Integration of hydrogen network design to the production planning in refineries based on multi-scenarios optimization and flexibility analysis

Silva, Patrícia R. da; Aragão, Marcelo Escobar; Trierweiler, Jorge O.

doi:10.1016/j.cherd.2022.09.003

Cited by 12 publications

(3 citation statements)

References 23 publications

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“…In order to quantify a system's ability to cope with uncertainties, several metrics were suggested in the 1980s, including the flexibility index developed by Swaney and Grossmann 19 which has gained much attention in the literature, and is still subject to research (see, e.g., Di Pretoro et al, 20 da Silva et al, 21 and Luo et al 22 ). The flexibility index is a scalar value that expresses the share of the expected uncertainty space that is feasible.…”

Section: Introductionmentioning

confidence: 99%

Novel Reformulations for Modeling Uncertainty and Variations in a Framework for Chemical Process Design

Langner,

Svensson,

Papadokonstantakis

et al. 2023

Ind. Eng. Chem. Res.

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Section: Introductionmentioning

confidence: 99%

Novel Reformulations for Modeling Uncertainty and Variations in a Framework for Chemical Process Design

Langner,

Svensson,

Papadokonstantakis

et al. 2023

Ind. Eng. Chem. Res.

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“…In linear programming, the algorithm is carried out step by step because it is sequential [48][49][50][51][52], whereas in all types of Petri nets, the processes run in parallel as in the real world. Section 2.2 of this article states that the GN includes, as a special case, the other types of Petri nets because of the presence of token characteristics and transition condition predicates.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Production Process of Fuel Gas, LPG, Propylene, and Polypropylene in a Petroleum Refinery Using Generalized Nets

Stratiev,

Dimitriev,

Stratiev

et al. 2023

Mathematics

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The parallel processes involved in the production of refinery fuel gas, liquid petroleum gas (LPG), propylene, and polypropylene, occurring in thirteen refinery units, are modeled by the use of a Generalized Net (GN) apparatus. The modeling of the production of these products is important because they affect the energy balance of petroleum refinery and the associated emissions of greenhouse gases. For the first time, such a model is proposed and it is a continuation of the investigations of refinery process modelling by GNs. The model contains 17 transitions, 55 places, and 47 types of tokens, and considers the orders of fuel gas for the refinery power station, refinery process furnaces, LPG, liquid propylene, and 6 grades of polypropylene. This model is intended to be used as a more detailed lower-level GN model in a higher-level GN model that facilitates and optimizes the process of decision making in the petroleum refining industry.

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“…Their output is typically fed into linear programming refinery models to evaluate the most economically valuable scenario to follow during the oil-refining process [21,22]. In linear programming, the algorithm is performed step by step because it is sequential [23][24][25][26][27]. In contrast, the use of another approach to modeling processes which run in parallel as it is in the real world is the availing of Petri nets (see, e.g., [28]) and their extensions as Generalized Nets (GNs, see [1]).…”

Section: Introductionmentioning

confidence: 99%

Generalized Net Model of Heavy Oil Products’ Manufacturing in Petroleum Refinery

Stratiev,

Dimitriev,

Stratiev

et al. 2023

Mathematics

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Generalized nets (GNs) are a suitable tool for the modeling of parallel processes. Through them, it is possible to describe the functioning and results of the performance of complex real processes running in time. In a series of articles, we consistently describe the main processes involved in the production of petroleum products taking place in an oil refinery. The GN models can be used to track the actual processes in the oil refinery in order to monitor them, make decisions in case of changes in the environment, optimize some of the process components, and plan future actions. This study models the heavy oil production process in a refinery using the toolkit of GNs. Five processing units producing ten heavy-oil-refined products in an amount of 106.5 t/h from 443 t/h atmospheric residue feed, their blending, pipelines, and a tank farm devoted to storage of finished products consisting of three grades of fuel oil (very low sulfur fuel oil (0.5%S) —3.4 t/h; low sulfur fuel oil (1.0%S) —4.2 t/h; and high sulfur fuel oil (2.5%S) —66.9 t/h), and two grades of road pavement bitumen (bitumen 50/70 —30 t/h and bitumen 70/100 —2 t/h) are modeled in a GN medium. This study completes the process of modeling petroleum product production in an oil refinery using GNs. In this way, it becomes possible to construct a highly hierarchical model that incorporates the models already created for the production of individual petroleum products into a single entity, which allows for a comprehensive analysis of the refinery’s operations and decision making concerning the influence of various factors such as disruptions in the feedstock supply, the occurrence of unplanned shutdowns, optimization of the production process, etc.

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Integration of hydrogen network design to the production planning in refineries based on multi-scenarios optimization and flexibility analysis

Cited by 12 publications

References 23 publications

Novel Reformulations for Modeling Uncertainty and Variations in a Framework for Chemical Process Design

Novel Reformulations for Modeling Uncertainty and Variations in a Framework for Chemical Process Design

Modeling the Production Process of Fuel Gas, LPG, Propylene, and Polypropylene in a Petroleum Refinery Using Generalized Nets

Generalized Net Model of Heavy Oil Products’ Manufacturing in Petroleum Refinery

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