Operation Of Wastewater Treatment Plants Research Articles

Multi-Objective Optimization Based on Simulation Integrated Pareto Analysis to Achieve Low-Carbon and Economical Operation of a Wastewater Treatment Plant

It is essential to reduce carbon emissions in wastewater treatment plants (WWTPs) to achieve carbon neutrality in society. However, current optimization of WWTPs prioritizes the operation cost index (OCI) and effluent quality index (EQI) over greenhouse gas (GHG) emissions. This study aims to conduct a multi-objective optimization of a WWTP, considering GHG emissions, EQI, and OCI. The anaerobic-anoxic-oxic integrated membrane bioreactor (AAO-MBR) process in an actual WWTP was selected as a typical case, tens of thousands of scenarios with combinations of six operational parameters (dissolved oxygen (DO), external carbon resource (ECR), poly aluminum chloride (PAC), internal reflux ratio (IRR), external reflux ratio (ERR), and sludge discharge (SD)) were simulated by GPS-X software (Hydromantics 8.0.1). It was shown that ECR has the greatest impact on optimization objectives. In the optimal scenario, the main parameters of ATDO, MTDO, IRR, and ERR were 0.1 mg/L, 4 mg/L, 50%, and 100%, respectively. The EQI, OCI, and GHG of the best scenario were 0.046 kg/m3, 0.27 ¥/m3, and 0.51 kgCO2/m3, which were 2.1%, 72.2%, and 34.6% better than the current situation of the case WWTP, respectively. This study provides an effective method for realizing low-carbon and economical operation of WWTPs.

Deconjugation potentials of natural estrogen conjugates in sewage and wastewater treatment plant: New insights from model prediction and on-site investigations

Natural estrogen conjugates play important roles in municipal wastewater treatment plant (WWTP), but their deconjugation potentials are poorly understood. This work is the first to investigate the relationships between the enzyme activities of arylsulfatase/β-glucuronidase and deconjugation potentials of natural estrogen conjugates. This work led to three important findings. First, the enzyme activity of β-glucuronidase in sewage is far higher than that of arylsulfatase, while their corresponding activities in activated sludge were similar. Second, a model based on β-glucuronidase could successfully predict the deconjugation potentials of natural estrogen glucuronide conjugates in sewage. Third, the enzyme activity of arylsulfatase in sewage was too low to lead to evident deconjugation of sulfate conjugates, which means that the deconjugation rate of estrogen sulfates can be regarded as zero. By comparing their theoretical removal based on enzyme activity and on-site investigation, it is reasonable to conclude that reverse deconjugation of estrogen conjugates (i.e., conjugation of natural estrogens to form conjugated estrogens) likely exist in WWTP, which explains well why natural estrogen conjugates cannot be effectively removed in WWTP. Meanwhile, this work provides new insights how to improve the removal performance of WWTP on natural estrogen conjugates. SynopsisThis work is the first to show how arylsulfatase/β-glucuronidase could affect deconjugation of natural estrogen conjugates and possible way to enhance their removal in wastewater treatment plant.

Machine learning assisted combined systems of wastewater treatment plants with constructed wetlands optimal decision-making

This study proposed an efficient framework for optimizing the design and operation of combined systems of wastewater treatment plants (WWTP) and constructed wetlands (CW). The framework coupled a WWTP model with a CW model and used a multi-objective evolutionary algorithm to identify trade-offs between energy consumption, effluent quality, and construction cost. Compared to traditional design and management approaches, the framework achieved a 27 % reduction in WWTP energy consumption or a 44 % reduction in CW cost while meeting strict effluent discharge limits for Chinese WWTP. The framework also identified feasible decision variable ranges and demonstrated the impact of different optimization strategies on system performance. Furthermore, the contributions of WWTP and CW in pollutant degradation were analyzed. Overall, the proposed framework offers a highly efficient and cost-effective solution for optimizing the design and operation of a combined WWTP and CW system.

Should wastewater treatment plants' operational mode radically change to minimize GHG emissions?

