Nonlinear process model-based self-optimizing control of complex crude distillation column

Torgashov, A. Yu.

doi:10.1016/s1570-7946(01)80126-7

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Liau et al [13] picked up the challenges by using neural network for the expert system design of a crude oil distillation unit for process optimization. Torgashov [14] designed self-optimizing control of complex crude distillation column control using non-linear process model-based method. Khairiyah et al [15] used the method proposed by Bawazir et al [12] for design of crude oil distillation column for real time optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework

Popoola¹,

Gutti²,

Susu³

2013

ACES

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This paper presents a comprehensive review of various traditional systems of crude oil distillation column design, modeling, simulation, optimization and control methods. Artificial neural network (ANN), fuzzy logic (FL) and genetic algorithm (GA) framework were chosen as the best methodologies for design, optimization and control of crude oil distillation column. It was discovered that many past researchers used rigorous simulations which led to convergence problems that were time consuming. The use of dynamic mathematical models was also challenging as these models were also time dependent. The proposed methodologies use back-propagation algorithm to replace the convergence problem using error minimal method.

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Section: Literature Reviewmentioning

confidence: 99%

A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework

Popoola¹,

Gutti²,

Susu³

2013

ACES

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“…Several studies have used ANN in the design of CDU [3,10,11], however, ANN use has not been very popular in the optimization of operating variables of CDU in the existing refineries. It is this void in the literature that the present study hopes to fill by studying the use of ANN in the optimization of CDU of an existing refinery.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network (ANN) in the Optimization of Crude Oil Refinery Process: New Port-Harcourt Refinery

Braimah

2020

JENRR

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Background: Optimizing the process conditions of the crude distillation unit is a main challenge for each refinery. Optimization increases profit by producing the required range of distillates at maximum yield and at minimum cost. To achieve an acceptable control of product quality an artificial neural network (ANN) can be used. ANNs are used for engineering purposes, such as pattern recognition, forecasting, and data compression. In the petroleum refinery industry, ANN has been used as controller in for the crude distillation unit. The aim of the current study was to use ANN to optimize and achieve control of product quality of crude distillation unit of an oil refinery. Materials: The research was carried out using the following materials; The design flowchart and the operating data of the crude distillation unit of the New Port Harcourt refinery, Simulation software (HYSYS 2006.5) and Matlab for the ANN. Results: The ANN predicted the optimum operating conditions at which the atmospheric distillation unit (ADU) can operate with the least irreversibility and without changing the design and compromising the products quality. The corresponding exergy efficiency after optimization with ANN for the input variable combinations was 70.6% which was a great improvement because the exergy efficiency increased as compared to the base case of 51.9%. Conclusion: Optimization using ANN, improved the efficiency of the ADU with the least irreversibility and without changing the design and compromising the products quality.

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“…Adjusting the operation parameters, which requires no changes in the structure and route of the distillation column, is utilized daily in practical production, including decreasing the column pressure, reducing the reflux ratio, increasing the feed temperature, changing the feed‐tray location, decreasing the overhead vaporization rate, adjusting the parameters of the pump‐around circuits and their heat duty distribution, etc. 2–4. However, restricted by various conditions, this leads to only limited energy savings.…”

Section: Introductionmentioning

confidence: 99%

A New Stripping Column Process and Optimization of Its Operation

Gao

Shi

2016

Chem Eng & Technol

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In view of a kind of stripping column used widely in chemical industry, being in operation of total cold reflux and without top light product, a new stripping process is suggested in this paper. It takes dual feeds of the hot plus the cold by replacing partial cold reflux with a part of cold feed, resulting in significant reduction of energy consumption and simplification of the overhead vapor route. Due to no any change on the column's body and only a pipe needed, this new process can be promoted conveniently in retrofitted projects. Further an optimization study is developed for choosing proper flow rate of cold feed and column's running pressure. An application in a stripper of pre-hydrotreating resultant of oil catalytic reformer shows that the new process is valid. Keywords: Catalytic reformer; Energy consumption; Process optimization; Simulation; Stripper IntroductionDistillation is one of the most common operation units in chemistry industry, and also is an energy intensive process. It assumes about 40% of the total energy used in chemical and petroleum refining industries [1], and hence stimulating the development of energy-efficient separation technology greatly. On distillation column, there are four categories of main energy-saving measures: adjusting operation parameters, upgrading internals, creating new column structure and rearranging route of feed or/and draw-offs.Adjusting operation parameters, due to no need of changing structure and route of distillation column, is daily utilized in practical production, including decreasing column pressure, reducing reflux ratio, increasing feed temperature, changing feed-tray location, decreasing over vaporization rate, adjusting parameters of pump-around circuits and their heat duties distribution, and so on [2,21,25]. However restricted by various conditions, it is only available to benefit from a limited energy-saving.Column internals, such as tray, packing, liquid dispatcher, drawn canal of side products, have played a very important role in determining level of energy consumption of distillation column. Usually highefficient tray requires lower reflux ratio, namely less consumptions of hot and cold utilities; large flux packing means lower pressure drop, namely higher feed vaporization rate when the column pressure and feed temperature keep unchanged, naturally less heat requirement from the bottom reboiler

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Nonlinear process model-based self-optimizing control of complex crude distillation column

Cited by 3 publications

References 5 publications

A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework

A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework

Application of Artificial Neural Network (ANN) in the Optimization of Crude Oil Refinery Process: New Port-Harcourt Refinery

A New Stripping Column Process and Optimization of Its Operation

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