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2013-05-08 08:51:40
Untag Telah Luluskan 35.000 Sarjana
2013-05-08 08:51:25
FKIP Untirta Gelar Gebyar Hardiknas 2013
2013-05-08 08:50:53
Hasil penelitian wajib disertai sosialisasi

Publikasi Penelitian Dosen

Virtual Sensor for Time Series Prediction of Hydrogen Safety Parameter in Degussa Sintering 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.

Penulis : DEDE SUTARYA, DR., ST., MT [PDF File] didownload : 33 x

Sistem Akusisi Data Online Proses Sintering dengan Atmosfir Hidrogen untuk Prediksi Parameter Keselamatan

Meningkatnya penggunaan dan penerapan hidrogen sebagai sumber energi terbarukan atau aplikasi pada proses industri, telah meningkatkan tekanan untuk memastikan penanganan keselamatan serta pemantauannya. Masalah keselamatan yang berkaitan dengan gas hidrogen lebih jauh dihambat oleh mahalnya instrumentasi yang diperlukan untuk mengukur persentase batas ledakan, laju aliran dan tekanan yang dihasilkan. Solusi inovatif untuk mengatasi isuisu keselamatan hidrogen dan sensor fisik yang mahal adalah dengan menggunakan sensor virtual berdasarkan jaringan syaraf tiruan yang belajar dari data untuk memprediksi parameter yang berkaitan dengan keselamatan hidrogen. Penelitian ini bertujuan untuk mendapatkan dataset yang menggambarkan seluruh perilaku proses sintering dengan atmosfir hidrogen. Dalam penelitian ini dikembangkan sebuah sistem akuisisi data untuk proses sintering serta mengujinya pada proses sintering sebenarnya untuk mendapatkan data yang diperlukan. Hasil percobaan pada sistem akuisisi data secara online untuk mengukur variabel proses menunjukkan kinerja sistem yang baik. Data yang diperoleh dapat menggambarkan perilaku keseluruhan hidrogen dalam proses sintering. Dengan demikian data ini dapat digunakan sebagai dataset untuk pemodelan jaringan saraf tiruan dalam memprediksi parameter keselamatan hidrogen dalam studi berikutnya.

Penulis : DEDE SUTARYA, DR., ST., MT [PDF File] didownload : 36 x

Outlier Filtering for Hydrogen Temperature and Flow Rate Time series data in Sintering Process

The data with free of noise or outliers will not be obtained in the chemical or physical process measurements using due to some kind of noise arising from thermodynamics and quantum effects may not be removed. The extensive use of personal computers in process instrumentation and flexibility of programming software, encourages its use for filtering time series data with satisfactory results. This paper will investigate outlier filtering techniques on time series data of temperature and flow rate of hydrogen gas as result of sensor measurement on the sintering process. The results an optimal parameters on the filtering technique that yields an adequate signal to noise ratio while still maintaining peak signal on the measurement results, Its is very important respect to process safety parameters of hydrogen gas.

Penulis : DEDE SUTARYA, DR., ST., MT [PDF File] didownload : 50 x

Identification of Industrial Furnace Temperature for Sintering Process in Nuclear Fuel Fabrication Using NARX Neural Networks

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 − 03 for heating step and 6.3859𝑒 − 08 for soaking step.That result shows the model successfully predict the evolution of the temperature in the furnace.

Penulis : DEDE SUTARYA, DR., ST., MT [PDF File] didownload : 32 x

Assesment of Quality Classification of Green Pellets for Nuclear Power Plants using Improved Levenberg-Marquardt Algorithm

Cylindrical uranium dioxide pellets, which are the main components for nuclear fuel
elements in Light Water Reactor, should have a high density profile, uniform shape and quality for
the safety used as a reactor fuel component. The quality of green pellets is conventionally
monitored through a laboratory measurement of the physical pellets characteristics followed by a
graphical chart classification technique. However, this conventional classification method shows
some drawbacks, such as the difficulties on its usage, low accuracy and time consuming, and does
not have the ability to adress the non-linearity and the complexity of the relationship between the
pellet’s quality variables and the pellett’s quality. In this paper, an Improved Levenberg-Marquard
based neural networks is used to classify the quality process of the green pellets. Robustness of this
learning algorithm is evaluated by comparing its recognition rate to that of the conventional Back
Propagation neural learning algorithm. Results show that the Improved Levenberg-Marquard
algorithm outperformed the Back Propagation learning algorthm for various percentage of
training/testing paradigm, showing that this system could be applied effectively for classification of
pellet quality.

Penulis : DEDE SUTARYA, DR., ST., MT [PDF File] didownload : 33 x

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