Environmental sensors are utilized to collect real-time data that can be viewed and interpreted using a visual format supported by a server. Machine learning (ML) methods, on the other hand, are excellent in statistically evaluating complicated nonlinear systems to assist in modeling and prediction. Moreover, it is important to implement precise online monitoring of complex nonlinear wastewater treatment plants to increase stability. Thus, in this study, a novel modeling approach based on ML methods is suggested that can predict the effluent concentration of total nitrogen (TNeff) a few hours ahead. The method consists of different ML algorithms in the training stage, and the best selected models are concatenated in the prediction stage. Recursive feature elimination is utilized to reduce overfitting and the curse of dimensionality by finding and eliminating irrelevant features and identifying the optimal subset of features. Performance indicators suggested that the multi-attention-based recurrent neural network and partial least squares had the highest accurate prediction performance, representing a 41% improvement over other ML methods. Then, the proposed method was assessed to predict the effluent concentration with multistep prediction horizons. It predicted 1-h ahead TNeff with a 98.1% accuracy rate, whereas 3-h ahead effluent TN was predicted with a 96.3% accuracy rate.
The water distribution system is an infrastructure system supplying water to urban areas. Since it has a great influence on the quality of life and financial aspect of customers, the performance evaluation of the system for an efficient management and operation is essential. Until now, most of the suggested performance indicators for the system are based on the available demand and pressure at demand nodes obtained from the hydraulic simulation. However, those performance indicators based on the hydraulic simulation may not consider the actual usability of water for customers properly. Therefore, in this study, the application of fuzzy functions along with the available demands at demand nodes, which are obtained from the hydraulic simulation, from the various points of view, makes us possible to evaluate the system performance by depending on the set value of the variables. For this purpose, we use a PDA model, which can simulate various abnormal operation conditions and suggest two performance indicators: the possible water supply range indicator (PWSRI) for the water supply performance evaluation for an individual demand node and the possible water supply indicator for the entire system (PWSIES). The suggested method and indicators are applied to the real water distribution system of A-city in Korea to verify the applicability.
In order to optimize the process operation against fluctuating influent water quality, it is essential to apply process control and simulate the process for deriving the optimal operation method. To simulate the process, the ASM2d model and ADM model of the IWA have been widely used for the simulation of the nitrogen and phosphorus removal process and biogas production process, which consist of ordinary differential equations. In order to simulate a sewage treatment plant, it is essential to simulate the steady state of a process under a given set of disturbances and operating conditions. However, the disadvantage is that the calculation time is long when analyzing the ordinary differential equations. In order to shorten the computation time, we propose an improved Newton-Raphson method. As a result, the ASM2d and the ADM1 were able to simulate the processes 32.3 times and 8 times faster than ordinary differential equation analysis, respectively.
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