Researcher:
Arkun, Yaman

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Yaman

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Arkun

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Researcher Of Publications: search.filters.isAuthorOfPublication.e12d2ff5-2fba-4748-965c-327ab4364381

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Now showing 1 - 10 of 45
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    KLE-(V)AR: A new identification technique for reduced order disturbance models with application to sheet forming processes
    (Elsevier Sci Ltd, 2001) Rigopoulos; Apostolos; Department of Chemical and Biological Engineering; Arkun, Yaman; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 108526
    A new identification technique that combines the Karhunen-Loeve expansion (KLE) with the use of Vector AutoRegressive processes (VAR) is presented in this paper. Given measurements, collected over a period of time, of a set of correlated random variables the method generates a reduced order state-space dynamic model describing the spatial and temporal relationship among the variables. Some of the advantages of the new method are the fewer number of parameters needed to be estimated compared with traditional subspace methods, and its ability to efficiently track nonstationary random processes. Simulation examples from high dimensional sheet forming processes are included for illustration. (C) 2001 Elsevier Science Ltd. All rights reserved.
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    Gap metric concept and implications for multilinear model-based controller design
    (Amer Chemical Soc, 2003) Galan, O.; Romagnoli, J.A.; Palazoglu, A.; Department of Chemical and Biological Engineering; Arkun, Yaman; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 108526
    The gap metric concept is used within the context of multilinear model-based control. The concept of distance between dynamic systems is used as a criterion for selecting a set of models that can explain the nonlinear plant behavior in a given operating range. The case studies presented include a CSTR and a pH neutralization reactor. The gap metric is used to analyze the relationships among candidate models, resulting in a reduced model set that provides enough information to design multilinear controllers. The simulation and experimental results indicate good performance and stability features.
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    ADCHEM 2009 special issue
    (Elsevier Sci Ltd, 2010) Engell, Sebastian; Department of Chemical and Biological Engineering; Arkun, Yaman; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 108526
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    Plant-wide hierarchical optimization and control of an industrial hydrocracking process
    (Elsevier Sci Ltd, 2013) Çakal, Berna; Gökçe, Dila; Kuzu, Emre; N/A; Department of Chemical and Biological Engineering; Şıldır, Hasan; Arkun, Yaman; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; 242076; 108526
    Hydrocracking is a crucial refinery process in which heavy hydrocarbons are converted to more valuable, low-molecular weight products. Hydrocracking plants operate with large throughputs and varying feedstocks. In addition the product specifications change due to varying economic and market conditions. In such a dynamic operating environment, the potential gains of real-time optimization (RTO) and control are quite high. At the same time, real-time optimization of hydrocracking plants is a challenging task. A complex network of reactions, which are difficult to characterize, takes place in the hydrocracker. The reactor effluent affects the operation of the fractionator downstream and the properties of the final products. In this paper, a lumped first-principles reactor model and an empirical fractionation model are used to predict the product distribution and properties on-line. Both models have been built and validated using industrial data. A cascaded model predictive control (MPC) structure is developed in order to operate both the reactor and fractionation column at maximum profit. In this cascade structure, reactor and fractionation units are controlled by local decentralized MPC controllers whose set-points are manipulated by a supervisory MPC controller. The coordinating action of the supervisory MPC controller accomplishes the transition between different optimum operating conditions and helps to reject disturbances without violating any constraints. Simulations illustrate the applicability of the proposed method on the industrial process.
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    Use of gap metric for model selection in multi-model based control design: an experimental case study of PH control
    (Elsevier, 2000) Palazoglu, Ahmet; Romagnoli, J.A.; Galan, O.; Department of Chemical and Biological Engineering; Arkun, Yaman; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 108526
    The gap metric concept is extended to multi-linear model-based control framework. The concept of distance between systems is used as a criterion to select a set of models that can explain the nonlinear plant behavior. Gap metric is used to analyze the relationships among candidate models, resulting in a reduced model set which provides enough information to design a H∞-robust controller.
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    Steady-state modeling of an industrial hydrocracking reactor by discrete lumping approach
