Coal gasification stripped gas liquor (CGSGL) wastewater contains large quantities of complex organic and inorganic pollutants which include phenols, ammonia, hydantoins, furans, indoles, pyridines, phthalates and other monocyclic and polycyclic nitrogen-containing aromatics, as well as oxygen- and sulphur-containing heterocyclic compounds. The performance of most conventional aerobic systems for CGSGL wastewater is inadequate in reducing pollutants contributing to chemical oxygen demand (COD), phenols and ammonia due to the presence of toxic and inhibitory organic compounds. There is an ever-increasing scarcity of freshwater in South Africa, thus reclamation of wastewater for recycling is growing rapidly and the demand for higher effluent quality before being discharged or reused is also increasing. The selection of hybrid fixed-film bioreactor (HFFBR) systems in the detoxification of a complex mixture of compounds such as those found in CGSGL has not been investigated. Thus, the objective of this study was to investigate the detoxification of the CGSGL in a H-FFBR bioaugmented with a mixed-culture inoculum containing Pseudomonas putida, Pseudomonas plecoglossicida, Rhodococcus erythropolis, Rhodococcus qingshengii, Enterobacter cloacae, Enterobacter asburiae strains of bacteria, as well as the seaweed (Silvetia siliquosa) and diatoms. The results indicated a 45% and 79% reduction in COD and phenols, respectively, without bioaugmentation. The reduction in COD increased by 8% with inoculum PA1, 13% with inoculum PA2 and 7% with inoculum PA3. Inoculum PA1 was a blend of Pseudomonas, Enterobacter and Rhodococcus strains, inoculum PA2 was a blend of Pseudomonas putida iistrains and inoculum PA3 was a blend of Pseudomonas putida and Pseudomonas plecoglossicida strains. The results also indicated that a 70% carrier fill formed a dense biofilm, a 50% carrier fill formed a rippling biofilm and a 30% carrier fill formed a porous biofilm. The autotrophic nitrifying bacteria were out-competed by the heterotrophic bacteria of the genera Thauera, Pseudaminobacter, Pseudomonas and Diaphorobacter. Metagenomic sequencing data also indicated significant dissimilarities between the biofilm, suspended biomass, effluent and feed microbial populations. A large population (20% to 30%) of unclassified bacteria were also present, indicating the presence of novel bacteria that may play an important role in the treatment of the CGSGL wastewater. The artificial neural network (ANN) model developed in this study is a novel virtual tool for the prediction of COD and phenol removal from CGSGL wastewater treated in a bioaugmented H-FFBR. Knowledge extraction from the trained ANN model showed that significant nonlinearities exist between the H-FFBR operational parameters and the removal of COD and phenol. The predictive model thus increases knowledge of the process inputs and outputs and thus facilitates process control and optimisation to meet more stringent effluent discharge requirements. / Thesis (PhD)--University of Pretoria, 2017. / Chemical Engineering / PhD / Unrestricted

Predictive Maintenance (PM) has been increasingly adopted in the Automotive industry, in the recent decades along with conventional approaches such as the Preventive Maintenance and Diagnostic/Corrective Maintenance, since it provides many advantages to estimate the failure before the actual occurrence proactively, and also being adaptive to the present status of the vehicle, in turn allowing flexible maintenance schedules for efficient repair or replacing of faulty components. PM necessitates the storage and analysis of large amounts of sensor data. This requirement can be a challenge in deploying this method on-board the vehicles due to the limited storage and computational power on the hardware of the vehicle. Hence, this thesis seeks to obtain low dimensional descriptive features from high dimensional data using Representation Learning. This low dimensional representation will be used for predicting vehicle faults, specifically Turbocharger related failures. Since the Logged Vehicle Data (LVD) was base on all the data utilized in this thesis, it allowed for the evaluation of large populations of trucks without requiring additional measuring devices and facilities. The gradual degradation methodology is considered for describing vehicle condition, which allows for modeling the malfunction/ failure as a continuous process rather than a discrete flip from healthy to an unhealthy state. This approach eliminates the challenge of data imbalance of healthy and unhealthy samples. Two important hypotheses are presented. Firstly, Parallel StackedClassical Autoencoders would produce better representations com-pared to individual Autoencoders. Secondly, employing Learned Em-beddings on Categorical Variables would improve the performance of the Dimensionality reduction. Based on these hypotheses, a model architecture is proposed and is developed on the LVD. The model is shown to achieve good performance, and in close standards to the previous state-of-the-art research. This thesis, finally, illustrates the potential to apply parallel stacked architectures with Learned Embeddings for the Categorical features, and a combination of feature selection and extraction for numerical features, to predict the Remaining Useful Life (RUL) of a vehicle, in the context of the Turbocharger. A performance improvement of 21.68% with respect to the Mean Absolute Error (MAE) loss with an 80.42% reduction in the size of data was observed.

Machine vision systems combined with classification algorithms are being increasingly used for different applications in the age of automation. One such application would be the quality control of the painted automobile parts. The fundamental elements of the machine vision system include camera, illumination, image acquisition software and computer vision algorithms. Traditional way of thinking puts too much importance on camera systems and ignores other elements while designing a machine vision system. In this thesis work, it is shown that selecting an appropriate illumination for illuminating the surface being examined is equally important in case of machine vision system for examining specular surface. Knowledge about the nature of the surface, type and properties of the defect to be detected and classified are important factors while choosing the illumination system for the machine vision system. The main illumination system tested were bright field, dark field and structured illumination and out of the three, dark field and structured illumination gave best results. This thesis work proposes a dark field illumination based machine vision system for fault detection on specular painted surface. A single layer Artificial Neural Network model is employed for the classification of defects in intensity images of painted surface acquired with this machine vision system. The results of this research work proved that the quality of the images and size of data set used for training the Neural Network model play a vital role in the performance of the classifier algorithm.

