Neural networks (NNs) are an effective tool to model nonlinear systems. However, their
forecasting performance significantly drops in the presence of process uncertainties and …

Neural networks (NNs) are extensively used in modelling, optimization, and control of
nonlinear plants. NN-based inverse type point prediction models are commonly used for …

The production index of gold cyanidation leaching process has an important influence on
the economic benefits of the plant-wide hydrometallurgical process. In the actual leaching …

In this paper, we formed prediction intervals using historical similarities, found through the
direct correlation. At first, a string of 5 to 20 recent samples is correlated with a long training …

A proportional-derivative (PD) fuzzy controller was presented to control the temperature of a
polycondensation reactor. Synthesis of various polyesters and copolyesters were conducted …

Prediction interval (PI) has been appeared as a promising tool to quantify the uncertainties
and disturbances associated with point forecasts. Despite of its numerous applications in …

Prediction interval (PI) has been extensively used to predict the forecasts for nonlinear
systems as PI-based forecast is superior over point-forecast to quantify the uncertainties and …

Chemical Reactors, such as continuous stir tank reactor (CSTR) and polymerization reactors
(PR) are nonlinear dynamics in nature. Moreover, in practice, uncertainties and disturbances …

It is a challenging task to control polymerization reactor due to the complex reactions
mechanism. Moreover, the dynamic behaviour of the polymerization reactor is highly …

Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point
predictions. Despite its informativeness, the design and deployment of PI-based controller …