The use of differential evolution algorithm for solving chemical engineering problems

Drăgoi, Elena Niculina; Curteanu, Silvia

doi:10.1515/revce-2015-0042

Cited by 35 publications

(17 citation statements)

References 132 publications

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“…Differential evolution (DE) is a powerful evolutionary algorithm 18 that is popular for pattern recognition and optimization in engineering 19 . Multiobjective DE (MODE) is an improved DE modified to fit multiobjective problems 20 , in which n ( n > 1) objectives are considered to synchronously search for optimal solutions 21 ; for example, maximization objectives can be formulated as maximize( f 1 ( X ), …, f i ( X )), where X ∈ , i is the number of objectives, is the feasible solution set, and f ( X ) is an objective function.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions

Yang

Chuang

Lin

2017

Sci Rep

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Epistasis within disease-related genes (gene–gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene–gene interactions; however, some gene–gene interactions may require identification through multiple contingency table measures. In this study, a multiobjective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene–gene interactions. Two contingency table measures, namely the correct classification rate and normalized mutual information, were selected to design the fitness functions in MODEMDR. The characteristics of multiobjective optimization enable MODEMDR to use multiple measures to efficiently and synchronously detect significant gene–gene interactions within a reasonable time frame. Epistatic models with and without marginal effects under various parameter settings (heritability and minor allele frequencies) were used to assess existing methods by comparing the detection success rates of gene–gene interactions. The results of the simulation datasets show that MODEMDR is superior to existing methods. Moreover, a large dataset obtained from the Wellcome Trust Case Control Consortium was used to assess MODEMDR. MODEMDR exhibited efficiency in identifying significant gene–gene interactions in genome-wide association studies.

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Section: Introductionmentioning

confidence: 99%

Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions

Yang

Chuang

Lin

2017

Sci Rep

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“…In addition, the process was optimized with a second method represented by DE, an efficient metaheuristic approach, that was successfully used (simple or in combination with other approaches) for optimization and modelling of a wide range of systems: robot control 59 , water quality monitoring 60 , adsorption processes 61 . Examples of DE application in chemical engineering can be found in 62 . The DE based software used was developed in 63 in combination with artificial neural networks (ANNs) and applied for predicting the liquid crystalline property of some organic compounds.…”

Section: Methodsmentioning

confidence: 99%

Differential evolution-based optimization of corn stalks black liquor decolorization using active carbon and TiO2/UV

Nechita

Suditu

Puițel

et al. 2021

Sci Rep

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In this work, the active carbon adsorption and TiO2/UV decolorization of black liquor were studied through experimental analysis (planned using Design of Experiments), modelling and optimization (with Response Surface Method and Differential Evolution). The aim is to highlight the importance of optimization methods for increasing process efficiency. For active carbon adsorption, the considered process parameters were: quantity of active carbon, dilution, and contact time. For TiO2 promoted photochemical decolorization the process parameters were: TiO2 concentration, UV path length and irradiation time. The determined models had an R squared of 93.82% for active carbon adsorption and of 92.82% for TiO2/UV decolorization. The optimization of active carbon resulted in an improvement from 83.08% (corresponding to 50 g/L quantity of active carbon, 30 min contact time and 200 dilution) to 100% (corresponding to multiple combinations). The optimization of TiO2/UV decolorization indicated an increase of efficiency from 36.63% (corresponding to 1 g/L TiO2 concentration, 60 min irradiation time and 5 cm UV path length) to 46.83% (corresponding to 0.4 g/L TiO2 concentration, 59.99 min irradiation time and 2.85 cm UV path length). These results show that the experiments and the subsequent standard RSM optimization can be further improved, leading to better performance.

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“…This type of algorithm is efficient in solving parametric determination problems since it uses stochastic methods, and it can analyze discontinuous spaces and migrate from local to global minima. 19,32,42,43 Information about initialization, mutation, crossover, and selection is listed in Table 2.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Modeling Application To Evaluate the Performance of Spathaspora passalidarum in Second-Generation Ethanol Production: Parametric Dynamics and the Likelihood Confidence Region

Martinez-Jimenez

Ribeiro

Sargo

et al. 2021

Ind. Eng. Chem. Res.

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Lignocellulosic materials and agro-industrial residues are essential in a sustainable biorefinery. The effective fermentation of renewable sugars is still an important challenge for an economically viable production of biofuels and biochemicals. This challenge involves biological aspects, such as choosing the microbial platform, and industrial aspects. These aspects can be assessed employing mathematical models to understand the complex synergies between the microorganism and processes. In this work, the performance of the yeast Spathaspora passalidarum was evaluated for the fermentation of xylose and glucose at different proportions, initial concentrations, and initial cell densities using an unstructured−unsegregated kinetic model. The likelihood confidence region, correlations between the parameters, and the convergence of the parameters through the optimization process were identified. It was also possible to identify that π Xyl, max , μ Glu, max , K ' S, Xyl , Y ' PEth/SGlu , Y ' PEth/SXyl , and Y ' X/SGlu were the most representative parameters, due to the high correlation (positive and negative) they have with the other model parameters. It was also possible to identify how initial substrate concentrations affected the specific uptake substrate and ethanol production rates; in this sense, the increase in the glucose concentration causes a decrease in the specific ethanol formation rate at a high concentration of xylose for S. passalidarum cultivated in the conditions proposed by this study.

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The use of differential evolution algorithm for solving chemical engineering problems

Cited by 35 publications

References 132 publications

Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions

Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions

Differential evolution-based optimization of corn stalks black liquor decolorization using active carbon and TiO2/UV

Dynamic Modeling Application To Evaluate the Performance of Spathaspora passalidarum in Second-Generation Ethanol Production: Parametric Dynamics and the Likelihood Confidence Region

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