Experimental and modelling studies of process intensification for the solvent-antisolvent precipitation of nanoparticles in a spinning disc reactor

Ph. D. Thesis.Solvent-antisolvent precipitation is a key process in pharmaceuticals industries. This research concerns solvent-antisolvent precipitation of starch nanoparticles in the spinning disc reactor (SDR), based on a combination of both experimental and modelling studies. The SDR’s ability to use surface rotation to improve micromixing within thin liquid films, as well as its capability to exhibit near plug flow characteristics is the primary motivation to investigate this process intensification technology for solvent-antisolvent precipitation. One of the objectives of this study is to highlight and understand interactions of the disc surface topography with conditions such as flowrate, solvent-antisolvent ratio and disc speed and their impact on the mixing and precipitation processes. Smaller nanoparticles with narrow particle size distributions (PSDs) were produced as flow rate increased from 6 to 18 mL/s (248 to 175 nm) and disc speed increased from 400 to 1200 rpm (234 to 175 nm). This is attributed to increased shear and instabilities within the liquid film, enhancing mixing as the liquid travels outwards on the disc surface. Increasing the antisolvent to solvent ratio from 1:1 to 9:1 also caused a reduction in size (276 to 175 nm), as greater supersaturation was generated at reduced solubilities, causing nucleation to dominate over particle growth. The disc texture did not significantly affect nanoparticle size; however, particles produced on the grooved disc were of narrower PSD with higher yields. Nucleation rates were determined for the precipitation of starch nanoparticles in the SDR. Nucleation rates increased with an increase in flow rate and disc speed but were a weak function of antisolvent to solvent ratio. The nucleation rate was greater on the grooved surface at the poorer precipitation conditions, as the precipitation then relied primarily on better mixing through the eddies generated by the grooved surface. A maximum nucleation rate of 6.44x1016 mL-1 s -1 was estimated at conditions of 1200 rpm, 9:1 ratio and 15 mL/s, on the smooth disc. Finally, experimentally obtained nucleation kinetics along with growth kinetics have been applied to formulate a predictive PSD model, combining the population balance equations (PBE) with a micromixing model. The model uses Hounslow’s discretisation method to solve the PBEs, accounting for nucleation, growth, and agglomeration in the SDR. Validation of the simulated PSDs has been done through comparison against experimental results. The modelled PSDs are in good agreement with the experimental result

Evaluation of characterization techniques for beneficial use of underutilized slag materials

Wisely using byproduct materials in beneficial use applications such as highway construction is becoming more important in the United States as virgin materials are depleted and landfill capacity declines. Slags are byproducts of the steel and iron industries found in the Midwestern United States. Historically, many of these materials have historically been used in construction applications, but methods for characterizing their environmental risk are limited. This research considers a series of steps used to identify whether a particular slag poses an environmental or human health risk. The first step involves identifying the appropriate use of the material. The second step involves identifying the site-specific parameters such as precipitation rates and expected pH conditions. The third step involves characterizing the material with a set of leaching procedures that test the material under the range of expected site-specific conditions. The majority of this research focused on this characterization step. The final step involves fate and transport modeling of the appropriate leaching data to identify the ultimate constituent concentrations expected at a receptor. (Abstract shortened by UMI.)

Spartan Daily, March 17, 1992

Volume 98, Issue 37https://scholarworks.sjsu.edu/spartandaily/8250/thumbnail.jp

Current, February 03, 1992

https://irl.umsl.edu/current1990s/1068/thumbnail.jp

Motor design for a sub-orbital hybrid rocket.

Masters Degree. University of KwaZulu- Natal, Durban.Abstract available in pdf

REMOTE SENSING DATA ANALYSIS FOR ENVIRONMENTAL AND HUMANITARIAN PURPOSES. The automation of information extraction from free satellite data.

