Nitrification/denitrification in a submerged anaerobic membrane bioreactor (SAMBR) for nitrogen removal

Due to serious drawbacks in the conventional nitrification-denitrification process for nitrogen removal from wastewaters, such as high energy and carbon inputs and significantly increased sludge production, the more efficient and cost-effective Anammox process is starting to be used more widely. Anammox is an anaerobic biological process where around equimolar amounts of NH4+ and NO2- are oxidised/reduced, respectively, to produce dinitrogen gas without using a carbon donor. In the first part of this study, the start-up and performance of the Anammox process were evaluated in a 3-litre submerged anaerobic membrane bioreactor (SAMBR). The start-up using seed culture from an anaerobic digester (Anglian Water, UK) was relatively quick (60 and 70 d) for SAMBR 1 (HRT = 2d) and SAMBR 2 (HRT = 4d), respectively, compared to other reports in the literature. Both reactors showed quite high NH4+ and NO2- removal efficiencies of over 80% and 65%, respectively, resulting in a molar ratio of NH4+ to NO2- consumption of 1:0.9, which was comparable to recently reported values in the literature which are lower than the originally cited ratio of 1:1.32. Despite different HRTs, there were no significant differences in performance between the reactors. The use of a flat sheet membrane panel (~0.4 m pore size-Kubota, UK) was shown to be capable of shortening the start-up period for Anammox compared to continuous flow through reactors such as conventional CSTRs. The second major part of this work examined the novel process of partial pre-oxidation of NH4+ using nanofiltration (NF) hollow fiber membrane modules, to provide a feasible alternative to conventional partial nitrification preceding the Anammox process. Prior to investigating the feasibility of this process, ammonium oxidising bacteria (AOB) were enriched from both activated sludge and full-scale SAMBR sludge, in batch reactors, in order to determine whether it was possible to use anaerobic sludge as a source of nitrifiers. The enriched AOB demonstrated stable and high nitrifying activity throughout the enrichment period of 200-300 days, with average NH4+ removal efficiencies of over 90%. In the pre-oxidation process, AOB in the shell-side of the membrane unit was shown to be capable of oxidising the NH4+ mainly to NO2- which then diffused back into the tube side, resulting in a mixture of NH4+ and NO2- in the exit stream from the membrane unit. It was found that only flow rates of above 3.0 L/h were feasible, with a maximum NH4+ flux in the range of 8 – 10 g/m2 h. After 48 hours of operation, and at a flow rate of 5.0 L/h, an approximately equimolar ratio of NH4+ to NO2- was observed in the exit stream, and this would meet the requirement for the Anammox process as suggested by previous reports. This study has demonstrated the potential benefits of applying the Anammox process in a SAMBR for the treatment of nitrogen-containing wastewater as it could reduce the process start-up period, and the operation can be carried out at a short HRT. The application of a membrane process for the pre-oxidation of NH4+ was found to be reasonably promising at a laboratory-scale, and practically viable at a scale similar to actual SHARON reactor (Whitlingham STC, UK) based on an estimation of the number of HF modules needed. However, a proper optimisation study of the process is strongly recommended so that its feasibility could be further examined at a larger scale linking both processes together.Open Acces

Pre-oxidation of ammonium using nanofiltration membranes for partial nitrification preceding Anammox

This study examined the pre-oxidation of ammonium using a nanofiltration (NF) hollow fiber membrane module to provide an alternative to conventional partial nitrification preceding Anammox. A permeability study showed that NF membranes were suitable for use in the pre-oxidation of ammonium due to high ammonium fluxes across the membrane (low rejection of ammonium), while most COD (in the form of glucose) was retained inside the bulk phase (high rejection of glucose-COD). In pre-oxidation, ammonium oxidizing bacteria (AOB) grown in the shell-side of the membrane module could oxidize the ammonium diffusing from the tube side mainly to nitrite, which then diffused back into the tube side, resulting in a mixture of ammonium and nitrite in the exit stream in an approximately equimolar ratio. This would meet the requirement for the Anammox process as suggested by previous reports. The application of a membrane process for the pre-oxidation of ammonium was found to be promising at a laboratory-scale, and practically viable at a scale similar to a full-scale reactor (Whitlingham STC, Norwich, UK) based on an estimate of the number of HF modules needed. However, a proper optimization study of the process is strongly recommended so that its feasibility could be further examined at a larger scale linking both nitritification and Anammox together

Computational issues in process optimisation using historical data.