Due to the rapidly increasing prominence of Artificial Intelligence in the last decade and the advancements in technology such as processing power and data storage, there has been increased interest in applying machine learning algorithms to time series prediction problems. There are many machine learning algorithms that can be used for time series prediction problems but selecting an algorithm can be challenging due to algorithms not being suitable to all types of datasets. This research investigates and evaluates machine learning algorithms that can be used for time series prediction. Experiments were carried out using the Artificial Neural Network (ANN), Support Vector Regressor (SVR) and Long Short-Term Memory (LSTM) algorithms on eight datasets. An empirical analysis was carried out by applying each machine learning algorithm to the selected datasets. A critical comparison of the algorithm performance was carried out using the Mean Absolute Error (MAE), the Mean Squared Error (MSE), the Root Mean Squared Error (RMSE) and the Mean Absolute Scaled Error (MASE). The second experiment focused on evaluating the stability and robustness of the optimal models identified in the first experiment. The key dataset characteristics identified; were the dataset size, stationarity, trend and seasonality. It was found that the LSTM performed the best for majority of the datasets, due to the algorithm's ability to deal with sequential dependency. The performance of the ANN and SVR were similar for datasets with trend and seasonality, while the LSTM overall proved superior to the aforementioned algorithms. The LSTM outperformed the ANN and SVR due to its ability to handle temporal dependency. However, due to its stochastic nature, the LSTM and ANN algorithms can have poor stability and robustness. In this regard, the LSTM was found to be a more robust algorithm than the ANN and SVR.

Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline in bee population can have tragic consequences, for humans as well as the bees and the ecosystem. Bee health has been a cause of urgent concern for farmers and scientists around the world for at least a decade but a specific cause for the phenomenon has yet to be conclusively identified. This work uses Artificial Intelligence and Computer Vision approaches to develop and analyze techniques to help in continuous monitoring of bee traffic which will further help in monitoring forager traffic. Bee traffic is the number of bees moving in a given area in front of the hive over a given period of time. And, forager traffic is the number of bees entering and/or exiting the hive over a given period of time. Forager traffic is an important variable to monitor food availability, food demand, colony age structure, impact of pesticides, etc. on bee hives. This will lead to improved remote monitoring and general hive status and improved real time detection of the impact of pests, diseases, pesticide exposure and other hive management problems.

Indiana University-Purdue University Indianapolis (IUPUI) / Thermoregulation system in mammal keeps their body temperature in a vital and yet narrow range of temperature by adjusting two main activities, heat generation, and heat loss. Also, these activities get triggered by other causes such as exercise or certain drugs. As a result, thermoregulation system will respond and try to bring back the body temperature to the normal range. Although these responses are very well experimentally explored, they often can be unpredictable and clinically deadly. Therefore, this thesis aims to analytically characterize the neural circuitry components of the system that control the heat generation and heat loss. This modeling approach can help us to analyze the relationship between different components of the thermoregulation system without directly measuring them and explain its complex responses in mathematical form. The first chapter of the thesis is dedicated to introducing a mathematical modeling approach of the circuitry components of the thermoregulation system in response to Methamphetamine which was first published in [1]. Later, in other chapters, we will expand this mathematical framework to study the other components of this system under different conditions such as different circadian phases, various pharmacological interventions, and exercise. This thesis is composed by materials from the following papers. ‎CHAPTER 1 uses the main idea, model, and figures from References [1]. Meanwhile, ‎CHAPTER 2 is based on [2] coauthored with me and is reformatted according to Purdue University Thesis guidelines. Also, ‎CHAPTER 3 interpolates materials from reference [3] coauthored and is reformatted to comply with Purdue University Thesis guidelines. ‎CHAPTER 4 is inserted from the reference [4] and is reformatted according to Purdue University Thesis guidelines. Finally, ‎CHAPTER 5 is based on Reference [5] and is reformatted according to Purdue University Thesis guidelines. Some materials from each of these references have been used in the introduction Chapter.

Indiana University-Purdue University Indianapolis (IUPUI) / Design optimization of composite structures is of importance in the automotive, aerospace, and energy industry. The majority of optimization methods applied to laminated composites consider linear or simplified nonlinear models. Also, various techniques lack the ability to consider the composite failure criteria. Using artificial neural networks approximates the objective function to make it possible to use other techniques to solve the optimization problem. The present work describes an optimization process used to find the optimum design to meet crashworthiness requirements which includes minimizing peak crushing force and specific energy absorption for a square tube. The design variables include the number of plies, ply angle and ply thickness of the square tube. To obtain an effective approximation an artificial neural network (ANN) is used. Training data for the artificial neural network is obtained by crash analysis of a square tube for various samples using LS DYNA. The sampling plan is created using Latin Hypercube Sampling. The square tube is considered to be impacted by the rigid wall with fixed velocity and rigid body acceleration, force versus displacement curves are plotted to obtain values for crushing force, deceleration, crush length and specific energy absorbed. The optimized values for the square tube to fulfill the crashworthiness requirements are obtained using an artificial neural network combined with Multi-Objective Genetic Algorithms (MOGA). MOGA finds optimum values in the feasible design space. Optimal solutions obtained are presented by the Pareto frontier curve. The optimization is performed with accuracy considering 5% error.

No description available.

No description available.

No description available.