Artificial Hummingbird-Based Optimisation with Advanced Crowding Distance of Energy Reduction in the Polyethylene Reactors

Rohman, Fakhrony Sholahudin; Alwi, Sharifah Rafidah Wan; Muhammad, Dinie; Idris, Iylia; Zahan, Khairul Azly; Murat, Muhamad Nazri; Azmi, Ashraf

doi:10.1007/s41660-023-00369-0

Process Integr Optim Sustain

2023

DOI: 10.1007/s41660-023-00369-0

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Artificial Hummingbird-Based Optimisation with Advanced Crowding Distance of Energy Reduction in the Polyethylene Reactors

Fakhrony Sholahudin Rohman,

Sharifah Rafidah Wan Alwi,

Dinie Muhammad

et al.

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2024

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Cited by 3 publications

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Stochastic-Based Optimization Strategy of Dimethyl Oxalate Hydrogenation for Ethylene Glycol Production

Rohman,

Alwi,

Kelani

et al. 2024

Process Integr Optim Sustain

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Stochastic-Based Optimization Strategy of Dimethyl Oxalate Hydrogenation for Ethylene Glycol Production

Rohman,

Alwi,

Kelani

et al. 2024

Process Integr Optim Sustain

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A non-dominated sorting based multi-objective neural network algorithm of ethylene glycol hydrogenation reactor in energy reduction

Rohman,

Alwi,

Muhammad

et al. 2024

Discov Chem Eng

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Multi-objective Bonobo optimisers of industrial low-density polyethylene reactor

Rohman,

Alwi,

Muhammad

et al. 2024

Chemical Product and Process Modeling

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A multi-objective optimization (MOO) technique to produce a low-density polyethylene (LDPE) is applied to address these two problems: increasing conversion and reducing operating cost (as the first optimization problem, P1) and increasing productivity and reducing operating cost (as the second optimization problem, P2). ASPEN Plus software was utilized for the model-based optimization by executing the MOO algorithm using the tubular reactor model. The multi-objective optimization of multi-objective Bonobo optimisers (MOBO-I, MOBO-II and MOBO-III) are utilised to solve the optimization problem. The performance matrices, including hypervolume, pure diversity, and distance, are used to decide on the best MOO method. An inequality constraint was introduced on the temperature of the reactor to prevent run-away. According to the findings of the study, the MOBO-II for Problems 1 and 2 was the most effective MOO strategy. The reason is that the solution set found represents the most accurate, diversified, and acceptable distribution points alongside the Pareto Front (PF) in terms of homogeneity. The minimum operating cost, the maximum conversion and productivity obtained by MOBO-II are Mil. RM/year 114.3, 31.45 %, Mil. RM/year 545.3, respectively.

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Artificial Hummingbird-Based Optimisation with Advanced Crowding Distance of Energy Reduction in the Polyethylene Reactors

Cited by 3 publications

References 23 publications

Stochastic-Based Optimization Strategy of Dimethyl Oxalate Hydrogenation for Ethylene Glycol Production

Stochastic-Based Optimization Strategy of Dimethyl Oxalate Hydrogenation for Ethylene Glycol Production

A non-dominated sorting based multi-objective neural network algorithm of ethylene glycol hydrogenation reactor in energy reduction

Multi-objective Bonobo optimisers of industrial low-density polyethylene reactor

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