This work is aimed at investigating technical possibilities to provide information on environmental parameters that can be used for risk management. The World food Program (WFP) is the United Nations Agency which is involved in risk management for fighting hunger in least-developed and low-income countries, where victims of natural and manmade disasters, refugees, displaced people and the hungry poor suffer from severe food shortages. Risk management includes three different phases (pre-disaster, response and post disaster) to be managed through different activities and actions. Pre disaster activities are meant to develop and deliver risk assessment, establish prevention actions and prepare the operative structures for managing an eventual emergency or disaster. In response and post disaster phase actions planned in the pre-disaster phase are executed focusing on saving lives and secondly, on social economic recovery. In order to optimally manage its operations in the response and post disaster phases, WFP needs to know, in order to estimate the impact an event will have on future food security as soon as possible, the areas affected by the natural disaster, the number of affected people, and the effects that the event can cause to vegetation. For this, providing easy-to-consult thematic maps about the affected areas and population, with adequate spatial resolution, time frequency and regular updating can result determining. Satellite remote sensed data have increasingly been used in the last decades in order to provide updated information about land surface with an acceptable time frequency. Furthermore, satellite images can be managed by automatic procedures in order to extract synthetic information about the ground condition in a very short time and can be easily shared in the web. The work of thesis, focused on the analysis and processing of satellite data, was carried out in cooperation with the association ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action), a center of research which works in cooperation with the WFP in order to provide IT products and tools for the management of food emergencies caused by natural disasters. These products should be able to facilitate the forecasting of the effects of catastrophic events, the estimation of the extension and location of the areas hit by the event, of the affected population and thereby the planning of interventions on the area that could be affected by food insecurity. The requested features of the instruments are: • Regular updating • Spatial resolution suitable for a synoptic analysis • Low cost • Easy consultation Ithaca is developing different activities to provide georeferenced thematic data to WFP users, such a spatial data infrastructure for storing, querying and manipulating large amounts of global geographic information, and for sharing it between a large and differentiated community; a system of early warning for floods, a drought monitoring tool, procedures for rapid mapping in the response phase in a case of natural disaster, web GIS tools to distribute and share georeferenced information, that can be consulted only by means of a web browser. The work of thesis is aimed at providing applications for the automatic production of base georeferenced thematic data, by using free global satellite data, which have characteristics suitable for analysis at a regional scale. In particular the main themes of the applications are water bodies and vegetation phenology. The first application aims at providing procedures for the automatic extraction of water bodies and will lead to the creation and update of an historical archive, which can be analyzed in order to catch the seasonality of water bodies and delineate scenarios of historical flooded areas. The automatic extraction of phenological parameters from satellite data will allow to integrate the existing drought monitoring system with information on vegetation seasonality and to provide further information for the evaluation of food insecurity in the post disaster phase. In the thesis are described the activities carried on for the development of procedures for the automatic processing of free satellite data in order to produce customized layers according to the exigencies in format and distribution of the final users. The main activities, which focused on the development of an automated procedure for the extraction of flooded areas, include the research of an algorithm for the classification of water bodies from satellite data, an important theme in the field of management of the emergencies due to flood events. Two main technologies are generally used: active sensors (radar) and passive sensors (optical data). Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of day or season, while passive sensors can only be used in the daytime cloud free conditions. Even if with radar technologies is possible to get information on the ground in all weather conditions, it is not possible to use radar data to obtain a continuous archive of flooded areas, because of the lack of a predetermined frequency in the acquisition of the images. For this reason the choice of the dataset went in favor of MODIS (Moderate Resolution Imaging Spectroradiometer), optical data with a daily frequency, a spatial resolution of 250 meters and an historical archive of 10 years. The presence of cloud coverage prevents from the acquisition of the earth surface, and the shadows due to clouds can be wrongly classified as water bodies because of the spectral response very similar to the one of water. After an analysis of the state of the art of the algorithms of automated classification of water bodies in images derived from optical sensors, the author developed an algorithm that allows to classify the data of reflectivity and to temporally composite them in order to obtain flooded areas scenarios for each event. This procedure was tested in the Bangladesh areas, providing encouraging classification accuracies. For the vegetation theme, the main activities performed, here described, include the review of the existing methodologies for phenological studies and the automation of the data flow between inputs and outputs with the use of different global free satellite datasets. In literature, many studies demonstrated the utility of the NDVI (Normalized Difference Vegetation Index) indices for the monitoring of vegetation dynamics, in the study of cultivations, and for the survey of the vegetation water stress. The author developed a procedure for creating layers of phenological parameters which integrates the TIMESAT software, produced by Lars Eklundh and Per Jönsson, for processing NDVI indices derived from different satellite sensors: MODIS (Moderate Resolution Imaging Spectroradiometer), AVHRR (Advanced Very High Resolution Radiometer) AND SPOT (Système Pour l'Observation de la Terre) VEGETATION. The automated procedure starts from data downloading, calls in a batch mode the software and provides customized layers of phenological parameters such as the starting of the season or length of the season and many others

Feature Selection and Classification Methods for Decision Making: A Comparative Analysis

The use of data mining methods in corporate decision making has been increasing in the past decades. Its popularity can be attributed to better utilizing data mining algorithms, increased performance in computers, and results which can be measured and applied for decision making. The effective use of data mining methods to analyze various types of data has shown great advantages in various application domains. While some data sets need little preparation to be mined, whereas others, in particular high-dimensional data sets, need to be preprocessed in order to be mined due to the complexity and inefficiency in mining high dimensional data processing. Feature selection or attribute selection is one of the techniques used for dimensionality reduction. Previous research has shown that data mining results can be improved in terms of accuracy and efficacy by selecting the attributes with most significance. This study analyzes vehicle service and sales data from multiple car dealerships. The purpose of this study is to find a model that better classifies existing customers as new car buyers based on their vehicle service histories. Six different feature selection methods such as; Information Gain, Correlation Based Feature Selection, Relief-F, Wrapper, and Hybrid methods, were used to reduce the number of attributes in the data sets are compared. The data sets with the attributes selected were run through three popular classification algorithms, Decision Trees, k-Nearest Neighbor, and Support Vector Machines, and the results compared and analyzed. This study concludes with a comparative analysis of feature selection methods and their effects on different classification algorithms within the domain. As a base of comparison, the same procedures were run on a standard data set from the financial institution domain