Researcher:
Şıldır, Hasan

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Hasan

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Şıldır

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Şıldır, Hasan

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2011 - 20158

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Researcher Of Publications: search.filters.isAuthorOfPublication.273a8e72-693b-47af-aa72-86b54fdf58da

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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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    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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    Economic model predictive control of an industrial fluid catalytic cracker
    (Amer Chemical Soc, 2014) Ari, Asli; Dogan, Ibrahim; Harmankaya, Murat; Department of Chemical and Biological Engineering; N/A; Arkun, Yaman; Şıldır, Hasan; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 108526; N/A
    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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    A dynamic non-isothermal model for a hydrocracking reactor: model development by the method of continuous lumping and application to an industrial unit
    (Elsevier Sci Ltd, 2012) 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 an important refinery process which is carried out in catalytic reactors to convert heavy petroleum fractions into valuable products. Because of the large number of species and complex reactions involved, modeling of hydrocracking is a challenging task. In this paper a dynamic, non-isothermal reactor model has been constructed using the method of continuous lumping which treats the complex reactive mixture as a continuum. In doing so concentrations are characterized in terms of reactivity which is a monotonic function of the true boiling point of the mixture. The material and energy balances are developed in the form of integro-differential equations. The significant modeling parameters are identified and estimated using data from an industrial reactor. Steady-state and dynamic predictions of the model outputs such as reactor temperature, product yields and hydrogen consumption are shown to be in good agreement with plant data.
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    Dynamic modeling and optimization of an industrial fluid catalytic cracker
    (Elsevier Sci Ltd, 2015) Canan, Ummuhan; Celebi, Serdar; Karani, Utku; Er, İlay; Department of Chemical and Biological Engineering; N/A; Arkun, Yaman; Şıldır, Hasan; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 108526; 242076
    Fluid Catalytic Cracking (FCC) is an important process which is used to convert heavy petroleum fractions into more valuable lighter products. In this work, the FCC process consists of the reactor, the regenerator and the fractionation units. Modeling is challenging due to the complex reaction chemistry and the interactions among the different process units. The reaction medium is modeled by the method of discrete lumping that uses narrow fractions. As a result, the number of discrete lumps (or pseudo-components) to model the process increases and this enables better prediction of fractionation products. For the reactor, we present a new kinetic model that includes a yield function for the cracking products. Kinetic constants and heat of cracking are correlated with the average boiling point of the pseudo-components. These correlations are next used in the development of first-principles models for the riser and the regenerator units. In addition, an empirical model is constructed for the purpose of predicting the individual amounts of the fractionation products from the reactor's effluent. Using parameter estimation, model parameters are estimated from actual industrial data. Model predictions match the plant measurements closely. Simulation and optimization results show that the developed model offers significant potential for use in real-time optimization and control. (C) 2015 Elsevier Ltd. All rights reserved.
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    Modeling of an industrial diesel hydro-processing plant by the continuous lumping approach
    (AIChE, 2014) Canan, Ümmühan; Kartal, Onur; Demir, Fatih; Erdoğan, Murat; N/A; Department of Chemical and Biological Engineering; N/A; Department of Chemical and Biological Engineering; Çelebi, Ayşe Dilan; Aydın, Erdal; Şıldır, Hasan; Arkun, Yaman; Master Student; Faculty Member; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 311745; 242076; 108526
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    Dynamic modeling of an industrial diesel hydroprocessing plant by the method of continuous lumping
    (Elsevier, 2015) Canan, Ümmuhan; İş, Gamze; Erdoğan, Murat; Department of Chemical and Biological Engineering; N/A; Aydın, Erdal; Çelebi, Ayşe Dilan; Şıldır, Hasan; Arkun, Yaman; Faculty Member; Master Student; PHD Student; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; 311745; N/A; 242076; 108526
