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Ant lion based optimization for performance improvement of methanol production

  • Mohd Azahar Mohd Ariff ORCID logo EMAIL logo , Sharifah Rafidah Wan Alwi , Dinie Muhammad , Muhamad Nazri Murat , Ashraf Azmi , Zulkifli Abdul Rashid und Fakhrony Sholahudin Rohman EMAIL logo
Veröffentlicht/Copyright: 13. November 2024
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Chemical Product and Process Modeling
Aus der Zeitschrift Chemical Product and Process Modeling Band 19 Heft 5

Abstract

Methanol (CH3OH) is a versatile compound used in various industries. Catalytic reactors used in CH3OH production are expensive due to high energy and raw material costs. Multi-objective optimization (MOO) is used to optimize CH3OH production, but it is still lacking. Researchers use alternative strategies or modify existing ones to achieve better results. This study applied model-based optimization using an ASPEN Plus simulator and Multi-objective Ant Lion Optimization (MOALO) to address the issue. The results revealed the highest conversion and product rate, with the lowest energy cost, side product, and bare module cost, CBM. The decision variable plots indicate that the reactor’s pressure significantly affects the optimal solution. This study provides valuable insight into optimizing CH3OH production.

Keywords: methanol; multi-objective optimisation; pareto front; ant lion optimizer; fixed bed reactor
Corresponding author: Mohd Azahar Mohd Ariff, Chemical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus,13500, Permatang Pauh, Pulau Pinang, Malaysia, E-mail: ; and Fakhrony Sholahudin Rohman, Process Systems Engineering Centre (UTM-PROSPECT), Research Institute of Sustainable Environment (RISE), Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia, E-mail:

Funding source: Universiti Teknologi MARA

Acknowledgement

The authors would like to sincerely thank Universiti Teknologi MARA, Cawangan Pulau Pinang, Malaysia, for their generous financial support.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Mohd Azahar Mohd Ariff contributes on conceptualization, methodology, data curation, formal analysis, writing-original draft, writing-review & editing; Sharifah Rafidah Wan Alwi contributes on writing-review & editing; Dinie Muhammad contributes on formal analysis, writing-review & editing; Zulkifli Abdul Rashid contributes on writing-review & editing, funding acquisition; Muhamad Nazri Murat contributes on formal analysis, writing-review & editing; Ashraf Azmi contributes on conceptualization, methodology, data curation; Fakhrony Sholahudin Rohman contributes on conceptualization, methodology, formal analysis, writing-original draft, writing-review & editing.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors declare no competing interests.

  6. Research funding: The authors would like to sincerely thank Universiti Teknologi MARA, Cawangan Pulau Pinang, Malaysia, for their generous financial support.

  7. Data availability: Datasets used and/or analysed in this study are available upon reasonable request.

Symbols

CBM [Million RM]

Bare module cost

CostE [Million RM/year]

Energy cost

Costcompress [Million RM/year]

Compression cost

d [m]

Diameter

FCH3OH [kmol/h)

Mole flowrate of CH3OH

FH2 [kmol/h]

Mole flowrate of H2

FH2O [kmol/h]

Mole flowrate of H2O

k [s−1]

The pre-exponential factor

l [m]

Length

pbesti [−]

Best acquired position

P [bar]

The pressure

P1 [-]

Problem 1

P2 [-]

Problem 2

P3 [-]

Problem 3

P4 [-]

Problem 4

P5 [-]

Problem 5

r

Rate of reaction

R [ J⋅K−1⋅mol−1]

The gas constant

T [K]

Temperature

Xm [%]

Conversion

XCO2 [%]

Conversion of CO2

Abbreviations

ALO

Ant lion optimization

CH3OH

Methanol

CO

Carbon monoxide

CO2

Carbon dioxide

CuO

Copper oxide

H2

Hydrogen

H2O

Water

LHHW

Langmuir-Hinshelwood-Hougen-Watson

m

Objective function

MOALO

Multi-objective ant lion optimization

MOO

Multi-objective optimization

n

Variable

ND

Non dominated

p

Inequality constraint

PF

Pareto front

q

Equality constraint

RM

Ringgit Malaysia

XCO2

Conversion

ZnO

Zinc oxide

Appendix

Detail algorithm of MOALO

The original random walk utilized in the ALO algorithm to simulate the random walk of ants is as follows:

(13) X ( t ) = [ 0 , c u m s u m ( 2 r ( t 1 ) − 1 ) , c u m s u m ( 2 r ( t 2 ) − 1 ) , … c u m s u m ( 2 r ( t n ) − 1 ) ]

Where cumsum calculates the cumulative sum, n is the maximum number of iterations, t shows the step of random walk.

where

r ( t ) = { 1   i f   r a n d ≥ 0.5 0   i f   r a n d ≤ 0.5 }

r(t) a stochastic function where t shows the step of random walk (iteration in this study) and rand is a random number generated with uniform distribution.

