A feasible path-based approach for Dividing Wall Column design procedure

Pretoro, Alessandro Di; Ciranna, Flavia; Fedeli, Matteo; Joulia, Xavier; Montastruc, Ludovic; Manenti, Flavio

doi:10.1016/j.compchemeng.2021.107309

Cited by 10 publications

(3 citation statements)

References 51 publications

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“…This case study given by Bezzina and et al, [12] ,P.FLOQUET and et al, [2] and S.aly [1] , The problem specifications are given in Table (8) with feed stream is 2000 kmol/hr. In the first step the main three variable parameters have been estimated then the normalized boiling point difference, normalized estimated mass load and normalized relative volatility for (ABCDEFGH) have been calculated to estimate the separation weight for each possible split in Table (9). The possible split of components (ABCDEF) and the normalization of the main three variables are used as input for fuzzy analogical gate to get the separation weight of each split as shown in Table (10) and AB/CDEF is the best choice because it has the largest separation weight value (0.278) where AB will separate in the top and CDEF will separate in the bottom.…”

Section: Case Study2mentioning

confidence: 99%

“…After that Alessandro Di Pretoro and et al, [9] presented a new design technique based on feasible pathways and raised or eliminated from the correct column section selected the number of trays with the help of composition profiles analysis, starting from a convergent design based on shortcut approaches. And that generated an optimized solution that is very close to the best in a short amount of time, without the requirement to solve the MINLP issue.…”

Section: Introductionmentioning

confidence: 99%

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Systematic Approach For Sharp Separation Network

ahmed,

Aly,

Awad

2023

Journal of Petroleum and Mining Engineering

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Distillation is one of the main separating methods which is used frequently in the industrial field. For separating a multi-component feed mixture using distillation process, the sequence of distillation columns is so important, but unfortunately it is really hard to find the best sequence due to the large number of possibilities. This work developed a multiobject approach for the synthesis of the sharp separation network which is used to separate a large number of components in a mixture that was difficult to separate because it was difficult to select the appropriate separation sequence from the extensive number of potential splits. The use of three expert rules to qualify the estimated mass load, the difference in boiling point, and the relative volatility forms the basis of the synthesis algorithm. There are two fuzzy analogical gates in use (symmetric and asymmetric). The normalized predicted mass load and the normalized relative volatility are the inputs for the symmetric gate (AND gate). The output of the AND gate and the normalization of the boiling point difference are the inputs for the asymmetric gate (INVOKE gate). for separation issues with many components, the size is huge and the amount of decision variables is enormous. This approach not only can be used to find the optimum separation sequence but also can be used with a huge number of components which was so difficult to find their best separation sequence. Also, it has been noticed that the developed approach gave a lower total annual cost compared with any other methods. So, the suggested method can be carried out manually and is easy to use for a large number of components. Several illustrative examples using existing fuzzy analogical gate are provided as examples of the suggested method's success.

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Section: Case Study2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Systematic Approach For Sharp Separation Network

ahmed,

Aly,

Awad

2023

Journal of Petroleum and Mining Engineering

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“…In addition, Acosta-Solano and Huerta-Guevara and Wang et al carried out sliding mode control (SMC) for controlling a DWC, which does not need an accurate mathematical model of the plant. As far as we know, modeling of a DWC is not an easy task owing to its inner complex structures. , Second, for DDC approaches, a PID controller, considered as the earliest DDC technique, could be found in much literature on its application in DWCs; ,− meanwhile, several attempts had been made to tune the parameters in PID. , Nevertheless, PID still has gaps in dealing with multivariable control, strong nonlinearity, and highly coupled systems. Tututi-Avila and Jiménez-Gutiérrez improved the fuzzy logic controller (FLC) by optimizing its gains and used it for the single-input single-output (SISO) composition control of a pentane–hexane–heptane DWC.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Multivariable Control of a Dividing Wall Column Using a Different-Factor Full-Form Model-Free Adaptive Controller

Chen

2022

Ind. Eng. Chem. Res.

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A dividing wall column (DWC), characterized by multivariable control, strong nonlinearity, and highly coupled systems, shows effective distillation capacity with a significant reduction in energy consumption and capital cost. Although multivariable control strategies for DWCs have attracted certain attention from both academia and industry, relatively little work has focused on data-driven multivariable controllers for such a complex system that is not easy to model. In this work, a novel different-factor full-form model-free adaptive controller (DF-FFMFAC) is first proposed for DWCs aiming to solve the problem of simultaneous control of the liquid level, column pressure, and temperature channels with quite different characteristics between them, which may be a challenging task for the prototype FFMFAC. Taking such complex dynamics into account, a parameter selection technique for the DF-FFMFAC based on neural networks is also developed, where gradient descent for the neural network is improved by the full-form dynamic linearization technique utilized in the DF-FFMFAC. Furthermore, the stability of the parameter tuning process is guaranteed by Lyapunov theory. The present work makes a noteworthy contribution to the multivariable control of DWCs in a purely online data-driven way without any offline training procedure and mathematical information. In terms of the separation of an ethanol–n-propanol–n-butanol DWC, the controller is cosimulated in MATLAB/SIMULINK and Aspen Plus Dynamics and tested against a series of feed flow rate and feed composition disturbances. As a result, the proposed method achieves encouraging control performance with smaller oscillations and faster responses compared with model predictive control and proportional–integral–derivative controllers, proving to be a promising data-driven method for the multivariable control of DWCs. Finally, the efficacy of the proposed scheme for the practical control of DWCs in the presence of measurement noise has also been demonstrated by adding white noise to the simulation.

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