Dynamic modeling and validation of a lignocellulosic enzymatic hydrolysis process – A demonstration scale study

Prunescu, Remus Mihail; Sin, Gürkan

doi:10.1016/j.biortech.2013.10.029

Cited by 19 publications

(34 citation statements)

References 22 publications

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“…The biomass composition resembles wheat straw with an initial dry matter of 89 % [6]. [19] (enzymatic hydrolysis) and [24] (C5 and C6 co-fermentation).…”

Section: Resultsmentioning

confidence: 99%

“…The enzymatic hydrolysis process was thoroughly described and analyzed in [19]. It runs at a high dry matter content in a continuous mode and consists of a series of hydrolysis tanks.…”

Section: Second Generation Bioethanol Plantmentioning

confidence: 99%

“…The system constraints are formulated based on validated large scale models for: hydrothermal pretreatment with steam [17], enzymatic hydrolysis [19], and C5 and C6 co-fermentation [24].…”

Section: The Optimization Layermentioning

confidence: 99%

“…The optimization layer uses a dynamic biorefinery simulator to calculate the have already been published in [17] and [19]. The fermentation tank is modeled as a continuous stirred tank reactor (CSTR) with reaction kinetics derived from [24,26].…”

Section: Mathematical Modelsmentioning

confidence: 99%

“…The enzymatic activity is a function of pH and temperature, which resemble Gaussian curves with single peaks at pH of 5 units and temperature 50 • C [19]. Any deviations from the optimal point would reduce the enzymatic efficiency.…”

Section: Identify the Decision Variablesmentioning

confidence: 99%

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Model-based plantwide optimization of large scale lignocellulosic bioethanol plants

Prunescu

Blanke

Jakobsen

et al. 2017

Biochemical Engineering Journal

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Second generation biorefineries transform lignocellulosic biomass into chemicals with higher added value following a conversion mechanism that consists of: pretreatment, enzymatic hydrolysis, fermentation and purification. The objective of this study is to identify the optimal operational point with respect to maximum economic profit of a large scale biorefinery plant using a systematic model-based plantwide optimization methodology. The following key process parameters are identified as decision variables: pretreatment temperature, enzyme dosage in enzymatic hydrolysis, and yeast loading per batch in fermentation. The plant is treated in an integrated manner taking into account the interactions and trade-offs between the conversion steps. A sensitivity and uncertainty analysis follows at the optimal solution considering both model and feed parameters. * Corresponding authorEmail address: gsi@kt.dtu.dk (Gürkan Sin) March 20, 2017 It is found that the optimal point is more sensitive to feedstock composition than to model parameters, and that the optimization supervisory layer as part of a plantwide automation system has the following benefits: (1) increases the economical profit, (2) flattens the objective function allowing a wider range of operation without negative impact on profit, and (3) reduces considerably the uncertainty on profit. Preprint submitted to Biochemical Engineering Journal

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“…The biomass composition resembles wheat straw with an initial dry matter of 89 % [6]. [19] (enzymatic hydrolysis) and [24] (C5 and C6 co-fermentation).…”

Section: Resultsmentioning

confidence: 99%

“…The enzymatic hydrolysis process was thoroughly described and analyzed in [19]. It runs at a high dry matter content in a continuous mode and consists of a series of hydrolysis tanks.…”

Section: Second Generation Bioethanol Plantmentioning

confidence: 99%

“…The system constraints are formulated based on validated large scale models for: hydrothermal pretreatment with steam [17], enzymatic hydrolysis [19], and C5 and C6 co-fermentation [24].…”

Section: The Optimization Layermentioning

confidence: 99%

Section: Mathematical Modelsmentioning

confidence: 99%

Section: Identify the Decision Variablesmentioning

confidence: 99%

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Model-based plantwide optimization of large scale lignocellulosic bioethanol plants

Prunescu

Blanke

Jakobsen

et al. 2017

Biochemical Engineering Journal

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Dynamic modeling and validation of a biomass hydrothermal pretreatment process—a demonstration scale study

et al. 2015

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Continuous enzymatic hydrolysis of lignocellulosic biomass in a membrane‐reactor system

Stickel

Adhikari

Sievers

et al. 2018

J of Chemical Tech & Biotech

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BACKGROUND:Converting abundant lignocellulosic biomass to sugars as fungible precursors to fuels and chemicals has the potential to diversify the supply chain for those products, but further process improvements are needed to achieve economic viability. In the current work, process intensification of the key enzymatic hydrolysis unit operation is demonstrated by means of a membrane reactor system that was operated continuously.RESULTS: Lignocellulosic biomass (pretreated corn stover) and buffered enzyme solution were fed to a continuously stirred-tank reactor, and clarified sugar solution was withdrawn via a commercial tubular ultrafiltration membrane. The membrane permeance decline and membrane cleaning efficacy were studied and did not vary significantly when increasing fraction insoluble solids (FIS) from 2.5% to 5%. Continuous enzymatic hydrolysis was

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Dynamic modeling and validation of a lignocellulosic enzymatic hydrolysis process – A demonstration scale study

Cited by 19 publications

References 22 publications

Model-based plantwide optimization of large scale lignocellulosic bioethanol plants

Model-based plantwide optimization of large scale lignocellulosic bioethanol plants

Dynamic modeling and validation of a biomass hydrothermal pretreatment process—a demonstration scale study

Continuous enzymatic hydrolysis of lignocellulosic biomass in a membrane‐reactor system

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