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38 results on '"Tufa, Lemma Dendena"'

6. Aspen Plus® Simulation Studies of Steam Gasification in Fluidized Bed Reactor for Hydrogen Production Using Palm Kernel Shell

Author

Hussain, Maham, Tufa, Lemma Dendena, Yusup, Suzana, Zabiri, Haslinda, Taqvi, Syed A., Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Mohamed Ali, Mohamed Sultan, editor, Wahid, Herman, editor, Mohd Subha, Nurul Adilla, editor, Sahlan, Shafishuhaza, editor, Md. Yunus, Mohd Amri, editor, and Wahap, Ahmad Ridhwan, editor

Published
2017
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7. Artificial Neural Network for Anomalies Detection in Distillation Column

Author

Taqvi, Syed A., Tufa, Lemma Dendena, Zabiri, Haslinda, Mahadzir, Shuhaimi, Shah Maulud, Abdulhalim, Uddin, Fahim, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Mohamed Ali, Mohamed Sultan, editor, Wahid, Herman, editor, Mohd Subha, Nurul Adilla, editor, Sahlan, Shafishuhaza, editor, Md. Yunus, Mohd Amri, editor, and Wahap, Ahmad Ridhwan, editor

Published
2017
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14. Simultaneous fault diagnosis based on multiple kernel support vector machine in nonlinear dynamic distillation column

Author

Taqvi, Syed Ali Ammar, Zabiri, Haslinda, Uddin, Fahim, Naqvi, Muhammad, Tufa, Lemma Dendena, Kazmi, Majida, Rubab, Saddaf, Naqvi, Salman Raza, Maulud, Abdulhalim Shah, Taqvi, Syed Ali Ammar, Zabiri, Haslinda, Uddin, Fahim, Naqvi, Muhammad, Tufa, Lemma Dendena, Kazmi, Majida, Rubab, Saddaf, Naqvi, Salman Raza, and Maulud, Abdulhalim Shah

Abstract

Although numerous works have been done, most of the studies in fault diagnosis are limited to single fault type at a time. Majority of the works reported in the literature do not extend the diagnosis of the root cause of the fault for simultaneous faults specifically in the distillation column. However, an industrial system is susceptible to more than one fault at a time, which may or may not be interrelated. These faults not only reduce the diagnosis performance but also increase the computational complexity of the diagnosis algorithm. In this work, therefore, a multiple kernel support vector machine (MK-SVM) algorithm is proposed to diagnose simultaneous faults in the distillation column. In the developed MK-SVM algorithm, multilabel approach based on various kernel functions has been utilized for the classification of simultaneous faults. Dynamic simulation of a pilot-scale distillation column using Aspen Plus(R) is used for generating data in normal and faulty operation. Eight different fault types are considered, including valve sticking at reflux and reboiler, tray upsets, loss of feed flow, feed composition, and feed temperature changes. In the classification of simultaneous faults, a combination of two, three, and four faults is introduced for the performance evaluation of the proposed MK-SVM algorithm. The result showed that the proposed MK-SVM has a high fault detection rate (FDR) of 99.51% and a very low misclassification rate (MR) of 0.49%. The MK-SVM-based classification is better with the F1 score of >97% for all combinations of faults. Moreover, it is observed that the proposed MK-SVM shows better fault diagnosis for single, multiple, and simultaneous faults as compared to other established machine-learning algorithms.

Published
2022
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31. Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model.

Author

TUAN, TRUONG THANH, TUFA, LEMMA DENDENA, MUTALIB, MOHAMMAD IBRAHIM ABDUL, and RAMLI, NASSER MOHAMED

Subjects
MATHEMATICAL optimization, PREDICTIVE control systems, PROBLEM solving, KALMAN filtering, PROFIT maximization, FEEDBACK control systems
Abstract

In advanced control, a control target tracks the set points and tends to achieve optimal operation of a process. Model predictive control (MPC) is used to track the set points. When the set points correspond to an optimum economic trajectory that is sent from an economic layer, the process will be gradually reaching the optimal operation. This study proposes the integration of an economic layer and MPC layer to solve the problem of different time scale and unreachable set points. Both layers require dynamic models that are subject to objective functions. The prediction output of a model is not always asymptotically equal to the measured output of a process. Therefore, Kalman filter is proposed as a state feedback to the two-layer integration. The proposed controller only considers the linear empirical model and the inherent model is identified by system identification, which is assumed to be an ample representation of the process. A depropanizer process case study has been used for demonstration of the proposed technique. The result shows that the proposed controller tends to improve the profit of the process smoothly and continuously, until the process reaches an asymptotically maximum profit point. [ABSTRACT FROM AUTHOR]

Published
2018
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32. Conceptual Framework for Using System Identification in Reservoir Production Forecasting.

Author

Negash, Berihun Mamo, Tufa, Lemma Dendena, Marappagounder, Ramasamy, and Awang, Mariyamni Bt.

Subjects
SYSTEM identification, ECONOMIC forecasting, MATHEMATICAL models, POLYNOMIALS, DATA analysis
Abstract

Defining a reliable forecasting model in petroleum reservoir management has always been a challenge. In cases where reservoir description is limited and when fast decision with an acceptable accuracy is required, current methods have significant limitations and restrictions. System identification, which is based on historical data and statistical methods could be promising. However, the complexity of a petroleum reservoir system and the availability of numerous model structures in system identification make it challenging to adapt this method effectively. In this paper, a conceptual framework for using system identification is proposed. Based on a reservoir's recovery mechanism, the conceptual framework will help to systematically select an appropriate model structure from the various model structures available in system identification. The results show that system identification polynomial models can provide very accurate models, in a very short time, to predict performance of reservoirs under primary and secondary recovery mechanisms. These models have also the potential to be established as a practical, cost-effective and robust tool for forecasting reservoir fluid production. [ABSTRACT FROM AUTHOR]

Published
2016

38. Performance prediction of a reservoir under gas injection using Box-Jenkins model

Author

Negash, Berihun Mamo, Tufa, Lemma Dendena, and Ramasamy, M.

Abstract

Current approaches used in prediction of a reservoir performance are less reliable and require much computational effort and time. In this study, system identification, which is common in economics and similar fields, is proposed for predicting performance of a reservoir under gas injection. Efficacy of the proposed method is verified using two synthetic case studies. Box-Jenkins, model structure which is common in system identification, is applied to capture dynamics of the reservoirs and predict their future performance. Box-Jenkins model with order BJ(2-2-2-2-1) is found to be efficient in terms of fitness during validation and having a reasonably less number of parameters. The model is validated using cross validation principle and the results found are auspicious for further investigation. Comparison of BJ(2-2-2-2-1) model with decline curve analysis, which is a well-recognised technique in oil and gas industry, shows that BJ(2-2-2-2-1) is superior in forecasting reservoir performance under gas injection. [Received: October 25, 2014; Accepted: January 31, 2015]

Published
2016
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