Application of artificial neural networks in modeling limestone–SO₂ reaction

Abstract

Four varieties of limestone distinguished on the basis of pore-size distributions were exposed in dynamic lo-, 20-and 50-

Development of an artificial neural network correlation for prediction of hold-up of slurry transport in pipelines

“…Since the early 1980s, artificial neural networks (ANNs) have been used extensively in chemical engineering for such various applications like adaptive control, model-based control, process monitoring, fault detection, dynamic modeling and parameter estimation (Bandyopadhyay et al, 1996;Baughman and Liu, 1995;Bhatt and McAvoy, 1990;Bowen et al, 1998;Hecht-Nielsen, 1989;Schaan et al, 2000;Venkatasubramanian and Chan, 1989). The ANN provides a non-linear mapping between input and output variables and is also useful in providing cross-correlation among these variables.…”

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

“…Gauri et al [ 15 ] compare the performance of the mechanistic kinetic modeling with the neural network. They consider a heterogeneous reaction of various limestones with SO 2 .…”

“…Thanks to these properties, the neural networks have found their use in vastly different fields, e.g., image classification [ 10 ], predicting combustion instability [ 11 ], or impact sensitivity of energetic materials [ 12 ]. Although ANNs have been extensively applied in chemical engineering since the 1990s [ 13 , 14 , 15 ], their usage in thermal analysis has commenced later [ 9 , 16 ], and most of the studies emerged only recently [ 17 , 18 , 19 , 20 , 21 ]. The present review aims to summarize such studies and to assess some future prospects.…”

A Comparative Study of ANN and GEP Model to Predict the Pressure Drop in the Water Transportation System

“…If a certain threshold is reached the neuron passes information to all other linked neurons, otherwise, it remains dormant. Artificial neural networks (ANNs) have extensively used in different engineering application such as adaptive control, model-based control, process monitoring, fault detection, dynamic modeling and parameter estimation in a working environment [7,8] ANN Technique has the wide range of application in the field of Multiphase flow and predicting the holdup and pressure drop variation in a pipeline network analysis. Although, built on the black box architecture the evolved models be short of closed-form analytical relationships between the chosen input and response variables.…”