Kinetic modeling of multi-feed simultaneous saccharification and co-fermentation of pretreated birch to ethanol

Wang, Ruifei; Koppram, Rakesh; Olsson, Lisbeth; Franzén, Carl Johan

doi:10.1016/j.biortech.2014.09.028

Cited by 35 publications

(28 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The pretreatment temperature is reached by direct steam injection with steam at 12.3 bar. After pretreatment Wang et al (2014).…”

Section: (5) Statistical Analysis Of Simulation Resultsmentioning

confidence: 99%

“…Compared to gasoline, bioethanol has the potential to significantly reduce greenhouse gas emissions (Muñoz et al, 2014) and provide energy security (Uría-Martínez et al, 2018). Lignocellulosic ethanol production utilizes agricultural and forestry waste, amongst others wheat straw (Erdei et al, 2013;Westman et al, 2017), corn (Öhgren et al, 2006;Uppugundla et al, 2014), and wood (Wang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Scale Variability Analysis of Wheat Straw-Based Ethanol Biorefineries Identifies Bioprocess Designs Robust Against Process Input Variations

et al. 2020

Self Cite

View full text Add to dashboard Cite

Bioprocesses based on (ligno-)cellulosic biomass are highly prone to batch-to-batch variations. Varying raw material compositions and enzyme activities hamper the prediction of process yields, economic feasibility and environmental impacts. Commonly, these performance indicators are averaged over several experiments to select suitable process designs. The variabilities in performance indicators resulting from variable process inputs are often neglected, causing a risk for faulty performance predictions and poor process design choices during scale-up. In this paper, a multi-scale variability analysis framework is presented that quantifies the effects of process input variations on performance indicators. Using the framework, a kinetic model describing simultaneous saccharification and ethanol fermentation was integrated with a flowsheet process model, techno-economic analysis and life cycle assessment in order to evaluate a wheat straw-based ethanol biorefinery. Hydrolytic activities reported in the literature for the enzyme cocktail Cellic ® CTec2, ranging from 62 to 266 FPU·mL −1 , were used as inputs to the multi-scale model to compare the variability in performance indicators under batch and multi-feed operation for simultaneous saccharification and fermentation. Bioprocess simulations were stopped at ethanol productivities ≤0.1 g·L −1 ·h −1 . The resulting spreads in process times, hydrolysis yields, and fermentation yields were incorporated into flowsheet, techno-economic and life cycle scales. At median enzymatic activities the payback time was 7%, equal to 0.6 years, shorter under multi-feed conditions. All other performance indicators showed insignificant differences. However, batch operation is simpler to control and well-established in industry. Thus, an analysis at median conditions might favor batch conditions despite the disadvantage in payback time. Contrary to median conditions, analyzing the input variability favored multi-feed operation due to a lower variability in all performance indicators. Variabilities in performance indicators were at least 50% lower under multi-feed operation. Counteracting the variability in enzymatic activities by adjusting the amount of added enzyme instead resulted in Nickel et al.Multi-Scale Variability Analysis of Biorefineries higher uncertainties in environmental impacts. The results show that the robustness of performance indicators against input variations must be considered during process development. Based on the multi-scale variability analysis process designs can be selected which deliver more precise performance indicators at multiple system levels.

show abstract

“…The pretreatment temperature is reached by direct steam injection with steam at 12.3 bar. After pretreatment Wang et al (2014).…”

Section: (5) Statistical Analysis Of Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Scale Variability Analysis of Wheat Straw-Based Ethanol Biorefineries Identifies Bioprocess Designs Robust Against Process Input Variations

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Co-cultures or microbial consortia may also be utilized in the CBP systems as the third methodology. Brandon et al 2011Kang et al, 2010Kim et al, 2008Geddes et al, 2011Nieves et al, 2011Mullinnix, 2014Yang et al, 2014Jin et al, 2014 Jin et al 2012aOhgren et al 2006Wang et al, 2014aYasuda et al, 2014Zhu et al, 2014Fonseca et al, 2011Teixeira et al, 1999Jin et al, 2010Tang et al, 2011Zhang (J) et al, 2009Kim and Lee, 2005Zhang et al, 2012bYu et al, 2014Moreno et al, 2013Erdei et al, 2013aBallesteros et al, 2013Geddes et al, 2013Turhan et al, 2014Lan et al, 2013Hargreaves et al, 2013Alvira et al, 2011Olofsson et al, 2010a . In co-culture systems, saccharolytic and ethanologenic microorganisms are co-cultured to enhance efficient saccharification and fermentation in one pot.…”

Section: Strategies To Design Ideal Microorganisms For Cbpmentioning

confidence: 99%

Advances in consolidated bioprocessing systems for bioethanol and butanol production from biomass: a comprehensive review