    (WCECS, 2012) Canan, Ümmühan; Department of Chemical and Biological Engineering; Arkun, Yaman; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 108526
    In this study, a steady-state model of an industrial hydrocracking reactor was developed by using discrete lumping approach. Discrete lumping considers the reaction mixture to be composed of discrete pseudo-compounds (lumps) based on their true boiling points. The model parameters were estimated by using real data from an industrial hydrocracking unit. The effects of catalyst deactivation on model parameters were investigated and temperature sensitivity was introduced to the model. Since the model consists of a set of ordinary differential equations and algebraic equations which have to be solved simultaneously, a code was written by using MATLAB. It was shown that the model predictions for temperature profile, product distribution and hydrogen consumption were in good agreement with real plant data.
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    A new approach to defining a dynamic relative gain
    (Elsevier Sci Ltd, 2003) Mc Avoy, T.; Chen, R.; Robinson, D.; Schnelle, P.D.; Department of Chemical and Biological Engineering; Arkun, Yaman; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 108526
    A new approach to defining a dynamic RGA (DRGA) is presented. The approach assumes the availability of a dynamic process model which is used to design a proportional output optimal controller. The new DRGA is defined based on the resulting controller gain matrix. Two examples in which the traditional RGA gives the wrong pairings and an inaccurate indication of the amount of interaction present are discussed. One example involves transfer function models and the other an industrial recycle/reactor system. In both cases the new DRGA indicates the best pairings to use and it accurately assesses the extent of interaction present.
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    Economic model predictive control of an industrial fluid catalytic cracking plant
    (AIChE, 2014) Arı, Aslı; Doğan, İbrahim; Harmankaya, Murat; N/A; Department of Chemical and Biological Engineering; Şıldır, Hasan; Arkun, Yaman; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; 242076; 108526
    Fluid catalytic cracking (FCC) is an important refinery process by which heavy hydrocarbons are cracked to form lighter valuable products over catalyst particles. FCC plants consist of the riser (reactor), the regenerator, and the fractionator that separates the riser effluent into the useful end products. In FCC plants the product specifications and feedstocks change due to varying economic and market conditions. In addition, FCC plants operate with large throughputs and a small improvement realized by optimization and control yields significant economic return. In previous work, we developed a nonlinear dynamic model and validated it with industrial data. In this study, our focus involves the development and application of a real-time optimization framework. We propose a hierarchical structure which includes a two-layer implementation of economic model predictive control (EMPC). EMPC provides the optimal riser and the regenerator temperature reference trajectories which are determined from a dynamic optimization problem maximizing the plant profit. A regulatory model predictive controller (RMPC) manipulates the catalyst circulation rate and the air flow rate to track the reference trajectories provided by EMPC. We consider changes in product prices and the feed content, both of which necessitate online optimization. Dynamic simulations show that the proposed hierarchical control structure achieves optimal tracking of plant profit during transitions between different operating regimes thanks to the combined efforts of EMPC and RMPC.
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    Modeling and analysis of Gab1 mediated feedback loops to understand Gab's role in erk-akt signaling and cance
    (IEEE, 2019) Department of Chemical and Biological Engineering; Arkun, Yaman; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 108526
    A mathematical model is presented to study the dynamics of the docking protein Gab1 that plays an important role in the regulation of ERK and AKT signaling pathways. Model predictions can be used to understand the role of Gab1 in the development of cancer which can give insight into targeted therapy.
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    Optimization of operations in supply chain systems using hybrid systems approach and model predictive control
    (amer Chemical Soc, 2006) N/A; N/A; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Mestan, Esen; Türkay, Metin; Arkun, Yaman; PhD Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 24956; 108526
    This paper addresses the optimal operation of multiproduct supply chain systems, using Model Predictive Control (MPC). the supply chain considered in this paper is a hybrid system governed by continuous/discrete dynamics and logic rules. for optimization purposes, it is modeled within the framework of the Mixed Logical Dynamical (MLD) system and the overall profit is optimized through MPC. Dynamic responses of the different nodes of the supply chain are analyzed when the supply chain is subjected to unknown but measurable changes in customer demand. the performances of a centralized decision-making scheme and two types of decentralized decision making schemes are compared.