The operation of municipal wastewater treatment plants (WWTPs) invariably results in significant emission of greenhouse gases (i.e., CH4, N2O, and CO2) into the atmosphere. We propose to consider a radical change in the way municipal WWTPs are operated, with the aim of minimizing GHG emissions while recycling most of the nutrient mass. The means to this end are to reduce the WWTP energy demand while maximizing the recovery of resources (phosphorus, ammonia, methane). The suggested concept involves operating the activated sludge process at a low sludge retention time (SRT < 2 d), i.e., under conditions that maximize the heterotrophic mass yield and eliminate nitrification. The ammonia concentration that remains in the water (considering N in the excess sludge and struvite production in the sludge-dewatering supernatant line) would be separated from the WWTP effluents using a unique ion-exchange material (ZnHCF), which would be regenerated using a low-volume 4 M NaCl solution. The ammonia would be then stripped at high pH and re-adsorbed by an acidic solution for reuse as fertilizer. The high bacterial yield and lack of nitrification in the aerobic step are expected to boost methane yield 3–4-fold, induce lower oxygen consumption, and most importantly, yield much lower N2O release. An approximate energy mass balance shows the concept to merit further consideration, owing to the potential significant reduction in N2O(g) emissions and recovery of resources. Empirical work followed by LCA is required to corroborate the hypothesis presented herein.

Application of a water-energy-carbon coupling index to evaluate the long-term operational stability of the anaerobic-anoxic-oxic-membrane bioreactor (A2/O-MBR) process under the influence of rainstorms

In the context of global climate change, sudden rainstorms and typhoons induce fluctuations in hydraulic shocks to wastewater treatment plants (WWTPs) in coastal areas, causing two challenges of stable effluent quality and low-carbon operation. We established a quantitative evaluation method for resistance of wastewater treatment processes to hydraulic shocks based on the water-energy-carbon nexus using operational data from a WWTP in southeast coastal China from July 2018 to December 2022. The effects of hydraulic shocks on the operational stability of the anaerobic-anoxic-oxic-membrane bioreactor (A2/O-MBR) process were analyzed following five steps. The results showed that the gray water footprint (GWF) of the process was 9.3% lower than that of the A2/O process. The energy footprint (ENF) and carbon footprint (CF) were approximately 2.1 times and 1.7 times higher than those of the A2/O process, respectively. The resistance to hydraulic shocks of the A2/O-MBR process is approximately 5.5 times higher than that of the A2/O process. In conclusion, the A2/O-MBR process exhibits higher process operational stability when subjected to hydraulic shocks, which is more conducive to the efficient and stable operation of WWTPs in rainstorm and typhoon-prone areas. The evaluation methodology provides qualitative technical support for selecting upgrading processes for WWTPs in different regions.

Evaluating the environmental and economic performance of biological and advanced biological wastewater treatment plants by life cycle assessment and life cycle costing.

The primary objective of this study is to assess and establish benchmarks for environmental and economic sustainability of biological and advanced biological wastewater treatment plants (WWTPs) with different treatment technologies and characteristics. Furthermore, the study aims to determine the beneficial role of WWTPs to reduction of eutrophication potential. Environmental and economic sustainability of ten municipal WWTPs was assessed using life cycle assessment (LCA) and life cycle costing (LCC). In the first section of the study, LCA was performed to determine the environmental performance of the WWTPs. Furthermore, net environmental benefit (NEB) approach was implemented to reveal the beneficial role of WWTPs to eutrophication potential. In the subsequent section, LCA-based LCC was conducted by integrating the results of LCA. The most significant environmental impact was determined as marine aquatic ecotoxicity, which is highly affected from the generation and transmission of electricity consumed in the WWTPs. Wastewater recovery and co-incineration of sewage sludge in cement kiln ensure significant environmental savings on ozone layer depletion, human toxicity, acidification, photochemical oxidation, and abiotic depletion (fossil fuel) potential. Considering NEB approach, the highest NEB values were found for the WWTPs with the higher organic load and nutrient concentration in the influent. The results of LCC in WWTPs varied between 0.21 and 0.53 €/m3. External (environmental) costs were evaluated higher than internal (operational) costs for all selected WWTPs. While eutrophication was the highest among environmental costs, electricity cost was the highest among operational costs for almost all WWTPs.

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.

Using Surrogate Parameters to Enhance Monitoring of Community Wastewater Management System Performance for Sustainable Operations.

Community wastewater management systems (CWMS) are small-scale wastewater treatment systems typically in regional and rural areas with less sophisticated treatment processes and often managed by local governments or communities. Research and industrial applications have demonstrated that online UV-Vis sensors have great potential for improving wastewater monitoring and treatment processes. Existing studies on the development of surrogate parameters with models from spectral data for wastewater were largely limited to lab-based. In contrast, industrial applications of these sensors have primarily targeted large wastewater treatment plants (WWTPs), leaving a gap in research for small-scale WWTPs. This paper demonstrates the suitability of using a field-based online UV-Vis sensor combined with advanced data analytics for CWMSs as an early warning for process upset to support sustainable operations. An industry case study is provided to demonstrate the development of surrogate monitoring parameters for total suspended solids (TSSs) and chemical oxygen demand (COD) using the UV-Vis spectral data from an online UV-Vis sensor. Absorbances at a wavelength of 625 nm (UV625) and absorbances at a wavelength of 265 nm (UV265) were identified as surrogate parameters to measure TSSs and COD, respectively. This study contributes to the improvement of WWTP performance with a continuous monitoring system by developing a process monitoring framework and optimization strategy.

