| 1. |
- Iplik, Esin, et al.
(författare)
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A Feedforward Model Predictive Controller for Optimal Hydrocracker Operation
- 2022
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Ingår i: Processes. - : MDPI AG. - 2227-9717. ; 10:12, s. 2583-2583
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Tidskriftsartikel (refereegranskat)abstract
- Hydrocracking is an energy-intensive process, and its control system aims at stable product specifications. When the main product is diesel, the quality measure is usually 95% of the true boiling point. Constant diesel quality is hard to achieve when the feed characteristics vary and feedback control has a long response time. This work suggests a feedforward model predictive control structure for an industrial hydrocracker. A state-space model, an autoregressive exogenous model, a support vector machine regression model, and a deep neural network model are tested in this structure. The resulting reactor temperature decisions and final diesel product quality values are compared against each other and against the actual measurements. The results show the importance of the feed character measurements. Significant improvements are shown in terms of product quality as well as energy savings through decreasing the heat duty of the preheating furnace.
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| 2. |
- Iplik, Esin, et al.
(författare)
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Crude-specific optimal operation of hydrodesulfurization
- 2021
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Ingår i: Chemical Engineering Transactions. - : Italian Association of Chemical Engineering - AIDIC. - 1974-9791 .- 2283-9216. ; 86, s. 961-966
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Tidskriftsartikel (refereegranskat)abstract
- Crude oil has different characteristics according to its origin, and this difference causes suboptimal operation if not considered. Similar to other refinery operations, hydrodesulfurization suffers from lacking this knowledge. Information on the true boiling point curve of the feed, next to its sulfur concentration, can be used to optimize the operating temperature. In this work, an optimization problem is demonstrated for two manipulated temperatures of the system and solved by using a gradient-based and a gradient-free algorithm. While the gradient based solution has a single objective of minimum sulfur content, the gradient-free solution has three objectives: minimum sulfur, inlet temperature, and secondary hydrogen flow rate. A continuous lumping model is used to predict the temperature and sulfur responses of a real hydrodesulfurization plant. An adaptive approach is preferred for the model to cope with the catalyst deactivation interference on the product sulfur content constraint. The effect of changing feed on optimality is demonstrated by using eight types of feeds with varying true boiling point and sulfur content. In addition to that, the impact of catalyst age is shown on similar feed processed on different dates.
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| 3. |
- Iplik, Esin
(författare)
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Energy savings for petroleum processing : Using mathematical models, optimal control and diagnostics
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Petroleum products are widely used as an energy supply, and the total production capacity of petroleum refineries is quite high. In this thesis, an energy intensive refinery process, hydroprocessing, is selected and evaluated in terms of its energy loss contributors. Digital solutions are discussed and demonstrated to reduce losses. Both hydrotreatment and hydrocracking processes are included in the evaluation since both require elevated temperatures due to the relevant reactions. While the former is the removal of undesired atoms, the latter is the production of short chain hydrocarbons from heavy oil. Both these processes contribute to cleaner fuel production.When these processes are carried out in fixed bed reactors, the catalyst ages over time, slowing the reactions. Understanding the changes in system dynamics enables the control system to calculate the necessary temperature adjustments to facilitate stable product quality. The usual response is increasing the temperature, which adds to the heat load. If reaction rates are known, the temperature increase can be kept to a minimum. Obtaining real-time feed quality information can aid flexible feed processing refineries intensely. With real-time feed characterization, it is possible to use a feed forward model predictive control system to optimize reactor temperatures. Therefore, for varying crude oil quality, the control system can estimate the minimum temperature requirements for the product to be in the desired quality interval. Additional notice should be given to the temperature sensors as they supply data to the suggested control architecture. Wrong measurements threaten the optimality of the estimated control response. Faulty sensors should be detected and replaced to minimize the risk and collect correct data.Observations made in this thesis show the possible energy gain for hydroprocessing by understanding the aging catalyst, soft sensor installation, feed forward model predictive control, and sensor fault detection. Hydroprocessing is a relevant topic for biorefineries. Although the demonstrations in this work are only for petroleum refineries, the suggested methods can be used in biorefineries as well as integrated co-processing petroleum and biorefineries.
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| 4. |
- Iplik, Esin, et al.
