Modelling of cutting forces as a function of cutting parameters in milling process using regression analysis and artificial neural network

38 - Artificial Neural Network Applications in Power Electronics and Electrical Drives

This article discusses the applications of Artificial Neural Network (ANN) and Fuzzy Logic Control (FLC) strategies in a control context of a grid connected Wind Energy Conversion System (WECS), using a Doubly-Fed Induction Generator (DFIG). To rigorously explore the performance of the controllers, a systemic approach based on an ideal model and a nominal model of the wind turbine is used for the comparative analysis between PI, FLC and ANN. It turns out that the PI is less efficient than ANN and FLC. It turns out that the PI is less efficient than ANN and FLC. Whereas, for a high level of control, the FLC has better performance than the ANN. For a low level, the performance of the ANN is very slightly superior to the FLC. In addition, one notes a good rejection of disturbance and a good robustness with the FLC compared to the ANN. The latter has a Total Harmonic Distortion (THD) and cos ϕ ≈ 1 slightly better than the FLC. In short, the ANN and FLC present great advantages for the WECS and the results obtained are satisfactory. The platform used for modeling and simulation studies is MATLAB/Simulink.KeywordsWind Energy Conversion SystemFuzzy Logic Control (FLC)Artificial Neural Network (ANN)DFIGPower grid

Development and application of reservoir models and artificial neural networks for optimizing ventilation air requirements in development mining of coal seams

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. The ANN has recently been applied in process control, identification, diagnostics, character recognition, sensory prediction, robot vision, and forecasting. In Textiles and Clothing industries, it involves the interaction of a large number of variables. Because of the high degree of variability in raw materials, multistage processing and a lack of precise control on process parameters, the relation between such variables and the product properties is relied on the human knowledge but it is not possible for human being to remember all the details of the process-related data over the years. As the computing power has substantially improved over last decade, the ANN is able to learn such datasets to reveal the unknown relation between various variables effectively. Therefore, the application of ANN is more widespread in textiles and clothing industries over last decade. In this chapter, it aims to review current application of ANN in textiles and clothing industries over last decade. Based on literature reviews, the challenges encountered by ANN used in the industries will be discussed and the potential future application of ANN in the industries will also be addressed. The structure of this chapter comprises of seven sections. The first section includes background of ANN, importance of ANN in textiles and clothing and the arrangement of this chapter. In forthcoming three sections, they include review of applications of ANN in fibres and yarns, in chemical processing, and in clothing over last decade. Afterwards, challenges encountered by ANN used in textiles and clothing industries will be discussed and potential future application of ANN in textiles and clothing industries will be addressed in last section.

The paper presents an innovative method for smoothing fluctuations of heat flux, using the thermal energy storage unit (TES Unit) with phase change material and Artificial Neural Networks (ANN) control. The research was carried out on a pilot large-scale installation, of which the main component was the TES Unit with a heat capacity of 500 MJ. The main challenge was to smooth the heat flux fluctuations, resulting from variable heat source operation. For this purpose, a molten salt phase change material was used, for which melting occurs at nearly constant temperature. To enhance the smoothing effect, a classical control system based on PID controllers was supported by ANN. The TES Unit was supplied with steam at a constant temperature and variable mass flow rate, while a discharging side was cooled with water at constant mass flow rate. It was indicated that the operation of the TES Unit in the phase change temperature range allows to smooth the heat flux fluctuations by 56%. The tests have also shown that the application of artificial neural networks increases the smoothing effect by 84%.

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed. HIGHLIGHTS 1. This paper discusses application of ANN in different emerging trends in food process. 2. Application of ANN to develop non-linear multivariate modeling is illustrated. 3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy. 4. ANN resulted in fast response making it suitable for application in real time systems.

This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN's MSE values are 40 times smaller than the LR's. The test data R value from ANN is 0.9931; thus indicating a high confidence level.

The aim of the paper is to discuss the application of classification functions and artificial neural networks (such as multilayer perceptron and radial basis function) to recognize the risk category of investigated companies. The research is based on data from 295 enterprises that applied for credit in two regional banks operating in Poland. Each firm is described by 13 diagnostic variables and potential borrowers are classified into four classes. The efficiency of classification is evaluated in terms of classification errors calculated from the actual classification made by the credit officers. The results of the experiments show that application of artificial neural networks and classification functions can support the creditworthiness evaluation of borrowers.

This paper presents the practical application of cm artificial neural network (ANN) to the problem of concrete mix in a factory. After a brief introduction to the complex problem of concrete mixes design and a quick review of the fundamental basis of neurocomputation, an optimal neural network model has been developed to cope with such a problem. For training the net, several control mixes have been fabricated recording in all cases both the characteristic 28 days compressive strength and the workability measured in terms of the slump of the Abrams' cone. After the training process of the net, the power of its predictive ability is checked by comparison of the results obtained with those corresponding to four reference mixes; in this way, it is shown that the considered approach can be used in multicriterial search for optimal concrete mixes.

