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Simulation and optimization of a hydrotreating reactor using a new hybrid imperialistic competition algorithm-based adaptive neuro-fuzzy inference system (ICA-ANFIS)

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Abstract

A diesel hydrotreating (HDT) trickle-bed reactor (TBR) with co- and counter-current streams was simulated using heterogeneous models. The simulation results for the output sulfur concentration agree with the pilot data in both co- and counter-current flows. Also, the effects of major operational parameters were examined on the performance of the reactor. The results show the positive effect of counter-current streams direction, temperature, hydrogen pressure, and negative effect of hydrogen sulfide (H2S) pressure and liquid and gas velocities on the hydrodesulfurization (HDS) reaction. The results of the HDT reactor simulation were then modeled using the adaptive neuro-fuzzy inference system (ANFIS) method. According to the results, ANFIS is very powerful in predicting the simulation results. Finally, the reactor operating conditions were optimized to maximize sulfur removal from diesel using a new combining the imperialistic competition algorithm (ICA) and ANFIS, called ICA-ANFIS. The ANFIS was adopted to calculate the cost function in the ICA and reduced the run-time of the optimization program by more than 1000 times. In the optimum result, sulfur removal increased by 33% compared with the baseline. The main novelty of this study is modeling and optimizing the heterogeneous simulation results using the hybrid of ANFIS and ICA methods.

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Data Availability

The datasets generated during the current study are not publicly available as that could compromise the research participant privacy/consent but are available from the corresponding author on reasonable request.

Abbreviations

\(a_{L}\) :

Interfacial area of gas − liquid (cm−1)

\(a_{s}\) :

Interfacial area of liquid − solid (cm−1)

\(C_{i}\) :

Component i molar concentration (gmol.cm−3)

\(C_{i}^{L}\) :

Concentration of i in the liquid phase (gmol.cm−3)

\(C_{i}^{s}\) :

Concentrations of i in the surface of the catalyst (gmol.cm−3)

\(D_{i}^{L}\) :

Component i molecular diffusivity in liquid (cm2.s−1)

\(d_{P}\) :

Particle diameter (cm)

\(E_{a}\) :

Activation energy (J.gmol−1)

\(G_{L}\) :

Superficial mass velocity of liquid (g.cm−2.s−1)

\(H_{i}\) :

Henry’s law coefficient (MPa.cm3.mol−1)

\(k_{f}\) :

Rate constant of forward HDA (s−1.MPa−1)

\(K_{{H_{2} S}}\) :

H2S adsorption equilibrium constant (cm3.mol−1)

\(k_{i}^{G} a_{L}\) :

Gas − liquid mass-transfer coefficient(s−1)

\(k_{i}^{j}\) :

Component i mass-transfer coefficient at the interface j (cm.s−1)

\(k_{i}^{L} a_{s}\) :

Liquid − solid mass-transfer coefficient(s−1)

\(k_{j}\) :

Reaction rate constant for reaction j

\(k_{r}\) :

Reverse HDA rate constant (s−1)

\(P_{i}^{G}\) :

Gaseous i partial pressure (MPa)

\(R\) :

Universal gas constant (J.gmol−1.K−1)

\(r\) :

Catalyst radius (cm)

\(r_{j}\) :

Rate of reaction j

\(T\) :

Absolute temperature (K)

\(T_{MeABP}\) :

Average boiling point (R)

\(u_{G}\) :

Superficial gas velocity (cm.s−1)

\(u_{j}\) :

J phase superficial velocity (cm·s−1)

\(u_{L}\) :

Superficial liquid velocity (cm·s−1)

\(z\) :

Reactor length (cm)

A:

Aromatic compound

API:

American petroleum institute

NB :

Basic nitrogen

NNB :

Non-basic nitrogen

Np :

Naphthenes

S:

Sulfur

\(\rho_{B}\) :

Bulk density of the catalyst

\(\rho_{0}\) :

Density of liquid at standard conditions (lb·ft−3)

\(\rho_{20}\) :

Density of liquid at 20 °C (g·cm−3)

\(\mu_{L}\) :

Viscosity of liquid (mPa·s)

\({\upeta }_{{\text{j}}}\) :

Reaction rate based on catalyst surface concentrations

\(\lambda_{i}\) :

Component i solubility coefficient (Nl·kg−1·MPa−1)

\(\nu_{c}\) :

Gaseous compounds critical specific volume (cm3.mol−1)

\(\nu_{i}\) :

Solute i molar volume at normal boiling temperature (cm3·mol−1)

\(\nu_{L}\) :

Liquid solvent molar volume at normal boiling temperature (cm3·mol−1)

\(\nu_{N}\) :

Molar gas volume at the standard conditions (cm3·mol−1)

\(\nu_{c}^{m}\) :

Critical specific volume (ft3·lbm−1)

G:

Gas phase

J:

Reaction

L:

Liquid phase

0:

Reactor inlet condition

S:

Solid phase

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Hosein Eshghanmalek & Habib Ale Ebrahim

  2. Department of Chemical Engineering, Faculty of Engineering, Urmia University, Urmia, Iran

    Mohammad Javad Azarhoosh

Authors
  1. Hosein Eshghanmalek
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  2. Habib Ale Ebrahim
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  3. Mohammad Javad Azarhoosh
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Contributions

HE was involved in HDT reactor simulation, sensitivity analysis, writing (original draft). HAE contributed to supervision and writing (review and Editing). MJA was involved in supervision, modeling the simulation results with ANFIS, HDT reactor optimization using ICA-ANFIS, writing (review and Editing).

Corresponding author

Correspondence to Mohammad Javad Azarhoosh.

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Conflict of interests

The authors have no relevant financial or non-financial interests to disclose.

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Eshghanmalek, H., Ebrahim, H.A. & Azarhoosh, M.J. Simulation and optimization of a hydrotreating reactor using a new hybrid imperialistic competition algorithm-based adaptive neuro-fuzzy inference system (ICA-ANFIS). Chem. Pap. 76, 6247–6261 (2022). https://doi.org/10.1007/s11696-022-02310-0

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  • DOI: https://doi.org/10.1007/s11696-022-02310-0

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