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A Feedforward Model Predictive Controller for Optimal Hydrocracker Operation
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Metals, Combustion and Energy, Linde Technology, 85716 Unterschleißheim, Germany.ORCID iD: 0000-0002-1240-5449
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-2978-6217
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-8466-356X
2022 (English)In: Processes, E-ISSN 2227-9717, Vol. 10, no 12, p. 2583-2583Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
2022. Vol. 10, no 12, p. 2583-2583
Keywords [en]
hydrocracking, model predictive control, feedforward control, deep neural network
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-61124DOI: 10.3390/pr10122583ISI: 000903010900001Scopus ID: 2-s2.0-85144843895OAI: oai:DiVA.org:mdh-61124DiVA, id: diva2:1716752
Available from: 2022-12-06 Created: 2022-12-06 Last updated: 2023-01-25Bibliographically approved
In thesis
1. Energy savings for petroleum processing: Using mathematical models, optimal control and diagnostics
Open this publication in new window or tab >>Energy savings for petroleum processing: Using mathematical models, optimal control and diagnostics
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2023
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 372
National Category
Chemical Process Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-61618 (URN)978-91-7485-581-4 (ISBN)
Public defence
2023-03-21, Gamma, Mälardalens universitet, Västerås, 09:00 (English)
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FUDIPO
Available from: 2023-01-25 Created: 2023-01-25 Last updated: 2023-03-09Bibliographically approved

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Iplik, EsinAslanidou, IoannaKyprianidis, Konstantinos

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