Search

This paper presents a neural network application to identify the behavior of the model for two thermochemical processes, which are used to transform organic solid wastes. The first model corresponds to the char reduction zone of a gasification process, including inputs signals. The second one corresponds to a fluidized bed sludge combustor focused on the dynamics of NOx formation. The identification presented in this work is based on a discrete-time recurrent high order neural network (RHONN), which is trained with an extended Kalman filter (EKF) algorithm. The objective is to reproduce with neural networks the different gaseous components production and to study different chemical reactions taking place inside the reactors; this allows analyzing theoretically the process behavior in order to better understand some of the main phenomena before a future experimental stage. The neural identifier is implemented in specialized software and its performance is illustrated via simulations considering several operating conditions. [ABSTRACT FROM AUTHOR]

In this paper, a reduced order neural observer (RONO) with a time-varying learning rate is proposed. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. A time-varying learning rate is designed in order to improve the learning of the neuronal network in presence of disturbances and parameter variations. This work includes the stability proof of the time-varying learning. The applicability of the developed observer is illustrated via simulations for a nonlinear anaerobic digestion process. [ABSTRACT FROM AUTHOR]

Copyright of Chemical & Biochemical Engineering Quarterly is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most conventional methods to remove heavy metals from water are efficient for high concentrations, but they are expensive, produce secondary pollution, and cannot remove low concentrations. This paper proposes a biological system to remove Cr(VI) from aqueous solutions; the biofilter is composed of a native Pseudomonas koreensis immobilized in calcium alginate beads. Lab experiments were conducted in batch reactors, considering different operating conditions: Cr(VI) concentration, temperature, pH, and time. At 30 °C and a pH of 6.6, the immobilized bacteria achieved their optimal adsorption capacity. In the chromium adsorption system, saturation was reached at 30 h with a q max = 625 mg g -1 . By adjusting the experimental data to the Langmuir and Freundlich models, it is suggested that P. koreensis forms a biofilm with a homogeneous surface where Cr(VI) is adsorbed and that the bacteria also incorporates the metal in its metabolism, leading to a multilayer adsorption. On the other hand, using Fourier transform infrared spectroscopy, it was inferred that the functional groups involved in the adsorption process were O-H and C=O, which are a part of the P. koreensis cell wall.