(författare)
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Feasibility study on the use of electrolyzers for short term energystorage
- 2021
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Ingår i: The First SIMS EUROSIM Conference on Modelling and Simulation, SIMS EUROSIM 2021, and 62nd International Conference of Scandinavian Simulation Society. - : Linköping University Electronic Press. - 9789179292195 ; , s. 234-240
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Konferensbidrag (refereegranskat)abstract
- Electricity grid flexibility is vital for renewable energy to be used effectively. Power-to-gas technologies are investigated to connect electricity grid to gas grid and to tackle capacity challenges. Grid management expenses consist of redispatch and feed-in management. These management procedures, next to being costly, cause a significantenergy loss. Proton-exchange membrane electrolyzer installations were studied to reduce these expenses and recover energy. The change in the levelized cost of hydrogen production with varying electrolyzer capacities was presented. The sensitivity of the levelized cost and net presentvalue with respect to installation costs, maintenance costs, and electricity prices were investigated. While the electricity prices have the most significant effect on the levelized cost of hydrogen production, the net present value was affected considerably by the hydrogen selling price. Possible energy savings were calculated between 2 – 23 GWh for 2, 5, 10, 20 MW installations. The annual gridmanagement expense savings were in the range of 0.2– 2.3 million Euros, increasing with the increasing electrolyzer capacity.
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| 5. |
- Iplik, Esin, et al.
(författare)
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Hydrocracking: A Perspective towards Digitalization
- 2020
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Ingår i: Sustainability. - : MDPI AG. - 2071-1050. ; 12:17
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Tidskriftsartikel (refereegranskat)abstract
- In a world of fast technological advancements, it is increasingly important to see how hydrocracking applications can benefit from and adapt to digitalization. A review of hydrocracking processes from the perspective of modeling and characterization methods is presented next to an investigation on digitalization trends. Both physics-based and data-based models are discussed according to their scope of use, needs, and capabilities based on open literature. Discrete and continuous lumping, structure-oriented lumping, and single event micro-kinetic models are reported as well as artificial neural networks, convolutional neural networks, and surrogate models. Infrared, near-infrared, ultra-violet and Raman spectroscopic methods are given with their examples for the characterization of feed or product streams of hydrocracking processes regarding boiling point curve, API, SARA, sulfur, nitrogen and metal content. The critical points to consider while modeling the system and the soft sensor are reported as well as the problems to be addressed. Optimization, control, and diagnostics applications are presented together with suggested future directions of interdisciplinary studies. The links required between the models, soft sensors, optimization, control, and diagnostics are suggested to achieve the automation goals and, therefore, a sustainable operation.
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| 6. |
- Iplik, Esin, et al.
(författare)
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Parameter estimation and sensitivity analysis for a diesel hydro-processing model
- 2021
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Ingår i: Computer Aided Chemical Engineering. - : Elsevier B.V.. ; 50, s. 573-578
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Tidskriftsartikel (refereegranskat)abstract
- Model-based approaches are essential for the operation, optimization, and control of applications in the process industry. Different structures are often investigated to build representative and robust models, and a set of parameters with the same attributes are required to utilize them effectively. Parameter estimation gets arduous with the increasing complexity of the process, the model, and the size of the parameter space. In this work, a parameter-estimation problem based on a steady-state model of diesel hydrodesulfurization is investigated using gradient-based and gradient-free optimizers. The optimal parameter sets obtained are then assessed in terms of performance and computational time for the different optimizers. Furthermore, the sensitivity of the various parameters is also investigated. Due to the catalytic reactions in this process, some parameters have to be updated depending on the catalyst activity. In addition to the initial estimation, the updated parameters are also studied, and instead of a time-based one, a tolerance-based recalculation schedule is suggested. Finally, the robustness of the final model is analyzed by giving different operating conditions and feed characteristics. The adaptive parameter approach proved better data fitting capabilities by improving the coefficient of determination for temperature predictions.
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| 7. |
- Iplik, Esin, et al.
(författare)
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Sensor fault detection with Bayesian networks
- 2020
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Ingår i: Proceedings of The 61st SIMS Conference on Simulation and Modelling, SIMS 2020. - : Linköping University Electronic Press. ; , s. 373-378
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Konferensbidrag (refereegranskat)abstract
- Several sensors are installed in the majority of chemical reactors and storage tanks to monitor temperature profiles for safety and decision-making processes such as heat demand or flow rate calculations. These sensors fail occasionally and generate erroneous measurement data that need to be detected and excluded from the calculations. However, due to the high number of process variables displayed in the chemical plants, this task is not trivial. In this work, a Bayesian network approach to detect faulty temperature sensors is proposed. By comparing the sensor measurements with each other, the faulty sensor is detected. A modular approach is preferred, and networks are created for 10 K temperature intervals to increase flexibility and sensitivity. Created networks can be adjusted for the operating temperature ranges; hence, they can be used for any catalyst and entire life cycle. The developed method is demonstrated on an industrial scale hydrocracker unit with 92 sensor couples installed in a series of reactors. From the investigated sensors, 16 of them showed a greater difference than the 2 K threshold chosen for the fault. In addition to that, 13 sensors showed an increasing temperature difference that may lead to a fault. Two scenarios were created to calculate the energy loss due to a faulty measurement, and a 5.5 K offset error was found to cause a 5.79 TJ energy loss every year for a small scale hydrocracker.
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