Is Africa Ready to Use Phycoremediation to Treat Domestic Wastewater as an Alternative Natural Base Solution? A Case Study

This review outlines the potential of phycoremediation as a natural, cost-effective solution for domestic wastewater treatment in Africa, particularly focusing on its application in less densely populated and rural areas. The urgency of improving sanitation access, a key objective in both the Millennium Development Goals (2000–2015) and the Sustainable Development Goals (2015–2030), is underscored by the fact that half of Africa’s population suffers from diseases linked to inadequate water and sanitation facilities. South Africa, a focal point of this study, faces significant challenges in wastewater management. These include the limited capacity of wastewater treatment plants to handle the burgeoning wastewater volumes due to population growth, unregulated discharges causing fluctuating pollution levels, and high operational costs leading to improper sludge disposal and odor issues. Compounding these problems are frequent power outages, financial constraints impacting wastewater treatment plant operations and maintenance across Africa, and a lack of skilled personnel to manage these facilities.

Toxic effects of polycyclic aromatic hydrocarbons on the two-sludge system: Combined functional gene, resistance gene and microbial community assessment

Polycyclic aromatic hydrocarbons (PAHs) were a class of persistent organic matter, and contained PAHs wastewater was discharged into wastewater treatment plants (WWTPs) finally, which could greatly affect their operational efficiency. The toxic effects of PAHs on the two-sludge system were assessed from functional genes, resistance genes, and microbial community. Three types of PAHs including Naphthalene (NAP), Anthracene (ANT), and Phenanthrene (PHE) were individually introduced into three separate two-sludge systems. Results showed that three types of PAHs did not adversely affect on nitrification performance, but exhibited a significant toxic effect on denitrification and phosphorus (PO43--P) removal. The effluent NH4+-N was almost 0 mg/L in all three systems, the highest total nitrogen (TN) removal rate was achieved at 90.88% in the PHE system, whereas the lowest rate was 72.46% in the ANT system. Concurrently, the PO43--P removal rate decreased from 93.10% to 51.22% in the ANT system. Functional genes, such as NORB and NIRS, and resistance genes such as PAH-RHD GNF/R and NAH-F/NH-R, were enriched in the PHE and NAP system, contributing to improved pollutant removal. However, the NIRK and PAH-RHD GPF/R were more abundant in the ANT system, resulting in varied pollutant removal capabilities. The composition of the microbial community was also influenced by PAH exposure with some specific functional bacteria being identified across the different systems. Among the PAHs tested, ANT was found to exert the highest toxicity towards the two-sludge system, surpassing the toxic effects of NAP and PHE. This study highlights the importance of considering PAH toxicity in the context of WWTPs operations and the potential need for tailored strategies to mitigate their impact on biological processes.

Integrated supervisory fuzzy control framework for biological wastewater treatment plants operation and their effect on GHG emissions

In this research, the effect on the greenhouse gases (GHG) emissions from biological wastewater treatment plants (WWTP) operation by implementing an integrated supervisory control framework on the bench-mark simulation model 2 gas (BSM2G) platform is studied. Ammonia based aeration control is considered for improving the efficiency of the nitrification process and also nitrate control is considered to enhance the performance of the overall plant. Different control strategies are proposed by considering PI control, Model Predictive control and Fuzzy controllers. A notable change is observed in GHG emissions due to bio-treatment because of the impact of control strategies on N2O emissions during denitrification with respect to the nitration where dissolved oxygen (DO) control alone is considered. In addition, when the nitrate loop is considered, a better change is observed in the overall plant performance. On comparison with other approaches, the proposed control strategies resulted in a reduction in the GHG emissions with marginal effect on the other performance indexes effluent quality index (EQI) and operational cost index (OCI).