This thesis presents a new generic approach to improve the computational efficiency of neural-network-training algorithms and investigates the applicability of its 'learning from examples'' featured in improving the performance of a current intelligent diagnostic system. The contribution of this thesis is summarised in the following two points: For the first time in the literature, it has been shown that significant improvements in the computational efficiency of neural-network algorithms can be achieved using the proposed methodology based on using adaptive-gain variation. The capabilities of the current Knowledge Hyper-surface method (Meghana R. Ransing, 2002) are enhanced to overcome its existing limitations in modelling an exponential increase in the shape of the hyper-surface. Neural-network techniques, particularly back-propagation algorithms, have been widely used as a tool for discovering a mapping function between a known set of input and output examples. Neural networks learn from the known example set by adjusting its internal parameters, referred to as weights, using an optimisation procedure based on the 'least square fit principle'. The optimisation procedure normally involves thousands of iterations to converge to an acceptable solution. Hence, improving the computational efficiency of a neural-network algorithm is an active area of research. Various options for improving the computational efficiency of neural networks have been reviewed in this thesis. It has been shown in the existing literature that the variation of the gain parameter improves the learning efficiency of the gradient-descent method. However, it can be concluded from previous researchers' claims that the adaptive-gain variation improved the learning rate and hence the efficiency. It was discovered in this thesis that the gain variation has no influence on the learning rate; however, it actually influences the search direction. This made it possible to develop a novel approach that modifies the gradient-search direction by introducing the adaptive-gain variation. The proposed method is robust and has been shown that it can easily be implemented in all commonly used gradient- based optimisation algorithms. It has also been shown that it significantly improves the computational efficiency as compared to existing neural-network training algorithms. Computer simulations on a number of benchmark problems are used throughout to illustrate the improvement proposed in this thesis. In a foundry a large amount of data is generated within the foundry every time a casting is poured. Furthermore, with the increased number of computing tools and power there is a need to develop an efficient, intelligent diagnostic tool that can learn from the historical data to gain further insight into cause and effect relationships. In this study the performance of the current Knowledge Hyper-surface method was reviewed and the mathematical formulation of the current Knowledge Hyper-surface method was analysed to identify its limitations. An enhancement is proposed by introducing mid-points in the existing shape formulation. It is shown that the midpoints' shape function can successfully constrain the shape of decision hyper-surface to become more realistic with an acceptable result in a multi-dimensional case. This is a novel and original approach and is of direct relevance to the foundry industry

Predicting Depression Using Social Media Posts

The use of Social Network Sites (SNS) is on the rise these days, particularly among the younger generations. Users can communicate their interests, feelings, and everyday routines thanks to the availability of social media sites. Many studies show that properly utilizing user-generated content (UGC) can aid in determining people's mental health status. The use of the UGC could aid in the prediction of mental healthparticularly depression where it is a significant medical condition that impairs one's ability to work, learn, eat, sleep, and enjoy life. However, all of the information about a person's mood and negativism can be gather from their SNS user profile. Therefore, this study utilize SNS as a data source by using machine learning models to screen and identify users in categorizing users based on their mental health. The performance of three machine learning models are evaluated to classify the UGC which are : Decision Forest, Neural Network and Support Vector Machine (SVM). The resuls shows that the accuracy and recall result of the Neural Network model is the same as the Support Vector Machine (SVM) model which is 78.27% and 0.042 but Neural Network performs better in the average precision value. This proves that the Neural Network model is the best models for making predictions to determine the level of depression by using social media posts

