A precise vehicle dynamics model is critical for simulation and algorithm testing. Neural
networks have been widely used to build high-fidelity vehicle dynamics models due to the …

The digital transformation of sludge treatment processes requires online sensing of
dewaterability. This topic has been attempted for many years based on macroscopic shear …

The traditional determination of optimum coagulant doses in drinking water treatment plants
(WTPs) using the jar test technique is time-consuming, expensive, significantly influenced by …

Accurately predicting drinking water quality is critical for intelligent water supply
management and for maintaining the stability and efficiency of water treatment processes …

If untreated, oilfield sewage generated during the oil exploitation process can lead to severe
environmental pollution. The coagulation/flocculation process plays a pivotal role in the …

Deep learning (DL) has emerged as a transformative and promising approach to address
inefficient resource management due to imprecise chemical dosing in conventional manual …

In this paper, the application of artificial intelligence, especially neural networks, in the field
of water treatment is comprehensively reviewed, with emphasis on water quality prediction …

The accurate effluent prediction plays a crucial role in providing early warning for abnormal
effluent and achieving the adjustment of feedforward control parameters during wastewater …

The escalating frequency and severity of extreme weather events, attributed to climate
change, present significant challenges for water treatment plants (WTPs). Addressing these …

While complex simulations of physical systems have been widely used in engineering and
scientific computing, lowering their often prohibitive computational requirements has only …