Performance of full-scale rural wastewater treatment plants in the reduction of antibiotic-resistant bacteria and antibiotic resistance genes from small-city effluents

The main objective of this study was to evaluate the performance of full-scale rural wastewater treatment plants (WWTPs) in the reduction of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) from small-city effluents. Twenty full-scale WWTPs in rural Chile with different biological treatment technologies (vermifiltration (VF), activated sludge (AS) and biodisc (BD)) and disinfection treatments (chlorination (Cl) and UV irradiation (UV)) were monitored and studied in two campaigns: Campaign 1 (20 WWTPs) and Campaign 2 (6 WWTPs). In both campaigns, the rural WWTPs improved the water quality of the effluents very significantly (90%, 70%, and 40% reductions in TU, COD, and NH4+-N, respectively) and reduced ARB and ARG loads by 2–4 log units. All three biological treatments contributed to the final quality of the effluents, especially in terms of microbiological parameters, with statistically indistinguishable efficiencies between them. These results show the importance of rural WWTPs in improving the water quality of urban effluents while reducing microbiological risk and the spread of antibiotic resistance into the environment. The study demonstrates the utility of a non-centralized, self-managed wastewater treatment scheme that can be implemented in many sparsely populated areas around the world.

Wastewater generation model to predict impacts of urine separation on wastewater treatment plants.

Wastewater treatment plants (WWTPs) are under increasing pressure to enhance resource efficiency and reduce emissions into water bodies. The separation of urine within the catchment area may be an alternative to mitigate the need for costly expansions of central WWTPs. While previous investigations assumed a spatially uniform implementation of urine separation across the catchment area, the present study focuses on an adapted stochastic wastewater generation model, which allows the simulation of various wastewater streams (e.g., urine) on a household level. This enables the non-uniform separation of urine across a catchment area. The model is part of a holistic modelling framework to determine the influence of targeted urine separation in catchments on the operation and emissions of central WWTPs, which will be briefly introduced. The wastewater generation model is validated through an extensive sampling and measurement series. Results based on observed and simulated wastewater quantity and quality for a catchment area of 366 residents for two dry weather days indicate the suitability of the model for wastewater generation and transport modelling. Based on this, four scenarios for urine separation were defined. The results indicate a potential influence of spatial distribution on the peaks of total nitrogen and total phosphorus.

Biochar from co-pyrolysis of biological sludge and sawdust in comparison with the conventional filling media of vertical-flow constructed wetlands for the treatment of domestic-textile wastewater.

A biochar from co-pyrolysis of a mixture of sawdust and biological sludge (70/30, w/w), providing a high environmental compatibility in terms of water leachable polycyclic aromatic hydrocarbons and inorganic elements, together with a remarkable surface area (389 m2/g), was integrated into laboratory-scale vertical-flow constructed wetlands (VF-CWs), planted with Phragmites australis and unplanted. Biochar-filled VF-CWs have been tested for 8 months for the refining of effluents from the tertiary clariflocculation stage of a wastewater treatment plant operating in a mixed domestic-industrial textile context, in comparison with systems filled with gravel. VF-CW influents and effluents were monitored for chemical oxygen demand (COD), nitrogen and phosphorus cycles, and absorbance values at 254 and 420 nm, the latter as rapid and reliable screening parameters of the removal of organic micropollutants containing aromatic moieties and/or chromophores. Biochar-based systems provided a statistically significant improvement in COD (Δ = 22%) and ammonia (Δ = 35%) removal, as well as in the reduction of UV-Vis absorbance values (Δ = 32-34% and Δ = 28% for 254 and 420 nm, respectively), compared to gravel-filled microcosms. The higher removal of organic was mainly attributed to the well-known adsorption properties of biochars, while for nitrogen the biological mechanisms seem to play a predominant role.

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.

Performance assessment of Mabula municipal wastewater plant: Zvishavane town, Zimbabwe

The characterisation and performance assessment of municipal wastewater treatment plants is very important as it provides information regarding the types of contaminants present in the water and their subsequent concentrations. It is also important in assessing gaps within the treatment process and is thus an essential step in needed when recalibrating and redesigning a plant to improve its efficiency. However, in Zimbabwe there is lack of comprehensive current data characterises effluent and influent from municipal wastewater treatment plants. The aim of the study was to assess the quality of wastewater from Mabula wastewater treatment plant, Zvishavane Zimbabwe, infer the performance of the plant and assess the implications of municipal effluent discharge on the water quality of Musavezi River. Composite water samples were collected over a 21-week sampling period from December 2022 to April 2023 from five sampling points, two from the treatment stages within the plant and three points along the course of the Musavezi River. Samples were taken to an independent laboratory and analysed for pH, Biological Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), Total Nitrogen (TN), Total Phosphorous (TP) and Ammonium-Nitrate (NH3–N) in line with standards set by the American Public Health Association (APHA). Results from the study indicate that the plant receives and treats effluent that is of medium to high strength. Mean values of the effluent at discharge point indicated organic and nutrient concentrations that were higher than the Zimbabwe effluent discharge standards as prescribed by the Environmental Management Agency. Removal efficiencies for the plant varied from 83.01%, 83.02%, 88.26%, 36.21%, 67.43% and 51.6% for BOD5, COD, TSS, TN, TP, and NH3–N respectively. This indicates that the treatment plant is not performing very well with regards to nutrient removal. Effluent disposal was revealed to have implication on the water quality of the Musavezi River as indicated by water quality index (WQI). There is need to consistently monitor and regulate the performance of Mabula wastewater treatment plant so that it meets the national effluent discharge regulations.

