RETRACTED: Production of Pure Ethanol from Azeotropic Solution by Pressure Swing Adsorption
−Pressure swing adsorption (PSA) is attractive for final separation in the process of water removal especially for fuel ethanol production. Despite many researches on simulation and experimental works on adsorption of water on 3A zeolite in a fixed bed, none have studied a process with the actual PSA system. The purpose of this research was to study the PSA process with two adsorbers and effects of several parameters. The research also included analysis of kinetic and thermodynamic data of ethanol-water adsorption on commercial 3A zeolites in a single fixed bed. A two-level factorial design experiment was used in this research work to preliminarily screen the influence and interaction among the factors. Effects of important parameters such as initial temperature, feed concentration and feed rate were investigated. It was proven that the Langmuir isotherm could best predict the experimental results. In the PSA pilot test, the principal factors, which had effects on the performance, were feed rate, feed concentration, adsorption pressure and the cycle time. Prediction of the process efficiency in terms of ethanol recovery and enrichment was proposed in the form of regression models. The results of the study in a fixed bed adsorber could help designing a pilotscale PSA unit. The experiments proved to be successful in terms of producing high concentration ethanol with high percentage of ethanol recovery. With further simulation work the process could be scaled up for an industrial use.
Pilot study of bioethanol dehydration with polyvinyl alcohol membranes
Reduction of production costs is an essential part in the development of more economic and competitive production processes. Membrane techniques have a great potential to decrease the separation costs in the biofuel production. In this research, the feasibility of pervaporative dehydration of bioethanol to be processed for vehicle fuel usage was studied. The focus was on the feed prefiltration, energy integration and permeation behaviour, which are important factors for membrane stability and product quality. In addition, they have a major impact on the energy efficiency of the ethanol dewatering. Experiments were done by using activated carbon filtration equipment and pervaporation unit equipped with hydrophilic crosslinked polyvinyl alcohol (PVA) membranes. Results indicate that active carbon filtration can provide an efficient and up-scalable pretreatment method for the pervaporation feed. Further, the tested pervaporation membranes provided adequate selectivity, not only for the retentate (ethanol), but also for the permeate (water). Low ethanol concentration in the permeate favors intense integration of energy flows and energy savings.
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.