Reconstruction of Dynamics of Aqueous Phenols and Their Products Formation in Ozonation Using Differential Neural Network Observers

Abstract

A differential neural network (DNN) is used to estimate the state dynamics in the phenols-ozone-water system of the model solution of phenol (PH), 4-chlorophenol (4-CPH), and 2,4-dichlorophenol (2,4-DCPH). This new technique, which is based on a differential neural network observer (DNNO), is applied to estimate decomposition dynamics of phenols, byproducts accumulation and decomposition, and final products accumulation. It is considered to be a process with an uncertain model ("black-box") and is affected by internal (variation of experimental conditions) and external perturbations (measurement noises). The monitored ozone concentration in the gas phase in the reactor outlet is used to obtain the summary characteristic curve (ozonogram) in ozonation. The proposed DNNO is trained using the variation of this parameter with the experimental data of the phenols decomposition, obtained using the HPLC technique, at pH 2 and pH 9. The trained DNNO then is applied to reconstruct the dynamics of the phenols decomposition, as well as the byproducts accumulation and the decomposition and the final product accumulation at pH 7 and pH 12. The proposed DNNO technique has been tested to compare estimated results to those experimentally obtained during semibatch ozonation of the model solution of phenols. A good correspondence between the experimental decomposition dynamics and those estimated by DNNO was obtained.

Technologies for the removal of phenol from fluid streams: A short review of recent developments

“…The conversion of phenol is only slightly increased if pH 10 is produced due to the addition of soda [78]. A further increase in phenol conversion may be obtained by the use of ultrasound, but this effect is more relevant at neutral than at basic pH [79].The use of Differential Neural Network Observers to the products formation during phenol ozonation has been the object of a recent paper [79].…”

“…The Fenton-type CWPO of phenol is made more efficient by coupling with sonication [79,90] and hydrodynamic cavitation [132]. On the other hand also the coupling of adsorption on alumina with electrical discharges [141] as well as of adsorption on ACs and polymeric materials with sonication [130] have been reported as very efficient methods to remove phenol.…”

Sensor Data Fusion as an Alternative for Monitoring Chlorate in Electrochlorination Applications

“…As a third option, a soft sensing approach can be taken [32]. In this case, a number of sensors are used in conjunction with a model of the production process to estimate the by-product formation or to increase the accuracy or robustness of such an estimate [18].…”