Data Authorization and Forecasting by a Proactive Soft Sensing Tool–Anammox Based Process

Abstract

Precise control of biological wastewater treatment for nitrogen removal is difficult because of the nonlinearity, time-varying, and time-consuming nature of the process. With due emphasis on addressing the challenges involved in its effective implementation, this study developed an artificial neural network (ANN) based soft sensor (SS) with a set of proposed thumb rules for online forecasting of the concentrations of hard-to-measure parameters (NH4 + and NO2 −) from secondary easy-to-measure variables, (reactor volume, dissolved oxygen, suspended solids, pH, temperature, and ORP) in an Anammox based pilot plant. Four hybrid neural networks (PCA-Kalman NN, PCA NN, Kalman NN, and Non NN) were applied to identify net optimum input vectors for the SS, using an appropriate quantity of samples from the set of secondary variables. The proposed hybrid SS was tested on a sewage wastewater treatment plant operated using a Matlab R2018a framework and validated using operational plant data. The results showed that the PCA-Kalman neural network with R 2 values of 0.9985 and 0.9263 for NH4 + and NO2 –, respectively, is potentially a valuable tool for plant operators in the selection of operational states to minimize cost and to efficiently predict important parameters that are prone to errors due to a failure in online sensors.

Hybrid Modeling Method for Soft Sensing of Key Process Parameters in Chemical Industry

“…(6) Nawaz et al proposed an artificial neural network (ANN) and four hybrid neural networks (PCA-Kalman NN, PCA NN, Kalman NN, and non-NN) for data-driven soft sensing in a sidestream anammox process. (7) Zhu et al summarized the application of deep learning in datadriven soft sensing modeling methods. (8) In summary, conventional regression methodologies, optimization algorithms, and new intelligent techniques can be effectively applied in data-driven soft sensing.…”

Advances and challenges of mainstream nitrogen removal from municipal wastewater with anammox‐based processes

“…With online measurement, the safety limit used in process design can be reduced, and the efficiency and flexibility of plant operation can be improved (Jeppsson, Alex, Pons, Spanjers, & Vanrolleghem, 2002). Because of the physical properties of the processing system and the complexity of the synergistic effects, the progress of mathematical modeling and performance optimization has been insufficient; although it is possible to measure the concentration of some parameters in the laboratory, the consumed time is much longer; therefore, high‐quality control of the wastewater is difficult, mainly for advanced wastewater treatment, and needs to be more accurate, timely, and effective control measure devices (Nawaz et al., 2019).…”

Ecofriendly Anaerobic Ammonium Oxidation System: Optimum Operation and Inhibition Control Strategies for Enhanced Nitrogen Removal

“…The soft-sensing technique thus developed can aid in addressing such issues. 19 Figure 1 shows the process flow diagram of the system and Figure S2 shows the experimental setup of the bench-scale SBR.…”

“…There were instances when zero or redundant parameter values were recorded, which could be attributed to the failure of the online sensor. The soft-sensing technique thus developed can aid in addressing such issues Figure shows the process flow diagram of the system and Figure S2 shows the experimental setup of the bench-scale SBR.…”