Intelligent control system for extractive distillation columns

Neves, Thiago; Ramos, Wagner Brandão; Neto, Gilvan Wanderley de Farias; Brito, Romildo Pereira

doi:10.1007/s11814-017-0346-0

Cited by 13 publications

(6 citation statements)

References 24 publications

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“…However, neural network control can efficiently adjust the operating parameters of the device, significantly reduce personnel operational errors, and ensure the long-term stability of the device. Neves et al developed and successfully implemented an intelligent control system for ED columns that produce anhydrous ethanol using glycol as the solvent . Artificial neural networks (ANN) have proven to be a quick and effective method for predicting new set points in response to interference.…”

Section: Dynamic Controlmentioning

confidence: 99%

“…Neves et al developed and successfully implemented an intelligent control system for ED columns that produce anhydrous ethanol using glycol as the solvent. 142 Artificial neural networks (ANN) have proven to be a quick and effective method for predicting new set points in response to interference. This control system gathers data from measurements of the azeotrope feed temperature, flow rate, and composition.…”

Section: ■ Dynamic Controlmentioning

confidence: 99%

“…Although the control analysis of feed disturbance is only carried out in this paper, considering the safety margin in the actual production process of the factory, the "single-end control" which combines the constant reflux and feed ratio greatly reduces the operating cost and control complexity, and provides a new idea for the control design of relevant ED. Goncalves das Neves et al 119 developed and implemented an intelligent control system for an ED column for the production of anhydrous ethanol with ethylene glycol as the solvent. The concept of artificial neural networks was applied to predict new set points after perturbations and proved to be a fast and feasible solution.…”

Section: ■ Dynamic Controlmentioning

confidence: 99%

See 2 more Smart Citations

Design and Multiple Performance Evaluation of Green Sustainable Process for Azeotropes Separation via Extractive Distillation

Zhong,

Cheng,

Dai

et al. 2023

ACS Sustainable Chem. Eng.

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Multicomponent liquid mixtures, particularly azeotropes, are extensively utilized in the chemical, petroleum, pharmaceutical, and other processing industries. Energy-saving and efficient separation of azeotropes holds significant value for the design and development of sustainable industrial processes. Extractive distillation (ED) has consistently played a pivotal role in azeotrope separation. We offer a broad and comprehensive review that encompasses recent progress in multiple aspects of ED, including the screening of entrainers, process design, enhancement schemes, dynamic control strategies, and environmental assessment. Initially, we delved into the application of quantum chemistry calculations and molecular dynamics simulation for screening suitable entrainers. We then elucidate the design principles and forms of two distinct types of ED processes. Subsequently, we explore the intensification mechanisms of the ED process through coupling mechanisms, such as heat integration and the combination of ED with other distillation processes. We also summarize the recent improvements in dynamic control strategies for various ED processes. Lastly, we evaluate various aspects of ED from the perspectives of environmental impact, economic viability, and exergy analysis. This study investigates the challenges, prospects, and emerging trends of ED technology for the separation of multicomponent azeotropes.

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Section: Dynamic Controlmentioning

confidence: 99%

Section: ■ Dynamic Controlmentioning

confidence: 99%

Section: ■ Dynamic Controlmentioning

confidence: 99%

See 1 more Smart Citation

Design and Multiple Performance Evaluation of Green Sustainable Process for Azeotropes Separation via Extractive Distillation

Zhong,

Cheng,

Dai

et al. 2023

ACS Sustainable Chem. Eng.

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“…The goal is to control the top and bottom compositions in distillation columns simultaneously, which is a major challenge in the control literature due to the highly interacted MIMO processes with time delays. In recent decades many studies have discussed the challenges of controlling a distillation column and have proposed different approaches in order to address these challenges [1–3]. From a system identification perspective, the distillation column has nonlinear time‐varying process dynamics and this is the reason why many researchers have devoted their research efforts to this subject [4, 5].…”

Section: Introductionmentioning

confidence: 99%

Event‐based neural network predictive controller application for a distillation column

Hadian

Mehrshadian

Karami

et al. 2019

Asian Journal of Control

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In this work, the case study is a distillation column, which is a multi‐input multi‐output (MIMO) nonlinear process. An event‐based neural network predictive controller is utilized for the case study, considering control and energy policies. Computation and communication reduction are the main purposes of the event‐based strategy. The event‐based model predictive controller also copes successfully with the multi‐input multi‐output (MIMO) time‐delayed nonlinear processes. In order to achieve a suitable nonlinear data‐based model of the process, an event‐based neural network predictive controller is proposed. Moreover, new Cuckoo Optimization Algorithm (COA) is employed to improve the efficiency of the neural network. Evaluation of the proposed controller has illustrated a satisfactory performance in both setpoint tracking and disturbance rejection while computational load has been significantly decreased.

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“…16 Singh et al 8 developed a model that predicted the material composition of the distillation process by employing ANNs. 8 Das Neves et al 17 developed an intelligent control system that exploited an ANN to predict new set points after a disturbance in the distillation process of ethanol production, by utilizing ethylene glycol as a solvent. The intelligent control system could predict new operating conditions for the raw material change in the distillation process and showed superior performance compared to systems in which ANNs were not applied.…”

mentioning

confidence: 99%

Development and application of machine learning‐based prediction model for distillation column

Kwon

Choi

et al. 2021

Int J Intell Syst

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Distillation is an energy‐consuming process in the chemical industry. Optimizing operating conditions can reduce the amount of energy consumed and improve the efficiency of chemical processes. Herein, we developed a machine learning‐based prediction model for a distillation process and applied the developed model to process optimization. The energy consumed in the distillation process is mainly used to control the temperature of the distillation column. We developed a model that predicted temperature according to the following procedure: (1) data collection; (2) characteristic extraction from the collected data to reduce learning time; (3) min–max normalization to improve prediction performance; and (4) a case study conducted to select the artificial neural network algorithm, optimization method, and batch size, which are the most appropriate elements for predicting production stage temperature. The result of the case study revealed that the most appropriate model was observed with a root mean squared error of 0.0791 and a coefficient of determination of 0.924 when the long short‐term memory algorithm, Adam optimization method, and batch size of 128 were applied. We calculated the amount of steam consumption required to consistently maintain the production stage temperature by utilizing the developed model. The calculation result indicated that the amount of steam consumption was expected to be reduced by approximately 14%, from an average flow rate of 2763–2374 kg/h. This study proposed a control method applying a machine learning‐based prediction model in the distillation process and confirmed that operation energy could be reduced through efficient operation.

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Intelligent control system for extractive distillation columns

Cited by 13 publications

References 24 publications

Design and Multiple Performance Evaluation of Green Sustainable Process for Azeotropes Separation via Extractive Distillation

Design and Multiple Performance Evaluation of Green Sustainable Process for Azeotropes Separation via Extractive Distillation

Event‐based neural network predictive controller application for a distillation column

Development and application of machine learning‐based prediction model for distillation column

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