A framework for model-based optimization of bioprocesses under uncertainty: Lignocellulosic ethanol production case

Morales-Rodríguez, Ricardo; Meyer, Anne S.; Gernaey, Krist V.; Sin, Gürkan

doi:10.1016/j.compchemeng.2011.12.004

Cited by 57 publications

(52 citation statements)

References 35 publications

(37 reference statements)

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“…Recent commercial cellulase preparations have been shown to be effective at hydrolyzing cellulose under industrially relevant conditions, however the high cost of enzymes remains a significant barrier to the economical production of ethanol from lignocellulosic biomass [3,4]. It is therefore necessary to reduce the amount of enzyme required for the enzymatic hydrolysis step.…”

Section: Introductionmentioning

confidence: 99%

Enzymatic lignocellulose hydrolysis: Improved cellulase productivity by insoluble solids recycling

Weiss

Börjesson²,

Pedersen³

et al. 2013

Biotechnol Biofuels

111

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BackgroundIt is necessary to develop efficient methods to produce renewable fuels from lignocellulosic biomass. One of the main challenges to the industrialization of lignocellulose conversion processes is the large amount of cellulase enzymes used for the hydrolysis of cellulose. One method for decreasing the amount of enzyme used is to recycle the enzymes. In this study, the recycle of enzymes associated with the insoluble solid fraction after the enzymatic hydrolysis of cellulose was investigated for pretreated corn stover under a variety of recycling conditions.ResultsIt was found that a significant amount of cellulase activity could be recovered by recycling the insoluble biomass fraction, and the enzyme dosage could be decreased by 30% to achieve the same glucose yields under the most favorable conditions. Enzyme productivity (g glucose produced/g enzyme applied) increased between 30 and 50% by the recycling, depending on the reaction conditions. While increasing the amount of solids recycled increased process performance, the methods applicability was limited by its positive correlation with increasing total solids concentrations, reaction volumes, and lignin content of the insoluble residue. However, increasing amounts of lignin rich residue during the recycle did not negatively impact glucose yields.ConclusionsTo take advantage of this effect, the amount of solids recycled should be maximized, based on a given processes ability to deal with higher solids concentrations and volumes. Recycling of enzymes by recycling the insoluble solids fraction was thus shown to be an effective method to decrease enzyme usage, and research should be continued for its industrial application.

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Section: Introductionmentioning

confidence: 99%

Enzymatic lignocellulose hydrolysis: Improved cellulase productivity by insoluble solids recycling

Weiss

Börjesson²,

Pedersen³

et al. 2013

Biotechnol Biofuels

111

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“…These models can include alternative product choices as well as alternative processing steps, and can be used to solve in combinatorial process design problems that are analogous to cell factory design problems described above [61]. Process flow models have also been integrated with dynamic bioreactor models for both fermentation and biomass hydrolysis reactors [62].…”

Section: Modeling the Overall Bioprocessmentioning

confidence: 99%

Multi‐scale modeling for sustainable chemical production

Zhuang

Bakshi

2013

Biotechnology Journal

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With recent advances in metabolic engineering, it is now technically possible to produce a wide portfolio of existing petrochemical products from biomass feedstock. In recent years, a number of modeling approaches have been developed to support the engineering and decision-making processes associated with the development and implementation of a sustainable biochemical industry. The temporal and spatial scales of modeling approaches for sustainable chemical production vary greatly, ranging from metabolic models that aid the design of fermentative microbial strains to material and monetary flow models that explore the ecological impacts of all economic activities. Research efforts that attempt to connect the models at different scales have been limited. Here, we review a number of existing modeling approaches and their applications at the scales of metabolism, bioreactor, overall process, chemical industry, economy, and ecosystem. In addition, we propose a multi-scale approach for integrating the existing models into a cohesive framework. The major benefit of this proposed framework is that the design and decision-making at each scale can be informed, guided, and constrained by simulations and predictions at every other scale. In addition, the development of this multi-scale framework would promote cohesive collaborations across multiple traditionally disconnected modeling disciplines to achieve sustainable chemical production.

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“…The optimal solution is analyzed from a sensitivity point of view using similar tools as in [14,15,17]. Mathematical models that describe complex systems are often over-parametrized.…”

Section: Plantwide Optimization Methodologymentioning

confidence: 99%

“…The paper shows how overall optimization can be achieved and how sensitivity and uncertainty can be assessed with respect to feedstock composition and kinetic parameters. A Monte Carlo technique with Latin Hypercube Sampling and correlation control is used for the uncertainty analysis following the methodology from [14,15].…”

mentioning

confidence: 99%

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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A framework for model-based optimization of bioprocesses under uncertainty: Lignocellulosic ethanol production case

Cited by 57 publications

References 35 publications

Enzymatic lignocellulose hydrolysis: Improved cellulase productivity by insoluble solids recycling

Enzymatic lignocellulose hydrolysis: Improved cellulase productivity by insoluble solids recycling

Multi‐scale modeling for sustainable chemical production

Model-based plantwide optimization of large scale lignocellulosic bioethanol plants

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