In wastewater treatment plants, the most adopted sensors are those with the properties of
low cost and fast response. Soft sensors are alternative solutions to the hardware sensor for …

Proper monitoring of quality-related but hard-to-measure effluent variables in wastewater
plants is imperative. Soft sensors, such as dynamic neural network, are widely used to …

Given the multivariable coupling, strong nonlinearity and time-varying features in the
wastewater treatment processes, adaptive strategies, including just-in-time learning (JITL) …

The on-line monitoring of wastewater treatment plant (WWTP) operations is a challenge due
to interference and breakdown from the harsh conditions endured by sensors in wastewater …

With the development of machine learning and artificial intelligence (ML/AI) models, data-
driven soft sensors, especially the neural network-based, have widespread utilization for the …

Soft-sensor applications for wastewater management can provide valuable information for
intelligent monitoring and process control above and beyond what is available from …

Recent studies into the estimation and control of an activated sludge process (ASP) at a
wastewater treatment plant suggest that artificial-intelligence methods, such as neural …

Because wastewater treatment processes are generally characterized by variables coupled,
significant nonlinearities, parameters shift and time delay, traditional hardware sensors are …

Soft-sensor is the most common strategy to predict hard-to-measure variables in the
wastewater treatment processes. However, existence of a large number of hard-to-measure …

Real‐time measurements of key effluent parameters play a highly crucial role in wastewater
treatment. In this research work, we propose a soft sensor model based on deep learning …