Controlled feeding of lignocellulosic substrate enhances the performance of fed-batch enzymatic hydrolysis in a stirred tank reactor

Sotaniemi, Ville Hermanni; Taskila, Sanna; Ojamo, Heikki; Tanskanen, Juha

doi:10.1016/j.biombioe.2016.05.037

Cited by 26 publications

(16 citation statements)

References 25 publications

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“…Qualitative filter paper, 600 (VWR International Europe BVBA, Belgium) was used as the substrate, and the cellulose content was measured to be 79% by the method described in (Sluiter et al, 2012). Flashzyme TM Plus 2G (Roal Oy, Finland) was used as the enzyme mixture, and the filter paper activity of the enzyme mixture is 111 FPU/ml and the cellobiase activity is 450 CB/ml (Sotaniemi et al, 2016). The protein concentration of the enzyme mixture was estimated to be 208 g/l by A280 assay of Nanodrop spectrophotometer (Thermo Scientific, US).…”

Section: Experimental Methodsmentioning

confidence: 99%

“…The goal of this study is to form a model that is applicable for the fed-batch operation of hydrolysis process aiming at high substrate concentration. It has been shown in previous studies that fed-batch operation can significantly reduce the overall energy and peak power consumption for agitation in a stirred tank reactor compared to batch-wise hydrolysis (Corrêa et al, 2016;Sotaniemi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Sample handling was similar to the small scale experiments, but the samples were not boiled but instead maintained in ice after sampling and stored frozen. A more detailed description of the fed-batch experiments can be found from(Sotaniemi et al, 2016).The data from the small scale hydrolysis experiments with a reaction time of 30 minutes were used to fit the parameters (E max , K L and k max ) in the model describing the initial kinetics without rate decreasing factors (Eqs. (1) -(6)).…”

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confidence: 99%

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A discretized model for enzymatic hydrolysis of cellulose in a fed-batch process

Tervasmäki

Sotaniemi

Kangas

et al. 2017

Bioresource Technology

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In the enzymatic hydrolysis of cellulose, several phenomena have been proposed to cause a decrease in the reaction rate with increasing conversion. The importance of each phenomenon is difficult to distinguish from batch hydrolysis data. Thus, kinetic models for the enzymatic hydrolysis of cellulose often suffer from poor parameter identifiability. This work presents a model that is applicable to fed-batch hydrolysis by discretizing the substrate based on the feeding time. Different scenarios are tested to explain the observed decrease in reaction rate with increasing conversion, and comprehensive assessment of the parameter sensitivities is carried out. The proposed model performed well in the broad range of experimental conditions used in this study and when compared to literature data. Furthermore, the use of data from fed-batch experiments and discretization of the model substrate to populations was found to be very informative when assessing the importance of the rate-decreasing phenomena in the model.

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Section: Experimental Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A discretized model for enzymatic hydrolysis of cellulose in a fed-batch process

Tervasmäki

Sotaniemi

Kangas

et al. 2017

Bioresource Technology

Self Cite

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“…This measurement can be used to assess how alterations in the reactor's architecture and its impellers impact the process power consumption [2][3][4][5]. Moreover, torque dynamometers that monitor the power consumption of the reactor can be used to control the substrate feeding rate, which diminishes the overall process power consumption while retaining similar productivity and yield [6]. Such control strategies can be used to reduce the process overall cost.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights

et al. 2020

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Second generation ethanol faces challenges before profitable implementation. Biomass hydrolysis is one of the bottlenecks, especially when this process occurs at high solids loading and with enzymatic catalysts. Under this setting, kinetic modeling and reaction monitoring are hindered due to the conditions of the medium, while increasing the mixing power. An algorithm that addresses these challenges might improve the reactor performance. In this work, a soft sensor that is based on agitation power measurements that uses an Artificial Neural Network (ANN) as an internal model is proposed in order to predict free carbohydrates concentrations. The developed soft sensor is used in a Moving Horizon Estimator (MHE) algorithm to improve the prediction of state variables during biomass hydrolysis. The algorithm is developed and used for batch and fed-batch hydrolysis experimental runs. An alteration of the classical MHE is proposed for improving prediction, using a novel fuzzy rule to alter the filter weights online. This alteration improved the prediction when compared to the original MHE in both training data sets (tracking error decreased 13%) and in test data sets, where the error reduction obtained is 44%.

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“…[5][6][7][8][9][10] The advantages of mechanical mixing are its flexibility and simplicity of operation, good control over parameters, and the wealth of experience associated with this unit operation. Biomass feeding methods, on the other hand, have the potential to manage the viscosity in the system, 5,6,11 and the two systems combined have been shown to enhance the overall conversion of sugars. 12 Our understanding of the interconnections among the slurry rheology, feeding strategies, and reactor design could be further advanced by determination of the flow field developed during mixing.…”

Section: Introductionmentioning

confidence: 99%

Enhancing biomass hydrolysis for biofuel production through hydrodynamic modeling and reactor design

Gaona

Lawryshyn

Saville

2019

Energy Science & Engineering

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A computational fluid dynamics model was developed to represent high‐solids enzymatic hydrolysis. This model accounted for the transient and multiphase (solids‐slurry) nature of the high‐solids enzymatic hydrolysis process. The model investigated the effect of slurry viscosity, rotational speed, and two impeller configurations on the distribution of insoluble solids. Initial CFD results identified segregation of the velocity contours for the non‐Newtonian slurry, which could potentially affect the reactor performance. The multiphase, transient CFD simulations showed that the first impeller configuration delayed the distribution of solids, and compartmentalized mixing in the reactor. The second impeller configuration, meanwhile, improved solids mixing and hydrolysis, while using lower rotational speeds (and thus, energy). The second impeller configuration also expanded the size of the pseudo‐cavern between impellers, which is critical for better dispersion of the solids. The CFD trends of the second impeller configuration were experimentally verified by conducting fed‐batch, high‐solids enzymatic hydrolysis trials with pretreated lignocellulose. The experimental results showed that the second impeller configuration provided better mixing of the non‐Newtonian slurry and enhanced solids‐enzyme interactions, leading to improved glucan‐to‐glucose conversion. This work illustrates that a transient multiphase CFD model can provide valuable insights into the design and optimization of high‐solids enzymatic hydrolysis reactors. The CFD model has identified pathways to improve the distribution of solids while reducing the energy needed for mixing. The CFD model can also guide experimental and design work to scale up these reactors from the laboratory to pilot and commercial scale.

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Controlled feeding of lignocellulosic substrate enhances the performance of fed-batch enzymatic hydrolysis in a stirred tank reactor

Cited by 26 publications

References 25 publications

A discretized model for enzymatic hydrolysis of cellulose in a fed-batch process

A discretized model for enzymatic hydrolysis of cellulose in a fed-batch process

Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights

Enhancing biomass hydrolysis for biofuel production through hydrodynamic modeling and reactor design

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