Comparative analysis of text classification algorithms for automated labelling of quranic verses

The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verse using text classification algorithms. We applied three text classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as “Shahadah” (the first pillar of Islam) or “Pray” (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses

Early Detection of Dengue Disease Using Extreme Learning Machine

Dengue disease is one of the serious and dangerous diseases that cause many mortality and spread in most area in Indonesia. There are about 201,885 cases had been reported in 2016 including 1,585 death cases. The availability of nowadays clinical data of Dengue disease can be used to train machine learning algorithm in order to automaticaly detect the present of Dengue disease of the patients.  This study will use the Extreme Learning Machine (ELM) method to classify the dengue by using the clinical data so that first aid can be given which can decrease some death risk. The back propagation neural network is one of the popular machine learning technique that capable of learning some complex relationship and had been used in many applications. However, back propagation neural network still suffers with some limitations such as slow convergence and easily getting stuck in local minima during training. Therefore, this research proposed an improved algorithm known as ELM which is an extension of Feed Forward Neural Network that utilize the Moore Penrose Pseudoinver matrix that gain the optimal weights of neural network architecture. The proposed ELM prevents several backpropagation issues by reducing the used of many parameters that solves the main drawbacks of Backpropagation algorithm that uses during the training phase of Neural Network. The result shows that the proposed ELM with selected clinical features can produce best generalization performance and can predict accurately with 96.94% accuracy. The proposed algorithm achieves better with faster convergence rate than the existing state-of-the-art hierarchical learning techniques. Therefore, the proposed ELM model can be considered as an alternative algorithm to apply for early detection of Dengue disease

The Effect of Adaptive Gain and Adaptive Momentum in Improving Training Time of Gradient Descent Back Propagation Algorithm on Classification Problems

The back propagation algorithm has been successfully applied to wide range of practical problems. Since this algorithm uses a gradient descent method, it has some limitations which are slow learning convergence velocity and easy convergence to local minima. The convergence behaviour of the back propagation algorithm depends on the choice of initial weights and biases, network topology, learning rate, momentum, activation function and value for the gain in the activation function. Previous researchers demonstrated that in ‘feed forward’ algorithm, the slope of the activation function is directly influenced by a parameter referred to as ‘gain’. This research proposed an algorithm for improving the performance of the current working back propagation algorithm which is Gradien Descent Method with Adaptive Gain by changing the momentum coefficient adaptively for each node. The influence of the adaptive momentum together with adaptive gain on the learning ability of a neural network is analysed. Multilayer feed forward neural networks have been assessed. Physical interpretation of the relationship between the momentum value, the learning rate and weight values is given. The efficiency of the proposed algorithm is compared with conventional Gradient Descent Method and current Gradient Descent Method with Adaptive Gain was verified by means of simulation on three benchmark problems. In learning the patterns, the simulations result demonstrate that the proposed algorithm converged faster on Wisconsin breast cancer with an improvement ratio of nearly 1.8, 6.6 on Mushroom problem and 36% better on  Soybean data sets. The results clearly show that the proposed algorithm significantly improves the learning speed of the current gradient descent back-propagatin algorithm

CSLM: Levenberg Marquardt based Back Propagation Algorithm Optimized with Cuckoo Search

Training an artificial neural network is an optimization task, since it is desired to find optimal weight sets for a neural network during training process. Traditional training algorithms such as back propagation have some drawbacks such as getting stuck in local minima and slow speed of convergence. This study combines the best features of two algorithms; i.e. Levenberg Marquardt back propagation (LMBP) and Cuckoo Search (CS) for improving the convergence speed of artificial neural networks (ANN) training. The proposed CSLM algorithm is trained on XOR and OR datasets. The experimental results show that the proposed CSLM algorithm has better performance than other similar hybrid variants used in this study