Prediction of Vapor-liquid Equilibrium Databy Using Radial Basis Neural Networks

Most of the Chemical Engineering processes are nonlinear and complex in nature. They often require conventional modeling and simulation techniques based on certain simplified transport, kinetic and thermodynamic assumptions. These assumptions may, however, alter the exact nature of the system and would provide misleading information about the complex behavior of the system. An artificial neural network has the ability to overcome these limitations of the conventional approach by extracting the desired information directly from the data. In this paper radial basis network, a new generation of artificial neural network, has been successfully incorporated for the prediction of vapor liquid equilibrium data for binary systems including two azeotropes and a ternary system. Radial basis networks require lesser neurons than standard feed forward backpropagation and they can be trained in a fraction of time. From this work it is been proved that radial basis neural network has been successfully used for the prediction of vapor liquid equilibrium (VLE) data

Prediction of Vapor-liquid Equilibrium Databy Using Radial Basis Neural Networks

Most of the Chemical Engineering processes are nonlinear and complex in nature. They often require conventional modeling and simulation techniques based on certain simplified transport, kinetic and thermodynamic assumptions. These assumptions may, however, alter the exact nature of the system and would provide misleading information about the complex behavior of the system. An artificial neural network has the ability to overcome these limitations of the conventional approach by extracting the desired information directly from the data. In this paper radial basis network, a new generation of artificial neural network, has been successfully incorporated for the prediction of vapor liquid equilibrium data for binary systems including two azeotropes and a ternary system. Radial basis networks require lesser neurons than standard feed forward backpropagation and they can be trained in a fraction of time. From this work it is been proved that radial basis neural network has been successfully used for the prediction of vapor liquid equilibrium (VLE) data

Kinetics of biological treatment of phenolic wastewater in a three phase draft tube fluidized bed bioreactor containing biofilm

Phenolic wastewater was treated in a three-phase draft tube fluidized bed reactor containing biofilm. Phenol removal rate with biofilm was evaluated both theoretically and experimentally. The results indicate that biodegradation of phenolic wastewater by biofilm process could be treated as a zero order reaction. The volumetric biological removal rate with biofilm is proportional to the specific surface area of the biofilm with characteristic constant (k) 0.74 x 10-2 kg PhOH/m2 biofilm/d. It is proven here thatalmost 100% phenol removal could be attained at a specific biofilm surface area per volumetric phenol loading rate exceeding 132 m2/(kg-PhOH/d). The bioparticle diameter and the bioparticle hold-up in the three phase draft tube fluidized bed bioreactor are the decisive factors for the efficiency of the phenol treatment

Experiments and ANFIS modelling for the biodegradation of penicillin-G wastewater using anaerobic hybrid reactor

The performance of an anaerobic hybrid reactor (AHR) for treating penicillin-G wastewater was investigated at the ambient temperatures of 30-35°C for 245days in three phases. The experimental data were analysed by adopting an adaptive network-based fuzzy inference system (ANFIS) model, which combines the merits of both fuzzy systems and neural network technology. The statistical quality of the ANFIS model was significant due to its high correlation coefficient R2 between experimental and simulated COD values. The R2 was found to be 0.9718, 0.9268 and 0.9796 for the I, II and III phases, respectively. Furthermore, one to one correlation among the simulated and observed values was also observed. The results showed the proposed ANFIS model was well performed in predicting the performance of AHR. © 2011 Elsevier Ltd

Molecular Interaction Studies in Binary Liquid Mixtures from Ultrasonic Data

Ultrasonic velocities and densities of the binary liquid mixtures of dimethy1 sulphoxide (DMSO) with phenol, o-cresol, m-cresol, p-cresol and p-chlorophenol at 318.15 K, over the entire composition range were measured. The theoretical values of ultrasonic velocity were evaluated using the Nomoto’s Relation (NR), Ideal Mixture Relation (IMR), Free Length Theory (FT) and Collision Factor Theory (FLT). The validity of these relations and theories was tested by comparing the computed sound velocities with experimental values. Further, the molecular interaction parameter (α) was computed by using the experimental and the theoretical ultrasonic velocity values. The variation of this parameter with composition of the mixtures has been discussed in terms of molecular interaction in these mixtures