With the advent of the Industry 4.0 and the introduction of smart technologies in wastewater treatment plants (WWTP); forecasting influent loads is essential to regulate exaggerated operational strategies for WWTP. However, due to various water usage and sources, it is challenging to forecast the fluctuating influent loads. To deal with highly nonlinear and temporal-correlated characteristics of influent loads, in this article, we proposed hybrid influent forecasting model based on multimodal and ensemble-based deep learning (ME-DeepL). The proposed ME-DeepL forecasting model combines strength of multiple deep-learning algorithms in ensemble-learning architecture to handle propagated intrinsic sublayers by empirical mode decomposition of influent loads. Then, the proposed model was assessed to predict the loads on long-term (daily), and short-term (hourly) with multisteps forecast horizons. The experimental results revealed that ME-DeepL model exhibited superior forecasting performance in comparison to five recurrent neural network-based reference models, by capturing the informative features and temporal patterns from fluctuating influent loads.

Detection and isolation of burst locations in water distribution networks (WDN) are challenging problems in urban management because burst events cause considerable economic, social, and environmental losses. In the present study, a novel monitoring and sensor placement approach is proposed for rapid and robust burst detection. Accordingly, a hybrid principal component analysis (PCA) and standardized exponential weighted moving average (EWMA) system is proposed for WDN monitoring and management. In addition, the optimal sensor configuration is obtained using PCA, k-means clustering, and a sensitivity analysis considering the diurnal patterns and the noises of pressure and flowrate data in the WDN. The proposed system is applied to a branched WDN, and the results are compared to those obtained with conventional monitoring systems. The results show that the proposed system detected the burst occurrence regardless of noise size with a detection rate of 93%. Compared to conventional systems, the isolation ratio improved by 10%, indicating that the bursts were isolated more accurately. In addition, the corresponding sensor configuration was 40% less expensive than the conventional systems.

This study proposed an optimal hybrid renewable energy system (HRES) to sustainably meet the dynamic electricity demand of a membrane bioreactor. The model-based HRES consists of solar photovoltaic panels, wind turbines, and battery banks with grid connectivity. Three scenarios, 101 sub-scenarios, and three management cases were defined to optimally design the system using a novel dual-scale optimization approach. At the system scale, the power-pinch analysis was applied to minimize both the size of components and the outsourced needed electricity (NE) from Vietnam’s electrical grid. At a local-scale, economic and environmental models were integrated, and the system was graphically optimized using a novel objective function, combined enviro-economic costs (CEECs). The results showed that the optimal CEECs were $850,710/year, $1,030,628/year, and $1,693,476/year for the management cases under good, moderate, and unhealthy air qualities, respectively. The smallest CEEC was obtained when 47% of the demand load of the membrane bioreactor was met using the HRES and the rest was supplied by the grid, resulting in 6,800,769 kg/year of CO₂ emissions.

Partial nitritation (PN) coupled with the anaerobic ammonium oxidation (Anammox) process has improved ammonium removal in wastewater treatment plants (WWTPs). The operation conditions of this process, i.e., the dissolved oxygen (DO) and the influent ammonium and nitrite concentrations, drive the process to an equilibrium to suppress nitrite-oxidizing bacteria and achieve a proper nitrite over ammonium (NO₂/NH₄) ratio. This study aimed to implement a set of control strategies in a WWTP model BSM2-SHAMX, combining PN in a single reactor system for high-activity ammonia removal over nitrite (SHARON) to an Anammox reactor, using proportional–integrative–derivative (PID) control and model predictive control (MPC) in a cascade. For correct coupling, the PN should maintain an output NO₂/NH₄ ratio between 1 and 1.3, suitable for the Anammox process. In the cascade controller feedback loop, the primary control loop controls the NO₂/NH₄ ratio through the DO concentration from the secondary control loop, which guarantees better effluent nitrogen removal. The performance of the plant was assessed by evaluating the control strategies with different influent carbon/nitrogen (C/N) loadings. The study results showed that the MPC controllers provided better results, with an improvement of 36% in the operational cost compared to the base case with a cost around 26,000 EUR/d, and better nitrogen removal surpassing 90% removal, 10% more than the base case.