Dissolved oxygen control strategies for water treatment: a review

Enhanced predictive modeling of dissolved oxygen concentrations in riverine systems using novel hybrid temporal pattern attention deep neural networks

Monitoring water quality and river ecosystems is vital for maintaining public health and environmental sustainability. Over the past decade, data-driven methods have been extensively used for river water quality modeling, including dissolved oxygen (DO) concentrations. Despite advancements, challenges persist regarding accuracy, scalability, and adaptability of data-driven models to diverse environmental conditions. Previous studies primarily employed singular models or basic combinations of machine learning techniques, lacking advanced integration of adaptive mechanisms to process complex and evolving datasets. The current study introduces innovative hybrid models that integrate temporal pattern attention (TPA) mechanisms with advanced neural networks, including feed-forward neural networks (FFNNs) and long short-term memory networks (LSTMs). This approach leverages the synergistic strengths of individual models, significantly enhancing the accuracy of DO predictions. The models were rigorously tested against water quality data obtained from two distinct riverine environments, the Illinois River (ILL) and Des Plaines River (DP). Daily measured water quality data, including DO, chlorophyll-a, nitrate plus nitrite, water temperature, specific conductance, and pH, from 2017 to 2024 provided a robust foundation for comprehensive analysis of DO dynamics in these rivers. We conducted 10 scenarios with different model inputs, wherein the hybrid TPACWRNN-LSTM-10 model particularly excelled, achieving coefficient of determination values of 0.993 and 0.965, and root mean squared errors of 0.241 mg/L and 0.450 mg/L for DO predictions at the ILL and DP stations, respectively. The model's reliability was further confirmed by Willmott's index values of 0.998 and 0.992 and Nash-Sutcliffe efficiency values of 0.990 and 0.961 at the ILL and DP stations, respectively. Additionally, Shapley additive explanations (SHAP) values were utilized to interpret each predictor's contribution, revealing key drivers of DO predictions. We believe the novel hybrid modeling approach presented in this study could benefit utilities and water resource management systems for predicting water quality in complex systems.

Modelling and set-point definition for the development of a joint control system of two interconnected wastewater treatment plants and its application in practice

The use of activated sludge models (ASMs) is a common way in the field of wastewater engineering in terms of plant design, development, optimization, and testing of stand-alone treatment plants. The focus of this study was the development of a joint control system (JCS) for a municipal wastewater treatment plant (mWWTP) and an upstream industrial wastewater treatment plant (iWWTP) to create synergies for saving aeration energy. Therefore, an ASM3 + BioP model of the mWWTP was developed to test different scenarios and to find the best set-points for the novel JCS. A predictive equation for the total nitrogen load (TN) coming from the iWWTP was developed based on real-time data. The predictive TN equation together with an optimized aeration strategy, based on the modelling results, was implemented as JCS. First results of the implementation of the JCS in the real environment showed an increase in energy efficiency for TN removal.

Towards stable and efficient nitrogen removal in wastewater treatment processes via an adaptive neural network based sliding mode controller

Advanced controllers often offer an innovative solution to proper quality control in wastewater treatment processes (WWTPs). However, nonlinearity and uncertain disturbances usually make the conventional control strategies inadequate or impossible for the stable operations of WWTPs. To guarantee the stability of ammonia nitrogen concentration ( S N H ) control in WWTPs, a direct adaptive neural networks-based sliding mode control (ANNSMC) strategy has been proposed in this article. A sliding mode controller is designed and implemented with the help of an adaptive Neural Network (ANN), named Radial Basis Function Neural Network (RBFNN), which can approach the desired control law accurately. Also, the stability of a system installed with the ANNSMC is analyzed by using the Lyapunov theorem, which ensures system robustness and adaptability. Additionally, to deal with high energy consumption and low treatment efficiency problems in the wastewater denitrification processes, this paper proposes a dual-loop denitrification control strategy and validates it in the Benchmark Simulation Model No.2 (BSM2) platform. The strategy can strengthen the denitrification efficiency by collaborating the S N H with nitrate nitrogen ( S N O ) concentration in the WWTPs properly. The experimental results demonstrate that the proposed strategy can obtain remarkable stability and robustness, reducing energy consumption effectively compared with other standard and advanced control strategies.

