Abstract: The Iranian bentonite was first characterized by
Scanning Electron Microscopy (SEM), Inductively Coupled Plasma
mass spectrometry (ICP-MS), X-ray fluorescence (XRF), X-ray
Diffraction (XRD) and BET. The bentonite was then treated
thermally between 150°C-250°C at 15min, 45min and 90min and
also was activated chemically with different concentration of
sulphuric acid (3N, 5N and 10N). Although the results of thermal
activated-bentonite didn-t show any considerable changes in specific
surface area and Cation Exchange Capacity (CEC), but the results of
chemical treated bentonite demonstrated that such properties have
been improved by acid activation process.
Abstract: Poly-β-hydroxybutyrate (PHB) is one of the most
famous biopolymers that has various applications in production of
biodegradable carriers. The most important strategy for enhancing
efficiency in production process and reducing the price of PHB, is the
accurate expression of kinetic model of products formation and
parameters that are effective on it, such as Dry Cell Weight (DCW)
and substrate consumption. Considering the high capabilities of
artificial neural networks in modeling and simulation of non-linear
systems such as biological and chemical industries that mainly are
multivariable systems, kinetic modeling of microbial production of
PHB that is a complex and non-linear biological process, the three
layers perceptron neural network model was used in this study.
Artificial neural network educates itself and finds the hidden laws
behind the data with mapping based on experimental data, of dry cell
weight, substrate concentration as input and PHB concentration as
output. For training the network, a series of experimental data for
PHB production from Hydrogenophaga Pseudoflava by glucose
carbon source was used. After training the network, two other
experimental data sets that have not intervened in the network
education, including dry cell concentration and substrate
concentration were applied as inputs to the network, and PHB
concentration was predicted by the network. Comparison of predicted
data by network and experimental data, indicated a high precision
predicted for both fructose and whey carbon sources. Also in present
study for better understanding of the ability of neural network in
modeling of biological processes, microbial production kinetic of
PHB by Leudeking-Piret experimental equation was modeled. The
Observed result indicated an accurate prediction of PHB
concentration by artificial neural network higher than Leudeking-
Piret model.