A comparison among stochastic optimization algorithms for parameter estimation of biochemical kinetic models

Ros, Simoní Da; Colusso, Gabriel; Weschenfelder, Thiago André; Terra, Lisiane de Marsillac; Castilhos, Fernanda de; Corazza, Marcos L.; Schwaab, Marcio

doi:10.1016/j.asoc.2013.01.019

Cited by 28 publications

(12 citation statements)

References 32 publications

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“…A set of four Arrhenius parameters [

\ln (k_{j}^{T normalref})

and EA j ], T ref = 210 ° C , were estimated concomitantly by minimizing the least squares objective function [Eqn (5)]. The optimization algorithm used was Particle Swarm Optimization (PSO) and the solution was refined using the MatLab ® built‐in routine nlinfit . The ODE system was solved with MatLab ® built‐in routine ode15s .…”

Section: Methodsmentioning

confidence: 99%

Assessment of niobium phosphate as heterogeneous catalyst in esterification with methyl acetate

Alves

Pinheiro

Brondani

et al. 2019

J of Chemical Tech & Biotech

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BACKGROUND The aim of this work was to investigate the esterification reaction from oleic acid with methyl acetate (MeA) catalyzed by niobium phosphate to produce methyl esters. This catalyst has an amorphous structure and large surface area. Catalytic tests were performed according to an experimental design. The results were analyzed statistically and all of the variables were significant, especially the temperature. RESULTS A kinetic study of the reaction was carried out and the most suitable reaction conditions were 240 °C, 10% (m/m) of catalyst and MeA: oleic acid molar ratio 10:1. After 2 h, it was possible to obtain ≈80% (m/m) methyl oleate content. A noncatalytic test corroborated niobium phosphate activity in the reaction. A kinetic model fitted experimental data for the catalyzed and noncatalyzed reactions; kinetics parameters for Arrhenius equation were estimated. In a reuse test, the catalyst showed a gradual decrease in catalytic activity over five reaction cycles. CONCLUSION Niobium phosphate can be considered as a promising heterogeneous catalyst for biodiesel synthesis. © 2019 Society of Chemical Industry

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“…A set of four Arrhenius parameters [

\ln (k_{j}^{T normalref})

Section: Methodsmentioning

confidence: 99%

Assessment of niobium phosphate as heterogeneous catalyst in esterification with methyl acetate

Alves

Pinheiro

Brondani

et al. 2019

J of Chemical Tech & Biotech

View full text Add to dashboard Cite

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“…Hence, according to this result, any meta-heuristics working in a continuous domain and showing outstanding performance when applied to benchmark test functions might not work well on (a subset of) real-world problems [15]. For instance, Da Ros et al [16] com- In this paper, we investigate the problem of the Parameter Estimation (PE) of biochemical systems, typical of Systems Biology analyses [17,18]. This research field aims at a thorough understanding of biological processes at a systemlevel by explicitly considering the complex interactions among biomolecules [19].…”

Section: Introductionmentioning

confidence: 99%

Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design

Tangherloni

Spolaor

Cazzaniga

et al. 2019

Applied Soft Computing

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Computational Intelligence methods, which include Evolutionary Computation and Swarm Intelligence, can e ciently and e↵ectively identify optimal solutions to complex optimization problems by exploiting the cooperative and competitive interplay among their individuals. The exploration and exploitation capabilities of these meta-heuristics are typically assessed by considering well-known suites of benchmark functions, specifically designed for numerical global optimization purposes. However, their performances could drastically change in the case of real-world optimization problems. In this paper, we investigate this issue by considering the Parameter Estimation (PE) of biochemical systems, a common computational problem in the field of Systems Biology. In order to evaluate the e↵ectiveness of various meta-heuristics in solving the PE problem, we compare their performance by considering a set of benchmark functions and a set of synthetic biochemical models characterized by a search space with an increasing number of dimensions. Our results show that some state-of-the-art optimization ⇤

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“…The problems tackled are both benchmarks [11][12][13][14][15][16][17] and real-life [18][19][20][21]. Some examples for chemical engineering applications include: oxidation processes [22][23][24], energy, fuels and petrol derivatives [25][26][27][28][29], fermentation [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling

Curteanu

Drăgoi

Dafinescu

2015

Advances in Computational Intelligence

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Abstract.A modelling procedure based on hybrid configuration composed of artificial neural networks, differential evolution and clonal selection algorithms is developed and applied in this work. The neural network represents the model of the system, while the differential evolution and clonal selection algorithms perform a simultaneous topological and parametric optimization of the model. The results indicated that the combination of the two optimizers produces better results compared with each of them working separately. As case study, styrene polymerization, a complex process which is difficult to model when taking into consideration all the internal interactions, was chosen. Neural networks, designed in an optimal form, proved to be adequate tools for modelling this system.

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A comparison among stochastic optimization algorithms for parameter estimation of biochemical kinetic models

Cited by 28 publications

References 32 publications

Assessment of niobium phosphate as heterogeneous catalyst in esterification with methyl acetate

Assessment of niobium phosphate as heterogeneous catalyst in esterification with methyl acetate

Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design

Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling

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