Design of an adaptive predictive control strategy for crude oil atmospheric distillation process

Raimondi, Angelo; Favela‐Contreras, Antonio; Beltrán-Carbajal, Francisco; Piñón-Rubio, Alejandro; Peña-Elizondo, Jose Luis de la

doi:10.1016/j.conengprac.2014.09.014

Cited by 14 publications

(14 citation statements)

References 10 publications

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“…Further, the model displays the potential behaviour of the system. However, their design requires a computer program to play with various control laws and to see the resulting performance [3]. On the other hand, distillation is the common method for separation of final products in petroleum industries.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Distribution Control System in Oil Refinery by Applying Hybrid Machine Learning Techniques

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Optimization of Distribution Control System in Oil Refinery by Applying Hybrid Machine Learning Techniques

et al. 2022

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“…[6]. Some work related to adaptive controllers includes design of an adaptive predictive control strategy for an atmospheric distillation process of crude oil, where the methodology proposed to the coupled system reduces the complexity of the control structures [7]. Another is optimized adaptive control of an ideal reactive distillation column which demonstrated that the scheme is able to satisfactorily track the optimum operating point of the system [8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive decoupling control for binary distillation column into a 3D virtual environment

2021

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The oil refinery plants have a particularly high energy consumption in the distillation process. Several authors state that these are the systems with the highest energy consumption in the petrochemical industry. Currently, work is being done on the implementation of adaptive controllers in order to improve the efficiency of the distillation process and the quality of the product. In this work the control of a binary distillation column developed within a 3D virtual laboratory is presented. Adaptive control by reference model (MRCA) is used for the manipulation of bottom and distillate concentrations. Initially, the mathematical description of the process is obtained, together with the computer-aided design, to subsequently implement the control technique. As a result, the behavior of the controlled system in simulation is shown and compared with the response of the embedded system within the virtual environment. The main contribution of this work is the interaction that a user can have with this type of processes and the possibility of implementing modern control strategies within this type of environments as a low-cost tool to support education and training activities.

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“…Following control systems for rectification columns can be outlined: robust [2], adaptive [3,4], supervised [5], optimal [6,7], control systems involving neural networks [8], fuzzy logic [9], non-linear prediction process model [10,11]. The quality and effectiveness of such systems can be improved with the use of distributed [12] or mobile [13,14] control actions.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Rectification Process Using Mobile Control Action With Account for Criterion of Maximizing Separation Quality

Sheikus¹,

Кovalenko²,

Kotok³

et al. 2020

Eureka: PE

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The use of mobile control action allows the improvement of technical-economical characteristics of the rectification process and allows for operation regimes that can’t be achieved with traditional control approaches. Mobility lies in the ability to choose the movement law of compound source and energy in the spatial region of apparatus. Mobile control over the rectification process can be realized by changing the column feed point. An optimal number of feed trays must be determined with consideration of cost and output performance, and also the quality of the target product. The work aimed to develop a method for calculating optimal control action, including mobile ones, on the rectification process with additional account for the criterion of maximizing quality of target product, and also, comparison of static column profiles that are optimal by different criteria. Mathematical modeling of the rectification column for separation of water-methanol mixture revealed that increasing quality requirements to target products decreases the number of the optimal feed tray. A method was described for process optimization by the normalized criterion that accounts for separation quality and power consumption. The method was used to determine optimal values of traditional (flows of heat into the column's cube and phlegm) and mobile (feed tray number) control actions that provide the best technical-economical parameters of the rectification column. A proof is presented for the existence and uniqueness of solutions for this optimization problem and the effectiveness of using mobile actions for different requirements to target. The optimal temperature profile of the culms was studied and their characteristic features that correspond to different specific and normalized optimization criteria were found

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Design of an adaptive predictive control strategy for crude oil atmospheric distillation process

Cited by 14 publications

References 10 publications

Optimization of Distribution Control System in Oil Refinery by Applying Hybrid Machine Learning Techniques

Optimization of Distribution Control System in Oil Refinery by Applying Hybrid Machine Learning Techniques

Adaptive decoupling control for binary distillation column into a 3D virtual environment

Optimization of Rectification Process Using Mobile Control Action With Account for Criterion of Maximizing Separation Quality

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