PSO-PD fuzzy control of distillation column

Al-Dunainawi, Yousif; Abbod, Maysam F.

doi:10.1109/intellisys.2015.7361194

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Regarding the optimization problems, in [8] the au- and a distillation column. Related to control issues, after the RTO analysis, the authors successfully updated the control systems of both processes using AI approaches; always considering the control applications, in [13] an AI based control-logic system was implemented to regulate product compositions of distillation.…”

Section: Related Workmentioning

confidence: 99%

Prediction Of Chemical Plants Operating Performances: A Machine Learning Approach

Campanile¹,

Bonito²,

Iacono³

et al. 2023

ECMS 2023 Proceedings Edited by Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni

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Modern environmental regulations require rigorous optimization of operations in process engineering to reduce waste, pollution, and risks while maximizing efficiency. However, the nature of chemical plants, which include components with non-linear behavior, challenges the use of consolidated tuning and control techniques. Instead, ad-hoc, self-adapting, and time-variant controls, with a balanced tuning of parameters at both the subsystem and system level, may be necessary. Needed computing processes may require significant resources and high performance systems, if managed by means of traditional approaches and with exact solution methods. In this regard, domain experts suggest instead the use of integrated techniques based on Artificial Intelligence (AI), which include Explainable AI (XAI) and Trustworthy AI (TAI), which are unique in this industry and still in the early stages of development. To pave the way for a real-time, cost-effective solution for this problem, this paper proposes an AI-based approach to model the performance of a real chemical plant, i.e. a marine scrubber installed on a Ro-Ro ship. The study aims to investigate Machine Learning (ML) techniques which can be used to model such processes. Notably, this analysis is the first of its kind, at the best of the authors’ knowledge. Overall, the study highlights the potential of using ML-based techniques, to optimize environmental compliance in the shipping industry.

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Section: Related Workmentioning

confidence: 99%

Prediction Of Chemical Plants Operating Performances: A Machine Learning Approach

Campanile¹,

Bonito²,

Iacono³

et al. 2023

ECMS 2023 Proceedings Edited by Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni

View full text Add to dashboard Cite

show abstract

“…They are rectification section and stripping section. Trays under the feed tray are called the section of stripping and trays above feed tray are called rectification section [10], Figure 1 Leaving the Condenser liquid is entered into the drum of the reflex. A part of this liquid column deals as a reflex and fall down by the trays.…”

Section: Process Descriptionmentioning

confidence: 99%

Interval type-2 fuzzy logic controller design for distillation column process

Hassan¹,

Ezzaten²

2019

IJRA

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Distillation columns are the most units used in oil reﬁneries, and chemical factories. This is a very difficult process and non-linear. Therefore, <br /> the development of intelligent control systems for the columns of <br /> the distillation is very difficult. In this paper, an intelligent control strategy using Mamdani type Interval Type-2 PI Like Fuzzy Logic Controller (IT2FLC) is used. The controller consists of PD-Like FLC with integrated output. Kernek Mendel (KM) algorithm is used as the type reduction method for the IT2FLC. This controller is applied to control a continuous binary trays distillation column. The controller has three tunable gains to reach minimum overshoot, minimum error and minimum settling time at least variables can be controlled. The controller is a variable of the molar fraction of distillate and the reflex ratio is the manipulated variable. Integral Time Absolute Error (ITAE) is employed as an objective function to measure the improvement in time response where the error is between desired and output product composition. The performance of IT2FLC is compared with Type-1 PI Like FLC (T1FLC). The results of the simulations have shown that the project of IT2FLC works efficiently to no- disturbance and the effects of disturbance. Improve average is of 85% for a constant set-point without a disturbance and 80% with a disturbance. Furthermore, the average improvement for a step set-point is 53% without disturbance and 74% with disturbance. All results of the simulation confirmed the hardiness and control any consistent inaccurate with obvious advantages for the IT2FLC.

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PI-Like Interval Type-2 Fuzzy Logic Control Based Social Spider Optimization for distillation column

Hassan

Ezzaten

2018

2018 Third Scientific Conference of Electrical Engineering (SCEE)

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PSO-PD fuzzy control of distillation column

Cited by 4 publications

References 14 publications

Prediction Of Chemical Plants Operating Performances: A Machine Learning Approach

Prediction Of Chemical Plants Operating Performances: A Machine Learning Approach

Interval type-2 fuzzy logic controller design for distillation column process

PI-Like Interval Type-2 Fuzzy Logic Control Based Social Spider Optimization for distillation column

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