Wastewater treatment plants are complex, non-linear, engineered systems of physical,
biological and chemical processes operating at different timescales. Sensor systems are …

In wastewater treatment processes, sampling delays, resulting from difficult-to-measure but
important variables, usually shape wastewater treatment processes (WWTPs) as multirate …

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 …

Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce
water's pollutant products, which are harmful to the environment at high concentrations. In …

Soft sensors have emerged as a powerful tool for predicting quality-related but hard-to-
measured variables in the wastewater treatment plants (WWTPs). However, due to high data …

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 …

This paper proposes a semisupervised deep neural regression network with embedding
manifold (SSE-DNN) for soft sensor modeling that integrates manifold embedding into deep …

Data-driven soft sensor methods have been widely used in municipal wastewater treatment
processes to achieve efficient monitoring of effluent indicators. However, the complex …

Data-driven soft sensors have been widely adopted in industrial processes to learn hidden
knowledge automatically from process data, then to monitor difficult-to-measure quality …

Refinery industrial processes are very complex with nonlinear dynamics resulting from
varying feedstock characteristics and also from changes in product prioritization. Along …