2015

View full text Add to dashboard Cite

HIGHLIGHTS Various CBP strategies have been discussed. Recently, lignocellulosic biomass as the most abundant renewable resource has been widely considered for bioalcohols production. However, the complex structure of lignocelluloses requires a multi-step process which is costly and time consuming. Although, several bioprocessing approaches have been developed for pretreatment, saccharification and fermentation, bioalcohols production from lignocelluloses is still limited because of the economic infeasibility of these technologies. This cost constraint could be overcome by designing and constructing robust cellulolytic and bioalcohols producing microbes and by using them in a consolidated bioprocessing (CBP) system. This paper comprehensively reviews potentials, recent advances and challenges faced in CBP systems for efficient bioalcohols (ethanol and butanol) production from lignocellulosic and starchy biomass. The CBP strategies include using native single strains with cellulytic and alcohol production activities, microbial co-cultures containing both cellulytic and ethanologenic microorganisms, and genetic engineering of cellulytic microorganisms to be alcohol-producing or alcohol producing microorganisms to be cellulytic. Moreover, high-throughput techniques, such as metagenomics, metatranscriptomics, next generation sequencing and synthetic biology developed to explore novel microorganisms and powerful enzymes with high activity, thermostability and pH stability are also discussed. Currently, the CBP technology is in its infant stage, and ideal microorganisms and/or conditions at industrial scale are yet to be introduced. So, it is essential to bring into attention all barriers faced and take advantage of all the experiences gained to achieve a high-yield and low-cost CBP process.

show abstract

“…Both cell death rate caused by end product and cultivation temperature can be described by Arrhenius-type kinetics (Mutturi and Lidén 2014). Furthermore, two distinct population of cells: one is active population able to replicate, Type I cell, and another is stalled population unable to replicate due to toxicity of inhibitors, Type II cell, can also be included in the cell growth model (Wang et al 2014). The predicted cell death rates due to endogenous metabolism, temperature, end product and toxicity of inhibitors present in hydrolysate are given as follows:…”

Section: Monod Cell Growth Kineticsmentioning

confidence: 99%

“…An SSF modelling approach is a useful guiding tool for rational design of the optimal feed profiles of solid substrate, enzyme and yeast cell in fed-batch SSF to avoid poor mass and heat transfer caused by high viscosity and to maximize process efficiency, thereby meeting the technical and economic requirement of the lignocellulosic biomass process Zhao et al 2013;Huang et al 2014). Several mechanistic models for SSF have been previously developed which describe kinetics of enzyme hydrolysis and yeast cell fermentation (Van Zyl et al 2011;Morales-Rodriguez et al 2011;Mutturi and Lidén 2014;Wang et al 2014). The SSF model comprises two interlinked models, the enzyme hydrolysis model providing the quantitative analysis of the enzyme kinetics and the fermentation kinetic model describing kinetics of cell growth and sugar fermentation by organisms.…”

Section: Fed-batch Lignocellulosic Bioprocess Optimizationmentioning

confidence: 99%

Bioprocess modelling for the design and optimization of lignocellulosic biomass fermentation

Unrean

2016

Bioresour. Bioprocess.

View full text Add to dashboard Cite

Lignocellulosic feedstocks, which are currently under-exploited, can be used for the production of biofuels, such as ethanol, and for biorefinery applications to produce a variety of value-added products. Although bioconversion of lignocellulose by microbial or yeast fermentation have been reported, efficient and economical lignocellulosic fermentation process is still a challenge due to multiple process parameters involved for bioprocess design, optimization and scale-up. Bioprocess modelling strategies have been proven effective for achieving high-production process* efficiency in yield, productivity or titer of desired product. Several types of bioprocess modelling for lignocellulosic application have been developed and successfully validated as a promising alternative for rapid design, optimization and scaling up of biomass-based process. This review aims to summarize the important development of bioprocess modelling for lignocellulosic bioprocess applications towards the success of biorefineries and bio-based economy. In particular, we discuss modelling relevant to lignocellulosic bioprocess including cell modelling based on kinetics, stoichiometry and integrative approaches and fermentation kinetic modelling for process performance assessment. An overview of these modelling approaches and their application for systematic design of efficient and economical lignocellulose-based bioprocesses are given.

show abstract

Kinetic modeling of multi-feed simultaneous saccharification and co-fermentation of pretreated birch to ethanol

Cited by 35 publications

References 33 publications

Multi-Scale Variability Analysis of Wheat Straw-Based Ethanol Biorefineries Identifies Bioprocess Designs Robust Against Process Input Variations

Multi-Scale Variability Analysis of Wheat Straw-Based Ethanol Biorefineries Identifies Bioprocess Designs Robust Against Process Input Variations

Advances in consolidated bioprocessing systems for bioethanol and butanol production from biomass: a comprehensive review

Bioprocess modelling for the design and optimization of lignocellulosic biomass fermentation

Contact Info

Product

Resources

About