In order to keep the random walk in the boundaries of the search space and prevent the ants from overshooting, the random walks should be normalized using the following equation:

(14) X i t = ( X i t − a i ) × ( d i t − c i t ) ( b i − a i ) + c i t

where c i t is the minimum of i-th variable at t-th iteration, d i t indicates the maximum of i-th variable at t-th iteration, ai is the minimum of random walk of i-th variable, and bi is the maximum of random walk in i-th variable.

ALO simulates the entrapment of ants in antlions pits by changing the random walks around antlions. The following equations have been proposed in this regard:

(15) c i t = A n t l i o n j t + c t

(16) d i t = A n t l i o n j t + d t

A n t l i o n j   t shows the position of the selected j-th antlion at t-th iteration.

For mimicking the sliding ants towards antlions, the boundaries of random walks should be decreased adaptively as follows:

(17) c t = c t l

(18) d t = d t l

I is a ratio.

The second to last step in ALO is catching the ant and reconstructing the pit. The following equation simulates this:

(19) A n t l i o n j t = A n t i t   i f   f   ( A n t i t ) < f ( A n t l i o n j t )

A n t i t indicates the position of i-th ant at t-th iteration.

The last operator in ALO is elitism, in which the fittest antlion formed during optimization is stored. This means that the random walks on antlions gravitates toward a selected antlion (chosen using the roulette wheel) and the elite antlion. The equation to consider both of them is as follows.

(20) A n t i t = R A t + R E t 2

R A t is is the random walk around the antlion selected by the roulette wheel at t-th iteration, R E t is the random walk around the elite at t-th iteration.

To improve the distribution of the solutions in the archive, we considered two mechanisms. Firstly, the antlions are selected from the solutions with the least populated neighbourhood. The following equation is used in this regard that defines the probability of choosing a solution in the archive.

(21) P i = c N i

where c is a constant and should be greater than 1 and N i is the number of solutions in the vicinity of the i-th solution.

Secondly, when the archive is full, the solutions with most populated neighbourhood are removed from the archive to accommodate new solutions. The following equation is used in this regard that defines the probability of removing a solution from the archive:

(22) P i = N i c

The pseudo code of MOALO is shown below:

While the end condition is not met

For every ant

Select a random antlion from the archive

Select the elite using Roulette wheel from the archive

Update c and d using equations Eqs. (A.5) and (A.6)

Create a random walk and normalize it using Eq. (A.1) and Eq. (A.2)

Update the position of ant using (A.8)

End for

Calculate the objective values of all ants

Update the archive

If the archive is full

Delete some solutions using Roulette wheel and Eq. (A.10) from the archive

To accommodate new solutions

End

End while

Return archive

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Received: 2024-04-23
Accepted: 2024-08-31
Published Online: 2024-11-13

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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  1. Frontmatter
  2. Research Articles
  3. Energy cost prediction for chromium removal by nanofiltration membrane
  4. Forecasting gasification sustainability through enhanced K-nearest neighbour models for hydrogen and nitrogen amount
  5. Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability
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  7. Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production
  8. Dynamic optimization of boiler for minimizing energy consumption in the intentionally transient process operation: effect of different interval number
  9. Heat transfer efficiency in gas–solid fluidized beds with flat and corrugated walls
  10. Ant lion based optimization for performance improvement of methanol production
  11. Boundary Element Method for Viscous Flow through Out-phase Slip-patterned Microchannel under the Influence of Inclined Magnetic Field
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https://doi.org/10.1515/cppm-2024-0030
Schlagwörter für diesen Artikel
methanol; multi-objective optimisation; pareto front; ant lion optimizer; fixed bed reactor

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Energy cost prediction for chromium removal by nanofiltration membrane
  4. Forecasting gasification sustainability through enhanced K-nearest neighbour models for hydrogen and nitrogen amount
  5. Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability
  6. Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization
  7. Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production
  8. Dynamic optimization of boiler for minimizing energy consumption in the intentionally transient process operation: effect of different interval number
  9. Heat transfer efficiency in gas–solid fluidized beds with flat and corrugated walls
  10. Ant lion based optimization for performance improvement of methanol production
  11. Boundary Element Method for Viscous Flow through Out-phase Slip-patterned Microchannel under the Influence of Inclined Magnetic Field
  12. Artificial neural network models for forecasting the extracted yield of essential oils from Curcuma longa L. by hydro-distillation
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