    Diesel hydroprocessing is an important refinery process which consists of hydrodesulfurization to remove the undesired sulfur from the oil feedstock followed by hydrocracking and fractionation to obtain diesel with desired properties. Due to the new emission standards to improve the air quality, there is an increasing demand for the production of ultra low sulfur diesel fuel. This paper is addressing the development of a reliable dynamic process model which can be used for real-time optimization and control purposes to improve the process conditions of existing plants to meet the low-sulfur demand. The overall plant model consists of a hydrodesulfurization (HDS) model for the first two reactor beds followed by a hydrocracking (HC) model for the last cracking bed. The models are dynamic, non-isothermal, pseudo-homogeneous plug flow reactor models. Reaction kinetics are modeled using the method of continuous lumping which treats the reaction medium as a continuum of species whose reactivities depend on the true boiling point of the mixture. The key modeling parameters are estimated using industrial data. Steady-state and dynamic model predictions of the reactor bed temperatures, sulfur removal, and diesel production match closely the plant data. (C) 2015 Elsevier Ltd. All rights reserved.
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    Modeling and control of an industrial hydrocracker using the discrete and continuous lumping methods and model predictive control
    (AICHE, 2011) Gokce, Dila; Çakal, Berna; Kuzu, Emre; Department of Chemical and Biological Engineering; N/A; N/A; Arkun, Yaman; Canan, Ümmühan; Şıldır, Hasan; Faculty Member; Master Student; N/A; PhD Student; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 108526; N/A; N/A
    Hydrocracking is a catalytic chemical process which converts high-boiling heavy petroleum fractions such as vacuum gas oil into lighter and more valuable products like naphta, diesel, kerosene, gasoline, and LPG. Hydrocracking takes place in the presence of rich hydrogenat elevated temperatures and pressures. The products are free of sulphur and nitrogen compunds which are hydrogenated into hydrogen sulfide and ammonia and which are subsequently removed. The aim of this work is to develop a model for an industrial hydrocracking reactor for optimization and control purposes. Our research is centered around the hydrocraking unit (HCU) of TUPRAS refineries which consists of four catalytic beds with interstage cooling by hydrogen quench. The overall reaction is exothermic and tight control of bed temperatures is crucial for achieving the optimal product distribution. For modeling purposes the methods of discrete and continuous lumping are used. In continuous lumping the reaction mixture is treated as a continuum in which the reaction rate constant kis a continuous function of the true boiling point of the mixture. A yield distribution function p (k,K) is introduced to formulate the amount of species with reactivity k formed from cracking the species with reactivity K. The existing HCU models of this type are steady state models and they do not explicitly include the heat effects. Our continuous lumping model is a pseudohomogeneous non-steady-state plug flow reactor model which includes both the material and energy balances. As such the model is original. In the case of discrete lumping the reaction mixture is characterized in terms of pseudocomponents that are defined for the lumped species boiling in a particular temperature range (i.e. cut). The two sets of lumping methods are fundamentally different approaches, and their joint development provides additional insight into understanding the behavior of HCU and arriving at a final reactor model for optimization and control. Model parameters were estimated using parameter estimation methods. With optimally tuned parameter values, the predicted reactor bed temperatures, hydrogen consumption, conversion and product distributions match TUPRAS' actual plant data very closely. This is demonstrated for different feedstocks in our training and validation data sets. The developed HCU model is used for two purposes: 1) To compute the optimal product distribution and the optimal reactor inlet temperature setpoint values. This economic optimization is performed under steady state conditions. 2) The dynamic model is used by a Model Predictive Controller (MPC) to control the product amounts at their optimal set points. When the product amounts (light naphta, heavy naphta, diesel, kerosene and bottoms) deviate from their optimal values due to feedstock changes, catalyst deactivation and other disturbances, MPC makes the necessary adjustments in the reactor beds inlet temperature setpoints. Temperature increase in each bed and the weighted average bed temperatures (WABT) are the addional variables which are kept within limits by MPC to maintain a desired level of conversion and uniform catalyst deactivation across the beds. Reactor inlet temperatures are changed to their new setpoints by the regulatory PID loops which adjust the hydrogen quenches between beds. This cascade arrangement of MPC and PIDs provides both optimizing and regulatory actions as shown in the figure below. In the presentation, modeling, optimization and control simulations will be given and compared with the present status in the plant.