Digitalization of phosphorous removal process in biological wastewater treatment systems: Challenges, and way forward

Phosphorus in wastewater poses a significant environmental threat, leading to water pollution and eutrophication. However, it plays a crucial role in the water-energy-resource recovery-environment (WERE) nexus. Recovering Phosphorus from wastewater can close the phosphorus loop, supporting circular economy principles by reusing it as fertilizer or in industrial applications. Despite the recognized importance of phosphorus recovery, there is a lack of analysis of the cyber-physical framework concerning the WERE nexus. Advanced methods like automatic control, optimal process technologies, artificial intelligence (AI), and life cycle assessment (LCA) have emerged to enhance wastewater treatment plants (WWTPs) operations focusing on improving effluent quality, energy efficiency, resource recovery, and reducing greenhouse gas (GHG) emissions. Providing insights into implementing modeling and simulation platforms, control, and optimization systems for Phosphorus recovery in WERE (P-WERE) in WWTPs is extremely important in WWTPs. This review highlights the valuable applications of AI algorithms, such as machine learning, deep learning, and explainable AI, for predicting phosphorus (P) dynamics in WWTPs. It emphasizes the importance of using AI to analyze microbial communities and optimize WWTPs for different various objectives. Additionally, it discusses the benefits of integrating mechanistic and data-driven models into plant-wide frameworks, which can enhance GHG simulation and enable simultaneous nitrogen (N) and Phosphorus (P) removal. The review underscores the significance of prioritizing recovery actions to redirect Phosphorus from effluent to reusable products for future considerations.

Effects of eutrophication on the horizontal transfer of antibiotic resistance genes in microalgal-bacterial symbiotic systems

Overloading of nutrients such as nitrogen causes eutrophication of freshwater bodies. The spread of antibiotic resistance genes (ARGs) poses a threat to ecosystems. However, studies on the enrichment and spread of ARGs from increased nitrogen loading in algal-bacterial symbiotic systems are limited. In this study, the transfer of extracellular kanamycin resistance (KR) genes from large (RP4) small (pEASY-T1) plasmids into the intracellular and extracellular DNA (iDNA, eDNA) of the inter-algal environment of Chlorella pyrenoidosa was investigated, along with the community structure of free-living (FL) and particle-attached (PA) bacteria under different nitrogen source concentrations (0-2.5 g/L KNO3). The results showed that KR gene abundance in the eDNA adsorbed on solid particles (D-eDNA) increased initially and then decreased with increasing nitrogen concentration, while the opposite was true for the rest of the free eDNA (E-eDNA). Medium nitrogen concentrations promoted the transfer of extracellular KR genes into the iDNA attached to algal microorganisms (A-iDNA), eDNA attached to algae (B-eDNA), and the iDNA of free microorganisms (C-iDNA); high nitrogen contributed to the transfer of KR genes into C-iDNA. The highest percentage of KR genes was found in B-eDNA with RP4 plasmid treatment (66.2%) and in C-iDNA with pEASY-T1 plasmid treatment (86.88%). In addition, dissolved oxygen (DO) significantly affected the bacterial PA and FL community compositions. Nephelometric turbidity units (NTU) reflected the abundance of ARGs in algae. Proteobacteria, Cyanobacteria, Bacteroidota, and Actinobacteriota were the main potential hosts of ARGs. These findings provide new insights into the distribution and dispersal of ARGs in the phytoplankton inter-algal environment.

