Application of neural network techniques to the ion-exchange process and prediction of abrasiveness characteristics of thermal coal

Abstract: The construction of a model for the prediction of process outputs is a valuable tool in the field of engineering. The models play an important role in the simulation and optimization of systems leading to the design of efficient and economical processes. Since 1943 neural network (NN) techniques have been considered as promising tools for use in simulation, prediction and modelling because of their simplicity. In this thesis a feed-forward neural network (FFNN) with back-propagation (BP) is used to test its effectiveness in modelling the ion-exchange process. The ion-exchange process has been widely employed in the removal of heavy metals from industrial wastewater. This process is a complex non-linear process involving many factors influencing the chemical process which is not well understood (the ions uptake mechanisms from the pregnant solution, the subsequent step being the elution). In order to improve the performance of the ion-exchange process, optimization and analysis of the process should be accomplished. Modelling and simulation are tools which can be used to achieve the objectives. The experimental design using analysis of variance (ANOVA) was chosen to compare to the NN techniques and for optimizing the effective input parameters (pH, temperature and initial concentration). The FFNN successfully tracked the non-linear behaviour of the ion-exchange process versus the input parameters with a mean square error (MSE), correlation coefficient (R) and mean square relative error (MSRE) of 0.102, 0.998 and 0.004, respectively. The results showed that the FFNN modelling techniques could effectively predict and simulate the highly complex system and non-linear process such as the ion exchange using activated zeolite...D.Tech. (Chemical Engineering

Steam extraction of essential oils : investigation of process parameters

M.Tech.Essential oils are volatile oils, generally odorous, which occur in certain plants or specified parts of plants, and are recovered by accepted procedures, such that the nature and composition of the product is, as nearly as practicable, unchanged by such procedures (ISO, 1968). The principal uses are as: flavouring agent, medicinal and aromatherapy application. Today, the essential oils are sought-after for innumerable applications starting from markers for plant identifications to bases for semi-synthesis of highly complex molecules. The extraction of highly delicate essential oils from plants remains a crucial step in all these applications. By using steam to mediate the extraction, it is possible to maintain mild conditions and effect superior extraction. In the current work, an integrated procedure for steam extraction followed by volatiles sampling and analysis from the leaves of the Eucalyptus tree was explored. There are two problems to overcome in the extraction from solid plant materials: that of releasing the essential oils from solid matrix and letting it diffuse out successfully in a manner that can be scaled-up to industrial volumes. Towards this end, the effect of different parameters, such as temperature, pressure and extraction time on the extraction yield was investigated and the experimental results show that all of these temperatures (90 °C, 97°C, and 99°C), were significant parameters affecting yield. Increase in yield was observed as pressure was increased and the use of high pressure (150 kPa) in steam extraction units permits much more rapid and complete distillation of essential oils over atmospheric pressure. The yield was calculated from the relation between the essential oil mass extracted and the raw material mass used in the extraction. The volatiles, Eucalyptus oil in vapour form released from the leaves were condensed and analyzed using Gas chromatography, and eight major components were found to be contained in this species. A mathematical model based on diffusion of essential oil from the leaves was developed. Using a numerical method, the best diffusion coefficient was established for different operating conditions by comparing the model concentration of oil remaining in the leaves with the experimental amount of oil recovered; hence minimizing the sum of squared errors. It was found that one cannot simply assume that the oil leached and recovered was the same as that originally present in the leaves. The initial mass of oil was determined by fitting the diffusion model to the data. An Arrhenius model was used to account for the effect of temperature. The resulting expression for the diffusion coefficient as a function of temperature can now be used to model the large scale extraction of the essential oils from Eucalyptus leaves