Evaluation of the Environmental Impact and Energy Utilization Efficiency of Wastewater Treatment Plants in Tumen River Basin Based on a Life Cycle Assessment + Data Envelopment Analysis Model

The environmental impacts from energy consumption account for a high percentage of the environmental impacts of wastewater treatment plants (WWTPs) throughout their life cycle; therefore, controlling energy use in WWTPs could bring substantial benefits to the environment. In this study, according to the different percentages of electricity generation from renewable energy compared to fossil energy, the global warming, acidification, eutrophication, human toxicity, and photochemical smog, the environmental impacts of WWTP operation were considered. Furthermore, to explore a more sustainable way of operating WWTPs under the “dual-carbon” strategic decision, the environmental impacts and energy utilization efficiency of different power allocation scenarios at present and in the next 40 years were compared based on the LCA+DEA integrated model. The study revealed that in scenarios 1–5, as the proportion of renewable energy power generation gradually increased, all LCA results showed a gradual decrease, of which GWP decreased by 83.32% and human toxicity decreased by 93.34%. However, in scenarios 2–5, the contribution ratio (proportion) of gas and electricity to GWP and POCP gradually increased, reaching 77.11% and 59.44%, respectively, in scenario 5. The contribution ratio (proportion) of biomass generation to AP and EP gradually increased as well, reaching 65.22% and 68.75%, respectively, in scenario 5. Meanwhile, the combined technical efficiency in energy utilization in the five scenarios showed a decreasing trend; only scenario 1 was fully efficient, and the combined efficiency was 1. The values of combined technical efficiency in scenarios 2, 3, 4, and 5 gradually decreased and were 0.7386, 0.4771, 0.2967, and 0.1673, respectively. This study discusses whether the use of renewable energy in place of fossil energy power elicits an environmental impact in WWTPs. We explore the feasibility of achieving energy savings and emission reductions in WWTPs within the Tumen River Basin, to provide a theoretical basis for their sustainable development.

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.

Role of Flexible Operation of a Wastewater Treatment Plant in the Reduction of Its Indirect Carbon Dioxide Emissions—A Case Study

The increasing share of renewables in electricity grids comes with a challenge of energy surpluses and deficits, which needs be handled by demand side management (DSM) and storage options. Within this approach, wastewater treatment plants (WWTPs), with flexible energy consumption and production processes and storage units, can contribute to stabilizing the grids and integrating more renewables. In this study, the operation of a real WWTP was optimized by mixed integer linear programming (MILP) to minimize its indirect carbon dioxide (CO2) emissions. The operation of the WWTP was shown to be flexible in following the CO2 emission factor of the electricity grid, which was possible with the utilization of the WWTP’s storage units and flexible co-substrate feeding. As a result, by changing only the operational behavior of the WWTP, its indirect CO2 emissions decreased by 4.8% due to the higher share of renewables in the electricity grid. The CO2 emissions were shown to decrease further up to 6.9% by adding virtual storage units.

The Efficiency of a Biological Reactor in a Domestic Wastewater Treatment Plant Operating Based on ABS (Acrylonitrile Butadiene Styrene) Material and Recycled PUR (Polyurethane) Foam

The primary objective of this research was to assess the efficacy of a novel solution under conditions closely resembling those of real-world scenarios. Biological beds, or filters, hold significant potential for widespread implementation in individual households, particularly in areas with dispersed housing. The system’s aim was to improve the quality of wastewater treated in on-site domestic biological treatment plants. A pivotal aspect of the project involved developing a prototype research installation for conducting comprehensive testing. Our installation system consisted of several components designed to create a laboratory-scale model for domestic wastewater treatment. The model comprised four biological reactors filled with ABS material and secured by a PUR frame. Additionally, the tested model included a controller for wastewater dosing control, a septic tank as a reservoir, and four tanks for collecting purified wastewater. Through regression analysis using the Generalized Linear Model (GLM), a correlation between CODCr and TSS was revealed. This study presents the research findings concerning the development of a prototype installation that incorporates an advanced reactor or filter. The data derived from this research have the potential to contribute to the creation of products that enhance the performance and efficiency of household wastewater treatment systems.