Abstract In the present study, the thermal conductivities of ionic liquid + water in the concentrations 0.1-0.6 %w/w and temperatures (302.5-337.9 K) are determined. The thermal conductivity of aqueous 1-butyl-3-methylimidazolium bromide declined with increasing concentration and temperature. A quadratic model is developed for predicting the thermal conductivity of aqueous ionic liquid using response surface methodology with R2 = 0.99. A two-layered feed forward back propagation neural network 2-10-1 is also modeled with mean square error, root means square and absolute average error percentage of 0.005, 0.071 and 2.684, respectively. The thermal conductivity of nanofluid (γ-Al2O3/water) is estimated and compared with the ionic liquid solution at the same concentration and temperature range. Thermal conductivity enhancement ratios of aqueous ionic liquids are found to be more than nanoparticle suspensions.

Aflatoxin contamination of peanut, due to infection by Aspergillus flavus, is a major problem of rain-fed agriculture in India. In the present study, molecular characterisation of 187 Aspergillus flavus isolates, which were sampled from the peanut fields of Gujarat state in India, was performed using AFLP markers. On a pooled cluster analysis, the markers could successfully discriminate among the ‘A’, ‘B’ and ‘G’ group A. flavus isolates. PCoA analysis also showed equivalent results to the cluster analysis. Most of the isolates from one district could be clustered together, which indicated genetic similarity among the isolates. Further, a lot of genetic variability was observed within a district and within a group. The results of AMOVA test revealed that the variance within a population (84%) was more than that between two populations (16%). The isolates, when tested by indirect competitive ELISA, showed about 68.5% of them to be atoxigenic. Composite analysis between the aflatoxin production and AFLP data was found to be ineffective in separating the isolate types by aflatoxigenicity. Certain unique fragments, with respect to individual isolates, were also identified that may be used for development of SCAR marker to aid in rapid and precise identification of isolates.

This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro-fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC) strategy are also presented.

This paper presents an application of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for system identification for controller tuning in a pH process. In this paper, the ANN based approach is applied to estimate the system parameters. Once the variations in parameters are identified frequently, GA optimally tunes the controller. The simulation results show that the proposed intelligent technique is effective in identifying the parameters and has resulted in a minimum value of the Integral Square Error, peak overshoot and minimum settling time as compared to conventional methods. The experimental results show that their performance is superior and it matches favorably with the simulation results.