Kinetic modeling for enzymatic hydrolysis of pretreated sugarcane straw

Angarita, J. D.; Souza, Renata Beraldo Alencar de; Cruz, Antonio José Gonçalves; Biscaia, Evaristo C.; Secchi, Argimiro Resende

doi:10.1016/j.bej.2015.05.021

Cited by 33 publications

(22 citation statements)

References 36 publications

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“…Two cases studies illustrate the performance of the proposed modifications in SELEST: (1) a hypothetical model [6] , and (2) an enzymatic hydrolysis model [7][8][9] . The hypothetical model presents a simple problem for parameter identifiability with known solution, composed of a linear model in the parameters and containing four parameters, being two of them correlated due to an inadequate experimental design.…”

Section: Methodsmentioning

confidence: 99%

“…The enzymatic hydrolysis model corresponds to a challenge problem for parameter identifiability, described by a biological dynamic model containing 16 highly correlated parameters, given the scarce experimental data. For the second case study, the experimental data set and the initial estimates of the kinetic and adsorption parameters were obtained from Angarita et al [9]…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Improving the Convergence of the SELEST Identifiability Procedure

Bernardino¹,

Alberton²,

Secchi³

2016

Modelling, Simulation and Identification / 841: Intelligent Systems and Control

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SELEST is a procedure for identifiability of parameters in which selection and estimation steps are simultaneous, ensuring a well-conditioned estimation problem for a subset of identifiable parameters. Nevertheless, since SELEST is based on local sensitivity analysis, the identifiability criteria are dependent on the parameters initial values, requiring intensive parameters evaluation. In order to improve the convergence of the algorithm, we propose to update the values of the selected parameters and their sensitivity submatrix when re-ranking the remaining parameters. Therefore, the parameters estimations are performed using more appropriate values than the initial estimates. Two cases studies illustrate the performance of the proposed procedure: a hypothetical model, and an enzymatic hydrolysis model. Results demonstrate that the proposed modifications improved the performance of the algorithm, reducing the computational time significantly.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Improving the Convergence of the SELEST Identifiability Procedure

Bernardino¹,

Alberton²,

Secchi³

2016

Modelling, Simulation and Identification / 841: Intelligent Systems and Control

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show abstract

“…The models presented by Kadam et al (2004) and Prunescu and Sin (2013) have been implemented in several case studies. Amongst others are the kinetic modeling of the enzymatic hydrolysis of pretreated sugarcane straw, as presented by Angarita et al (2015), and the model-based optimization of a large-scale bioethanol plant, presented by Prunescu et al (2017). More recently, Liang et al (2019) developed a longterm Holtzapple-Caram-Humphrey-1 (HCH-1) model for the enzymatic hydrolysis of cellulose (C) by adapting the original HCH-1 model developed by Holtzapple et al (1984).…”

Section: Enzymatic Hydrolysismentioning

confidence: 99%

Modeling Biowaste Biorefineries: A Review

Buck

Polańska

Impe

2020

Front. Sustain. Food Syst.

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The biorefining of biowaste is an upcoming novel strategy, but is mostly still in its conceptual phase. Biowaste biorefineries would allow (rural) communities to convert their biowaste into value-added biofuels, biochemical compounds, and fertilizers. Several different types of biowaste biorefineries have already been developed, but little to none of these designs are already commercially exploited. Their further development and commercial implementation is hampered by the high investment costs and risks, little trusts in its novel technologies, expected yields and profits, and operating reliability. Modeling these integrated processes, together with their supply chains, would allow for optimizing the considered biorefinery designs and coincidently speeding up the R&D-process. The optimized biorefinery designs and supply chains would additionally embed an increased amount of trust in potential investors in terms of the economic sustainability of the considered novel processes. Therefore, in this publication, a summary of existing biorefinery models is presented, together with supply chain network models. The discussed biorefinery models are categorized according to the conversion platform they use, being thermochemical, biological, or hybrid ones. Furthermore, the overall inherent advantages and disadvantages of all conversion platforms are summarized and a scope of further research needs is presented.

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“…Equations (5)-(9) are kinetic models based on the Langmuir adsorption isotherm, and extended to hemicellulose hydrolysis [31]. The parameters in these equations follow the same units used by Kadam It is important to note that the information regarding the amount of each enzyme within the enzymatic complex was unavailable.…”

Section: Standalone Reaction Rate Modelsmentioning

confidence: 99%

Fuzzy-Enhanced Modeling of Lignocellulosic Biomass Enzymatic Saccharification

et al. 2019

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The enzymatic hydrolysis of lignocellulosic biomass incorporates many physico-chemical phenomena, in a heterogeneous and complex media. In order to make the modeling task feasible, many simplifications must be assumed. Hence, different simplified models, such as Michaelis-Menten and Langmuir-based ones, have been used to describe batch processes. However, these simple models have difficulties in predicting fed-batch operations with different feeding policies. To overcome this problem and avoid an increase in the complexity of the model by incorporating other phenomenological terms, a Takagi-Sugeno Fuzzy approach has been proposed, which manages a consortium of different simple models for this process. Pretreated sugar cane bagasse was used as biomass in this case study. The fuzzy rule combines two Michaelis-Menten-based models, each responsible for describing the reaction path for a distinct range of solids concentrations in the reactor. The fuzzy model improved fitting and increased prediction in a validation data set.

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Kinetic modeling for enzymatic hydrolysis of pretreated sugarcane straw

Cited by 33 publications

References 36 publications

Improving the Convergence of the SELEST Identifiability Procedure

Improving the Convergence of the SELEST Identifiability Procedure

Modeling Biowaste Biorefineries: A Review

Fuzzy-Enhanced Modeling of Lignocellulosic Biomass Enzymatic Saccharification

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