This article presents an application of Artificial Neural Networks (ANNs) and multiple regression models for estimating mean annual maximum discharge (index flood) at ungauged sites. Both approaches were tested for 145 small basins in Slovakia in areas ranging from 20 to 300 km2. Using the objective clustering method, the catchments were divided into ten homogeneous pooling groups; for each pooling group, mutually independent predictors (catchment characteristics) were selected for both models. The neural network was applied as a simple multilayer perceptron with one hidden layer and with a back propagation learning algorithm. Hyperbolic tangents were used as an activation function in the hidden layer. Estimating index floods by the multiple regression models were based on deriving relationships between the index floods and catchment predictors. The efficiencies of both approaches were tested by the Nash-Sutcliffe and a correlation coefficients. The results showed the comparative applicability of both models with slightly better results for the index floods achieved using the ANNs methodology.

Application of response surface methodology and artificial neural networks for optimization of recombinant Oryza sativa non-symbiotic hemoglobin 1 production by Escherichia coli in medium containing byproduct glycerol

Samaria Tertiary field in southeastern Mexico produces extra-heavy to medium grade oil (5 to 23° API) from sand-shale sequences. It is well known that the nuclear magnetic resonance (NMR) logging application can provide the total and effective porosity values, as well as free and related fluids independent of mineralogy. When the NMR logging tool is run in heavy oil deposits, oils of varying viscosities can be distinguished qualitatively. Unfortunately, however, in the presence of heavy oils, this measurement process underestimates the total porosity and effective porosity, and overestimates the bulk volume irreducible water (a key parameter for estimating permeability). Although the literature specifies methods that can correct this porosity and fluid volume bias by using fundamental magnetic resonance principles and conventional borehole information, they are only effective when the drilling fluid used is water-based mud. The artificial neural networks (ANN) have traditionally been used to correct well log data corrupted by tension pulls on the tool or degraded by poor borehole conditions. They have also been used for cases in which it is not feasible to obtain a traditional set of openhole well log data. In such cases, a neural network is used to generate synthetic openhole data from pulsed neutron logging data. In this application of ANN, the data is first manually interpreted to delineate uncorrupted zones of the data. This interpretation will correspond to zones in which there is no hydrocarbon or the hydrocarbon type is not heavy oil. The aggregate of such zones is used to define a training set. The remaining data is used to define the application data set. Traditional openhole data (such as resistivity, neutron, bulk density, and thorium) is used to train a neural network to predict NMR fluid volumes. The network obtained is then applied to the application data set, and it is shown that the resulting predicted NMR fluid volumes have been corrected for heavy oil effects in the presence of oil-based mud. This paper demonstrates the effectiveness of this approach and discusses the results obtained. It also demonstrates how the process can be automated.

An artificial neural network model of a continuous stirred ultrafiltration process, is proposed in the present study, which is able to predict permeate volumetric flux and permeate concentration at different bulk concentration, stirrer speed, pressure and time. Because of the complexity in generalization of the phenomenon of ultrafiltration by any mathematical model, the neural network proves to be a very promising method for the purpose of process simulation. The network uses the Back-propagation Algorithm for evaluating the connection strengths, representing the correlations between inputs (bulk concentration, stirrer speed, pressure and time) and output (permeate concentration and flux). The network employed in the present study uses four input nodes corresponding to the operating variables, and two output nodes corresponding to the measurement of the performance of the network (flux and permeate concentration). Experiments were performed to constitute the learning databases for the continuous stirred ultrafiltration process using PEG-6000 solute, and cellulose acetate membrane of 5000 MWCO. The network employed in the present study uses two hidden layers, with the optimum number of nodes being thirty and twenty. A leaning rate of 0.3, and momentum factor of 0.4 was used. The results predicted by the model were in good agreement with the experimental data, and the average deviations for all the cases are found to be well within ±10 %.

Artificial neural network (ANN) applications in immunology include simulations of peptide binding to histocompatibility complex molecules, which present peptides for recognition by the immune system. These peptides are derived from protein antigens and represent prime targets for vaccine discovery. ANN models have proven superior when compared to the alternative models. Applications of ANN models help minimise the number of necessary wet-lab experiments. In this article we describe three specific applications in which targets of immune recognition have been determined from diabetes-, melanoma-, and malaria-related antigens.

Application of artificial neural networks to the simulation of a Dedicated Outdoor Air System (DOAS)