Simultaneous optimization of heat-integrated crude oil distillation systems

Luo, Yan; Wang, Liwen; Wang, He; Yuan, Xin

doi:10.1016/j.cjche.2015.03.008

Cited by 13 publications

(10 citation statements)

References 14 publications

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“…The most important thing considered in the economic objective function is how to improve the value of the product and reduce the energy consumption simultaneously. The formulation of the economic objective function is given by Luo et al:

φ true(k true) = true{normalP normalr normalo normald normalu normalc normalt normalo normalu normalt normalp normalu normalt normalv normala normall normalu normale normal- normalcos normalt normalo normalf normalf normale normale normald normal- normale normaln normale normalr normalg normaly normalcos normalt normaltrue}

which can be written as:

φ true(k true) = true{{true \sum}^{} (F_{i} \times P_{i}) - F_{c} \times P_{c} - {true \sum}^{} (E_{j} \times P_{j}) true}

where

φ

is the economical function at time

true[k true]

F_{i}

is the product flow rate of product

i

at time

true[k true]

P_{i}

is the product unit price,

F_{c}

is the feed flow rate at time

true[k true]

and

P_{c}

is the unit price of the product,

E_{j}

is the energy consumption including furnace heat duty, cooling duty, and stripping steam,

P_{j}

is the price per energy unit.

i

represents the Light naphtha, Heavy naphtha, Kerosene, Diesel, AGO, and Residue, while

j

represents the he...…”

Section: Nonlinear Model Predictive Controlmentioning

confidence: 99%

An improved economic‐based nonlinear model predictive control strategy for the crude oil distillation process

et al. 2018

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Improving performance and increasing process profitability represent a priority for the long‐term operation of the crude oil distillation process. To increase process profitability and maintain control performance, in this paper, an improved economic‐based nonlinear model predictive control (NMPC) strategy is used to optimize the crude oil distillation (CDU) process. Considering the strong coupling and nonlinear dynamics of the CDU process, we make some modifications for the existing NMPC strategy. To reduce the complexity of the nonlinear prediction model, a support vector regression (SVR) based model is employed to describe the dynamical behaviour of the CDU process. Considering the coupling of the CDU process, we propose the concept of the coupling weighting factor to modify the objective function of the NMPC optimization problem. The simulation results demonstrate the effectiveness of the proposed improved NMPC algorithm for the CDU process.

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φ true(k true) = true{normalP normalr normalo normald normalu normalc normalt normalo normalu normalt normalp normalu normalt normalv normala normall normalu normale normal- normalcos normalt normalo normalf normalf normale normale normald normal- normale normaln normale normalr normalg normaly normalcos normalt normaltrue}

which can be written as:

φ true(k true) = true{{true \sum}^{} (F_{i} \times P_{i}) - F_{c} \times P_{c} - {true \sum}^{} (E_{j} \times P_{j}) true}

where

φ

is the economical function at time

true[k true]

F_{i}

is the product flow rate of product

i

at time

true[k true]

P_{i}

is the product unit price,

F_{c}

is the feed flow rate at time

true[k true]

and

P_{c}

is the unit price of the product,

E_{j}

is the energy consumption including furnace heat duty, cooling duty, and stripping steam,

P_{j}

is the price per energy unit.

i

represents the Light naphtha, Heavy naphtha, Kerosene, Diesel, AGO, and Residue, while

j

represents the he...…”

Section: Nonlinear Model Predictive Controlmentioning

confidence: 99%

An improved economic‐based nonlinear model predictive control strategy for the crude oil distillation process

et al. 2018

View full text Add to dashboard Cite

show abstract

“…In general, model-based operational optimization techniques have been widely used. [8][9][10][11][12][13][14][15][16] Luo et al [14] optimized the crude oil distillation process and proposed a system optimization method that maximizes the annual economic benefit of the crude oil distillation system. In this method, the product output value and energy consumption were considered simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Multi‐stage intelligent operation optimization for a hydrocracking fractionation system with a multi‐fractionator series‐parallel structure

Wang

Sun

et al. 2020

Can J Chem Eng

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A fractionation system is an essential unit in the hydrocracking process. Its optimal operation is challenging because of the complexity in the structure of the distillation tower and composition of the stream. In addition, the seriesparallel structure between the distillation towers of different techniques aggravates the coupling and complexity of the hydrocracking fractionation system (HFS). This, in turn, increases the time complexity of the optimization problem. In this paper, a rigorous mechanism model of an actual HFS is first applied to describe the operating conditions of the HFS. Then, an improved state transition algorithm (STA) with a staged evaluation strategy is proposed to solve the above problem. To overcome problems caused by the series-parallel structure of HFS, the model is divided into multiple stages for evaluation by mechanism analysis. Furthermore, several typical convergence estimation criteria are introduced to reduce unnecessary model calculations. To solve time-consuming problems associated with HFS optimization, the adaptive change operator is used to improve the search function of the original algorithm and two performance criteria are presented to reduce the optimization time. The proposed algorithm is successfully applied to the operational parameter optimization problem of HFS with a multi-fractionator series-parallel structure. The experimental results indicated that the staged evaluation strategy improved the fast convergence probability of the HFS mechanism model and reduced unnecessary calculations, whereas the improved algorithm increased accuracy and significantly decreased optimization time.

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“…In industrial processes [1], the nonsharp distillation sequences are widely used for obtaining nonsharp separated products. Compared to sharp separation sequences, nonsharp sequences are relatively understudied.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Distillation Sequences with Nonsharp Separation Columns

Wang

Zhang

et al. 2019

Processes

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Nonsharp distillation sequences are widely used in industrial separation processes; however, most current research has not discussed this topic, except in sequences with heat integration under special operating conditions, including complex columns. The sequence with nonsharp separation has the features of general distillation sequences, which are usually optimized by adjusting the separation sequence and the design/operation parameters of each column in the sequence, making the optimization a mixed integer nonlinear programming (MINLP) problem, which is usually hard to solve. With inclusion of nonsharp separation columns, the sequence optimization becomes even more complicated and computationally intensive. This work aimed to optimize the distillation sequence, including nonsharp distillation alongside simple columns and dividing wall columns. Inspired by the dynamic programing method for sharp distillation sequence, a framework for automatic optimization is proposed to decompose the MINLP problem into integer programming (IP) and nonlinear programming (NLP) problems. The optimization processes of sharp and nonsharp distillation sequences are compared and the solution space in terms of the possible number of distillation sequences with nonsharp separation is discussed. Two optimization cases, including an industrial one, are included to validate the proposed framework.

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Simultaneous optimization of heat-integrated crude oil distillation systems

Cited by 13 publications

References 14 publications

An improved economic‐based nonlinear model predictive control strategy for the crude oil distillation process

An improved economic‐based nonlinear model predictive control strategy for the crude oil distillation process

Multi‐stage intelligent operation optimization for a hydrocracking fractionation system with a multi‐fractionator series‐parallel structure

Optimization of Distillation Sequences with Nonsharp Separation Columns

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