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A state of art review on applications of multi-objective evolutionary algorithms in chemicals production reactors

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Abstract

Chemical reactors are employed to produce several materials, which are utilized in numerous applications. The wide use of these chemical engineering units shows their importance as their performance vastly affects the production process. Thus, improving these units will develop the process and/or the manufactured material. Multi-objective optimization (MOO) with evolutionary algorithms (EA’s) has been used to solve several real world complex problems for improving the performance of chemical reactors with conflicting objectives. These objectives are of different nature as they could be economy, environment, safety, energy, exergy and/or process related. In this review, a brief description for MOO and EA’s and their several types and applications is given. Then, MOO studies, which are related to the materials’ production via chemical reactors, those were conducted with EA’s are classified into different classes and discussed. The studies were classified according to the produced material to hydrogen and synthesis gas, petrochemicals and hydrocarbons, biochemical, polymerization and other general processes. Finally, some guidelines are given to help in deciding on future research.

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Funding

This work was supported by Sultan Qaboos University, Sultanate of Oman under Grant IG/ENG/PCED/19/01.

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Authors and Affiliations

  1. Department of Petroleum & Chemical Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, P.C. 123, Al-Khod, Muscat, Sultanate of Oman

    Zainab Al Ani, Ashish M. Gujarathi & Ala’a H. Al-Muhtaseb

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  1. Zainab Al Ani
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  2. Ashish M. Gujarathi
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  3. Ala’a H. Al-Muhtaseb
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Correspondence to Ashish M. Gujarathi.

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Nomenclature

Nomenclature

ADE

Adaptive Differential Evolution

AMOPSO-AHP

Adaptive Multi-Objective Particle Swarm Optimization Analytic Hierarchy Process

ANFIS

Adaptive Neuro-Fuzzy Inference System

AODE

Adaptive Opposition Based Differential Evolution

ALIFMO

Additive Linear Interdependent Fuzzy Multi-Objective Optimization

ACO

Ant Colony Algorithm

ABC

Artificial Bee Colony Algorithm

AIS

Artificial Immune System

ANN-GA

Artificial Neural Network-Genetic Algorithm

ALGA

Augmented Lagrangian Genetic Algorithm

B-NSGA-II-aJG

Biomimetic Non-Dominated Sorting Genetic Algorithm-II-a Jumping Genes

CPEA

Clustering Pareto Evolutionary Algorithm

CROA

Coral Reef Algorithm

DETL

Differential Evolution with Tabu List

DE

Differential Evolution

DPEA

Dual Population Evolutionary Algorithm

EA’s

Evolutionary Algorithms

EMOO

Excel-Based Multi-Objective Optimization

EP

Evolutionary Programing

FCCU

Fluidized-Bed Catalytic Cracking Unit

FADE

Fuzzy Adaptive Differential Evolution

GA

Genetic Algorithm

GSA

Gravitational Search Algorithm

GSA*

Grid Search Approach

HDE

Hybrid Differential Evolution

HS

Harmony Search

HMODE-DLS

Hybrid Dynamic Local Search MODE

ISADE

Immune Self-Adaptive Multi-Objective Differential Evolution Algorithm

I-MODE

Improved Multi-Objective Differential Evolution

JG

Jumping Genes

MINLP

Mixed Integer Non Linear Programming

MUGA

Multicriteria Genetic Algorithm

MODE

Multi-Objective Differential Evolution

MOGA

Multi-Objective Genetic Algorithm

MOGA-II

Multi-Objective Genetic Algorithm-II

MOO

Multi-Objective Optimization

MOPSO

Multi-Objective Particle Swarm Optimization

MOSA

Multi-Objective Simulated Annealing Algorithm

NComDE

Newton Competitive Differential Evolution

NPGA

Niched-Pareto Genetic Algorithm

NSPSO

Non-Dominated Particle Swarm Optimization

NSGA

Non-Dominated Sorting Genetic Algorithm

NSGA-II

Non-Dominated Sorting Genetic Algorithm-II

NSGA-II-RJG

Non-Dominated Sorting Genetic Algorithm-II With Real Jumping Genes

ODE

Opposition Based Differential Evolution

PSO

Particle Swarm Algorithm

PET

Polyethylene Terephthalate

PBMODE

Population Based Multi-Objective Differential Evolution

MODE-RMO

Ranking-Based Mutation Multi-Objective Differential Evolution

RPCEMA

Reference Point based Competing Evolutionary Membrane Algorithm

RSM

Response Surface Method

SADE

Self-Adaptive Multi-Objective Differential Evolution Algorithm

SQP*

Sequential Quadratic Programming

SOO

Single Objective Optimization

SPEA

Strength Pareto Evolutionary Algorithm

SQP

Successive Quadratic Programming

TL

Tabu List

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Al Ani, Z., Gujarathi, A.M. & Al-Muhtaseb, A.H. A state of art review on applications of multi-objective evolutionary algorithms in chemicals production reactors. Artif Intell Rev 56, 2435–2496 (2023). https://doi.org/10.1007/s10462-022-10219-z

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