the anaerobic stage and contributed to ammonia volatilization. The detected antibiotic in the raw wastewater was norfloxacin (< 0.79 to 60.55 μg L-1), andthe SWWTP seems to equalize peaks of norfloxacin variation probably due to sludge adsorption. CLB reduced the absolute abundance of ARGs by up to 2.5 log, while the facultative stage does not seem to improve the quality of the final effluent in terms of resistance elements. Considering the relative abundances, the reduction rates of total and ARG-carrying bacteria appear to be similar. Finally, correlation tests also revealed that organic matter and solids control in liquid manure treatment systems could help reduce the spread of ARGs after the waste final disposal.

Enhanced treatment of synthetic wastewater by bioaugmentation with a constructed consortium

Bioaugmentation by adding well-functioning mixed microorganism consortia represents a potentially useful approach to improve contaminant removal in wastewater treatment plants (WWTPs). However, unfavorable environmental conditions (i.e., low temperatures) can severely inhibit microbial activity, drawing our attention to constructing cold-tolerant microorganism preparations and investigating their availability in practical applications. Here we screened four in situ functional isolates from the activated sludge of secondary sedimentation tanks in WWTPs to construct a psychrophilic microbial consortium, which was used to perform bioaugmentation for enhanced removal of nitrogen and phosphorus under low temperatures. The consortium was established by cocultivation of four isolates, characterized by 16 S rRNA as the COD-degrading bacterium Aeromonas sp. Z3, aerobic denitrifying bacterium Acinetobacter sp. HF9, nitrifying bacterium Klebsiella sp. X8, and polyphosphate-accumulating bacterium Pseudomonas sp. PC5 respectively. The microorganism preparation was composed of Z3, HF9, X8, and PC5 under the ratio of 1: 1: 3: 1, which can exert optimal pollutant removal under the conditions of 12 °C, 6.0–9.0 pH, 120–200 r‧min−1, and a dosage of 5% (V/V). A 30-day continuous operation of the bioaugmented and control sequencing batch reactors (SBRs) was investigated, and the bioaugmented SBR showed a shorter start-up stage and a more stable operating situation. Compared to the control SBR, the COD, NH4+-N, TN, and TP removal efficiency of the bioaugmented SBR increased by an average of 7.95%, 9.05%, 9.54%, and 7.45% respectively. The analysis of the microbial community revealed that the introduced isolates were dominant in the activated sludge and that functional taxa such as Proteobacteria, Bacteroidota, and Actinobacteria were further enriched after a period of bioaugmentation. The study provides some basis and guidance for the practical application of how to strengthen the stable operation of WWTPs under low temperatures.

Static calibration of wastewater treatment plant models: Investigating calibration processes and objective functions

Mathematical modeling provides a reliable tool for optimal management and operation of wastewater treatment plants (WWTPs). Accurate calibration is crucial for producing reliable results, and static calibration is a critical initial step for this purpose, ensuring enough accuracy and minimizing later difficulties. The two main areas to investigate in static calibration are the appropriate calibration process and objective function. This study proposes a three-stage methodology for static calibration of model parameters. Nine different calibration objective functions considering various target variables are investigated to evaluate how different objective functions can affect the calibration process accuracy. The proposed approach is applied in a real WWTP with a carousel oxidation ditch for secondary treatment. Benchmark Simulation Model No.2 (BSM2) is modified to be used for oxidation ditch modeling. The calibration results show less than 3 % prediction error for COD, TN, and TSS, and 6.7 % mean overall error. The validation resulted in 22.5 %, 10.6 %, and 31.5 % MARE (Mean Absolute Relative Error) for effluent COD, TN, and, TSS, respectively, indicating successful calibration. Comparing different objective functions indicates that in static calibration, the objective functions that consider more targets usually yield more accurate results in a relatively acceptable time.

Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment

The aim of wastewater treatment plants (WWTPs) is to clean wastewater before it is discharged into the environment. Real-time monitoring and control will become more essential as the regulations for effluent discharges are likely to become stricter in the future. Model-based soft sensors provide a promising solution for estimating important process variables such as chemical oxygen demand (COD) and help in predicting the performance of WWTPs. This paper explores the possibility of using interpretable model structures for monitoring the influent and predicting the effluent of paper mill WWTPs by systematically finding the best model parameters using an exhaustive algorithm. Experimentation was conducted with regression models such as multiple linear regression (MLR) and partial least squares regression (PLSR), as well as LASSO regression with a nonlinear scaling function to account for nonlinearities. Some autoregressive time series models were also built. The results showed decent modelling accuracy when tested with test data acquired from a wastewater treatment process. The most notable test results included the autoregressive model with exogenous inputs for influent COD (correlation 0.89, mean absolute percentage error 8.1%) and a PLSR model for effluent COD prediction (correlation 0.77, mean absolute percentage error 7.6%) with 20 h prediction horizon. The results show that these models are accurate enough for real-time monitoring and prediction in an industrial WWTP.