Optimizing performance of a microbial carbon-capture cell using Box-Behnken design

Neethu, B.; Tholia, V.; Ghangrekar, Makarand M.

doi:10.1016/j.procbio.2020.05.018

Cited by 25 publications

(6 citation statements)

References 40 publications

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“…The response surface method is an experimental design method that uses various quadratic regression equations to analyze the regression equation through the functional relationship between the experimental data relevant factor and the response value to find the best process parameters. It is widely used in many fields. − Compared with single-factor design and orthogonal design, it has the advantages of a shorter design time, a shorter period, and a higher accuracy of a regression equation. There are many design schemes in experimental RSM design, among which the Box–Behnken design and the central composite design are the most common design models.…”

Section: Methodsmentioning

confidence: 99%

Scale-Up and Development of Synthesis 2-Ethylhexyl Nitrate in Microreactor Using the Box–Behnken Design

Guo

Zhan

Zhu

et al. 2021

Org. Process Res. Dev.

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In this study, kilogram-scale synthesis of 2-ethylhexyl nitrate in a microreactor was reported. Using Box–Behnken experimental design, the effects of process parameters such as temperature, molar ratio, total flow rate, and mass fraction of sulfuric acid on the yield of the target product were systematically investigated. The experimental design was studied at four factors and three levels. The results revealed that the sulfuric acid mass fraction and temperature significantly affected the yield of the product. There was a good agreement between the experimental results and predicted results by the proposed regression model. Subsequently, the mixing effect of the liquid–liquid heterogeneous system in microreactor was simulated by using computational fluid dynamics. The results provide solid evidence that the microreactor has excellent mixing efficiency in mass and heat transfer compared with the conventional reactor. Consequently, based on the results of process parameter optimization, an enlarged microreactor was designed to intensify the preparation of 2-ethylhexyl nitrate by a throughput of 16 kg/h.

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Section: Methodsmentioning

confidence: 99%

Scale-Up and Development of Synthesis 2-Ethylhexyl Nitrate in Microreactor Using the Box–Behnken Design

Guo

Zhan

Zhu

et al. 2021

Org. Process Res. Dev.

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“…Thus, a statistical evaluation was conducted by adopting the Box-Behnken design to assess parameters such as the concentration of the lipid-extracted algae (LEA) of the various microalgae cultures present in the anodes and the concentration of nitrates in the catholyte. 162 Although there is no interactive effect among the parameters (LEA, photoperiod, and nitrate) on the power density, each parameter exhibits signicant individual effects. The growth of the algae depends on the duration of light and the concentration of nitrate.…”

Section: Algal Carbon Capture Cellsmentioning

confidence: 99%

Phototrophic microbial fuel cells: a greener approach to sustainable power generation and wastewater treatment

Sonawane¹,

Vijay²,

Deng³

et al. 2023

Sustainable Energy Fuels

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“…116 Milner et al 116 found that the framework dmpn- 121 Sivarajasekar et al, 122 Jafari et al, 123 and Merzouki et al 124 used response surface methodology to investigate the effect of factors for a fixed-bed column to find the optimum factors, that is, the fixed-bed parameters such as bed height, inlet flow rate, and particle size, and study the interactions among these factors using polynomial models. Although black-box DoE has been widely used for adsorption processes, 13,[125][126][127][128][129] the underlying data-driven surrogate models cannot make predictions if the experimental apparatus is modified (e.g., longer bed length) as easily as science-based models.…”

Section: Case Study: Fixed-bed Breakthrough Experimentsmentioning

confidence: 99%

Pyomo.DOE: An open‐source package for model‐based design of experiments in Python

Wang

Dowling

2022

AIChE Journal

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Predictive mathematical models are a cornerstone of science and engineering. Yet selecting, calibrating, and validating said science‐based models often remains an art in practice. Model‐based design of experiments (MBDoE) provides a systematic framework to maximize information gain from experiments while minimizing time and resource costs. But MBDoE remains limited to niche application areas, in part because practitioners must integrate expertise in statistics, computational optimization, and modeling. To help reduce this barrier, we introduce Pyomo.DOE, an open‐source package for MBDoE. Pyomo.DOE uses a nonlinear sensitivity analysis code k_aug to quickly approximate the Fisher information matrix and leverages a new stochastic programming abstraction. We demonstrate Pyomo.DOE with the first application of MBDoE to fixed‐bed breakthrough experiments, which highlights the power of Pyomo.DOE to quantify the value of experimental modifications a priori for large‐scale partial differential‐algebraic equation (PDAE) models. We also provide a mathematical primer on MBDoE targeted at general chemical engineers.

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Optimizing performance of a microbial carbon-capture cell using Box-Behnken design

Cited by 25 publications

References 40 publications

Scale-Up and Development of Synthesis 2-Ethylhexyl Nitrate in Microreactor Using the Box–Behnken Design

Scale-Up and Development of Synthesis 2-Ethylhexyl Nitrate in Microreactor Using the Box–Behnken Design

Phototrophic microbial fuel cells: a greener approach to sustainable power generation and wastewater treatment

Pyomo.DOE: An open‐source package for model‐based design of experiments in Python

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