Optimal Bio Marine Fuel production evolutionary Computation: Genetic algorithm approach for raw materials mixtures

Kyriklidis, Christos; Kyriklidis, Marios-Errikos; Loizou, Efstratios; Stimoniaris, Adam; Tsanaktsidis, Constantinos

doi:10.1016/j.fuel.2022.124232

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…Genetic algorithm is a metaheuristic technique that is motivated by the evolutionary theory of natural selection [ 47 ]. The unique advantages of GA are its capacity for optimizing real-life multivariate systems using advanced fitness functions and its ability to carry out global optimization of nonlinear systems.…”

Section: Methodsmentioning

confidence: 99%

Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process

Amenaghawon,

Igemhokhai,

Eshiemogie

et al. 2024

Heliyon

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Section: Methodsmentioning

confidence: 99%

Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process

Amenaghawon,

Igemhokhai,

Eshiemogie

et al. 2024

Heliyon

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“…Following the establishment of the initial population, a framework is implemented, encompassing a range of ± IPLS (Incremental Percentage of the Last Solution) from the best chromosome percentages. This framework, suggested by Kyriklidis et al [41], facilitates the optimization process by focusing on the optimal solution around the best chromosome from the previous generation. Each generation randomly selects a new IPLS value within a defined range (e.g., Generation 1, IPLS: 6%; Generation 2, IPLS: 9%; … Generation 100, IPLS: 10%).…”

Section: Ga-chromosome Representationmentioning

confidence: 99%

Application of Evolutionary Computation to the Optimization of Biodiesel Mixtures Using a Nature-Inspired Adaptive Genetic Algorithm

Vasileiadis,

Kyriklidis,

Karayannis

et al. 2024

Algorithms

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The present research work introduces a novel mixture optimization methodology for biodiesel fuels using an Evolutionary Computation method inspired by biological evolution. Specifically, the optimal biodiesel composition is deduced from the application of a nature-inspired adaptive genetic algorithm that first examines percentages of the ingredients in the optimal mixtures. The innovative approach’s effectiveness lies in problem simulation with improvements in the evaluation of the specific function and the way to define and tune the genetic algorithm. Environmental imperatives in the era of climate change currently impose the optimized production of alternative environmentally friendly biofuels to replace fossil fuels. Biodiesel in particular, appears to be more attractive in recent years, as it originates from renewable bio-derived resources. The main ingredients of the specific biofuel mixture investigated in this research are diesel and biodiesel (100% from bioresources). The assessment of the new biodiesel examined was performed using a fitness function that estimated both the density and cost of the fuel. Beyond the evaluation criterion of cost, density also influences the suitability of this biofuel for commercial use and market sale. The outcomes from the modeling process can be beneficial in saving cost and time for new biodiesel production by using this novel decision-making tool in comparison with randomized laboratory experimentations.

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“…Yang et al [ 19 ] solved the mathematical model of workshop layout by genetic algorithm and optimized other production factors by Plant Simulation, reducing the logistics volume by 63.5% and increasing the throughput by 42.0%. Kyriklidis et al [ 20 ] solved the problem of feedstock proportioning for marine biofuel blending using the genetic algorithm. Shehadeh et al [ 21 ] proposed and validated a mathematical model for minimizing the time and cost of earthworks based on the genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Simulation and optimization of scrap wagon dismantling system based on Plant Simulation

Chen

Dong

et al. 2023

Vis. Comput. Ind. Biomed. Art

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Based on the existing plant layout and process flow, a simulation analysis was conducted using the Plant Simulation platform with the utilization efficiency of each station and production capacity of the dismantling system as indicators. A problem with long-term suspension in the disassembly process was determined. Based on the two optimization directions of increasing material transportation equipment and expanding the buffer capacity, a cost-oriented optimization model is established. A genetic algorithm and model simulation were used to solve the model. An optimization scheme that satisfies the production needs and has the lowest cost is proposed. The results show that the optimized dismantling system solves the suspended work problem at the dismantling station and a significant improvement in productivity and station utilization efficiency compared with the previous system.

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Optimal Bio Marine Fuel production evolutionary Computation: Genetic algorithm approach for raw materials mixtures

Cited by 11 publications

References 27 publications

Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process

Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process

Application of Evolutionary Computation to the Optimization of Biodiesel Mixtures Using a Nature-Inspired Adaptive Genetic Algorithm

Simulation and optimization of scrap wagon dismantling system based on Plant Simulation

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