Hybrid supervised hierarchical control of a biological wastewater treatment plant

In wastewater treatment intensification, hierarchical control structures are developed to improve the plant's performance. This paper proposes two novel hybrid supervised hierarchical control structures for specifying the dissolved oxygen concentration in the last aerobic reactor of the wastewater treatment plant (WWTP) based on the nitrification rate and the ammonia level in this reactor. These structures combine the optimum disturbance rejection PI control (OPI), adaptive neuro-fuzzy inference system (ANFIS), and genetic algorithms (GA) to reduce energy consumption and operational costs, improve effluent quality, and reduce the number and percentage of times the established maximum concentration of pollutants in the effluent of the WWTP is violated. The proposed control strategy is implemented and evaluated using benchmark simulation model no. 1 (BSM1). The OPI-ANFIS-GA configuration significantly enhances effluent quality in dry, rainy, and stormy weather conditions, reducing total nitrogen violations by 50.17%, 63.35%, and 47.35%, respectively. Then, 6.79% and 7.12% of aeration energy and 1.44% and 1.46% of operational costs are reduced in dry and rain weather conditions. The OPI-ANFIS configuration enhanced significant energy savings and a cost reduction in storm weather conditions. Both configurations led to a 49.89% decrease in total suspended sludge (TSS) during stormy weather conditions. The proposed controller significantly improves the performance of the WWTP in all weather scenarios compared to the default controller and similar controllers found in the literature.

Effect of Micro-Nanobubbles on Arsenic Removal by Trichoderma atroviride for Bioscorodite Generation

The global environmental issue of arsenic (As) contamination in drinking water is a significant problem that requires attention. Therefore, the aim of this research was to address the application of a sustainable methodology for arsenic removal through mycoremediation aerated with micro-nanobubbles (MNBs), leading to bioscorodite (FeAsO4·2H2O) generation. To achieve this, the fungus Trichoderma atroviride was cultivated in a medium amended with 1 g/L of As(III) and 8.5 g/L of Fe(II) salts at 28 °C for 5 days in a tubular reactor equipped with an air MNBs diffuser (TR-MNBs). A control was performed using shaking flasks (SF) at 120 rpm. A reaction was conducted at 92 °C for 32 h for bioscorodite synthesis, followed by further characterization of crystals through Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and X-ray diffraction (XRD) analyses. At the end of the fungal growth in the TR-MNBs, the pH decreased to 2.7-3.0, and the oxidation-reduction potential (ORP) reached a value of 306 mV at 5 days. Arsenic decreased by 70%, attributed to possible adsorption through rapid complexation of oxidized As(V) with the exchangeable ferrihydrite ((Fe(III))4-5(OH,O)12), sites, and the fungal biomass. This mineral might be produced under oxidizing and acidic conditions, with a high iron concentration (As:Fe molar ratio = 0.14). The crystals produced in the reaction using the TR-MNBs culture broth and characterized by SEM, XRD, and FTIR revealed the morphology, pattern, and As-O-Fe vibration bands typical of bioscorodite and römerite (Fe(II)(Fe(III))2(SO4)4·14H2O). Arsenic reduction in SF was 30%, with slight characteristics of bioscorodite. Consequently, further research should include integrating the TR-MNBs system into a pilot plant for arsenic removal from contaminated water.

Prediction of the roughness coefficient for drainage pipelines with sediments using GA-BPNN

Accurate prediction of the roughness coefficient of sediment-containing drainage pipes can help engineers optimize urban drainage systems. In this paper, the variation of the roughness coefficient of circular drainage pipes containing different thicknesses of sediments under different flows and slopes was studied by experimental measurements. Back Propagation Neural Network (BPNN) and Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) were used to predict the roughness coefficient. To explore the potential of artificial neural networks to predict the roughness coefficient, a formula based on drag segmentation was established to calculate the roughness coefficient. The results show that the variation trend of the roughness coefficient with flow, hydraulic radius, and Reynolds number is consistent. With the increase of the three parameters, the roughness coefficient decreases overall. Compared to the traditional empirical formula, the BPNN model and the GA-BPNN model increased the determination factors in the testing stage by 3.47 and 3.99%, respectively, and reduced the mean absolute errors by 41.18 and 47.06%, respectively. The study provides an intelligent method for accurate prediction of sediment-containing drainage pipes roughness coefficient.