Cylindrical uranium dioxide pellets, which are the main components for nuclear fuel elements in light water reactors, should have a high density profile, a uniform shape, and a minimum standard quality for their safe use as a reactor fuel component. The quality of green pellets is conventionally monitored by laboratory measurement of the physical pellet characteristics; however, this conventional classification method shows some drawbacks, such as difficult usage, low accuracy, and high time consumption. In addition, the method does not address the non-linearity and complexity of the relationship between pellet quality variables and pellet quality. This paper presents the development and application of a modified Radial Basis Function neural network (RBF NN) as an automatic classification system for green pellet quality. The weight initialization of the neural networks in this modified RBF NN is calculated through an orthogonal least squared method, and in conjunction with the use of a sigmoid activation function on its output neurons. Experimental data confirm that the developed modified RBF NN shows higher recognition capability when compared with that of the conventional RBF NNs. Further experimental results show that optimizing the quality classification problem space through eigen decomposition method provides a higher recognition rate with up to 98% accuracy.
Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neural network model are gradually becoming established not only in the academia, but also in industrial application. An identification scheme of nonlinear systems for sintering furnace temperature in nuclear fuel fabrication using neural network autoregressive with exogenous inputs (NNARX) model investigated in this paper. The main contribution of this paper is to identify the appropriate model and structure to be applied in control temperature in the sintering process in nuclear fuel fabrication, that is, a nonlinear dynamical system. Satisfactory agreement between identified and experimental data is found with normalized sum square error 1.9e-03for heating step and6.3859e-08for soaking step. That result shows the model successfully predict the evolution of the temperature in the furnace.
Hydrogen is increasingly investigated as an alternative energy source to petroleum products in industrial application, internal combustion engines (transportation) and electrical power plant. The safety issues related to hydrogen gas are further exasperated by expensive instrumentation required to measure the temperature, flow rates and production pressure. This paper investigates the use of model based virtual sensors in connection with DEGUSSA Sintering furnace with hydrogen gas as process atmosphere for UO2 pellet sintering processes. The virtual sensors are used to predict relevant hydrogen safety parameters, such as hydrogen output temperature, hydrogen pressure and hydrogen flow rate as a function of different input conditions parameters. The virtual sensors are developed by means of the application of various Artificial Intelligent techniques. To train and appraise the neural network models as virtual sensors, the Degussa sintering system is instrumented with necessary sensors to gather experimental data which together with neural networks and adaptive neuro-fuzzy inference systems were used as predictive tools to estimate hydrogen safety parameters. It was shown that using the neurofuzzy inference system, hydrogen safety parameters were predicted with the average RMSE 0.0387, 0.0283, 0.1301 and MAE 0.0241, 0.0115, 0.0355 sequentially for temperature, pressure, and flow rate of hydrogen.
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