Modeling the design and operational mode of a continuous membrane reactor for enzymatic lignin modification

Accepted Manuscript Title: Modeling the design and operational mode of a continuous membrane reactor for enzymatic lignin modification Author: Nadine Busse Matthias Kraume Peter Czermak PII: DOI: Reference:

S1369-703X(17)30101-8 http://dx.doi.org/doi:10.1016/j.bej.2017.04.007 BEJ 6690

To appear in:

Biochemical Engineering Journal

Received date: Revised date: Accepted date:

25-10-2016 28-3-2017 17-4-2017

Please cite this article as: N. Busse, M. Kraume, P. Czermak, Modeling the design and operational mode of a continuous membrane reactor for enzymatic lignin modification, Biochemical Engineering Journal (2017), http://dx.doi.org/10.1016/j.bej.2017.04.007 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Modeling the design and operational mode of a continuous membrane reactor for enzymatic lignin modification

Institute of Bioprocess Engineering and Pharmaceutical Technology, University of Applied Sciences Mittelhessen, Giessen,

Germany b

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a

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Nadine Bussea, Matthias Kraumeb, Peter Czermaka,c,d,*

Chair of Chemical and Process Engineering, Technische Universität Berlin, Berlin, Germany

c

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Department of Chemical Engineering, Kansas State University, Manhattan KS, USA

d

Fraunhofer Institute of Molecular Biology and Applied Ecology (IME), Fraunhofer project group “Bioresources”, Giessen,

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Germany

*Corresponding author at: Institute of Bioprocess Engineering and Pharmaceutical Technology, University of Applied

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Email address: [email protected]

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Sciences Mittelhessen, Wiesenstrasse 14, 35390 Giessen, Germany.

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Abstract

The modification of lignin-derived compounds such as technical lignins, which are highly aromatic and therefore valuable as renewable feedstocks for the biobased product industry, is still a

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challenging and multidisciplinary task. An enzyme membrane reactor system (EMRS) featuring a continuous stirred-tank reactor and an external ceramic crossflow ultrafiltration membrane is a promising configuration, particularly when combined with ligninolytic heme peroxidases (PODs) as biocatalysts, such as the new versatile peroxidases (VP) described herein. However, time-dependent irreversible enzyme inactivation caused by the co-substrate H2O2 and the fouling of the filtration membrane are limiting factors. To facilitate rational bioprocess development and reactor design, we present an overall modeling concept for a continuous operating mode that addresses both limitations. When including H2O2-related VP inactivation dynamic model analyses showed that two

Page 1 of 33

H2O2 molecules were required to convert one molecule of the reducing substrate (here, adlerol). In this context, an (initial) enzyme inactivation rate ki0 caused by factors other than H2O2 (e.g. the used buffer system) was introduced. Because oxygen utilization is characteristic of normal POD actions, the continuous online measurement of dissolved oxygen concentration was useful to monitor

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enzyme inactivation and therefore an excess of H2O2.

Highlights

As well as H2O2 another enzyme inactivation rate (ki0 = 0.013 min-1) was introduced



Two H2O2 molecules are required for the conversion of one substrate molecule



The generation of O2 was prevalent when ~40% of the enzyme was inactivated



Continuous enzyme addition was able to maintain an appropriate H2O2 level



An optimal hydraulic retention time of 2 h was suitable for continuous operation

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

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Keywords: Lignin modification, Versatile peroxidase, Reaction kinetics, Irreversible inactivation, Enzyme membrane reactor, Mathematic modeling

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1 Introduction

The pulp and paper industry produces large quantities of technical/industrial lignins such as ligninsulfonates (LSs) and kraft lignins as waste products (> 50 million t/a [1]) [1-4]. Biorefineries that convert lignocellulosic biomass into second-generation biofuels and/or other carbohydrate-derived products also produce waste lignins [5, 6]. The modification and valorization of renewable waste lignins to produce new biobased products, particularly aromatic monomers, could open up promising new markets [4, 7] while ensuring the lasting profitability of both industries [8]. However, the unique and complex properties of native lignin generate inconsistent feedstocks and product streams, so the Page 2 of 33

integration of delignification into broader industrial processes remains a challenging and multidisciplinary task [4, 7, 9, 10]. Among the many lignin modification strategies under consideration, the use of ligninolytic heme peroxidases (PODs) is promising, particularly the recently-characterized versatile peroxidases (VPs,

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EC 1.11.1.16) from Basidiomycetes white-rot fungi [7, 11-18]. However, VPs and the other PODs are susceptible to inhibition by their co-substrate H2O2 at pH values that support maximum oxidative

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reactivity [11, 12], and it is also difficult to produce sufficient amounts of active enzyme [14, 19]. A

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further challenge is the complex reaction mechanism, which is not yet fully understood but appears strongly dependent on: (i) reaction and operating conditions (e.g. pH, H2O2 levels); (ii) the reducing

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substrate; (iii) the enzyme type; and (iv) the (radical) fission products that are produced. Mechanism-based reactions with H2O2 are the most important hurdle because they produce an

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enzyme intermediate (the so-called compound III) that is either less active or even irreversibly inactivated (for more details refer to [11]). The exploitation of such enzymes requires a detailed

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understanding of the bioprocess, and the development of an appropriate bioprocess design is therefore as important as protein engineering strategies to optimize enzyme activity. In this context,

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enzyme membrane reactor systems (EMRSs) featuring a continuous stirred-tank reactor and an external ceramic crossflow ultrafiltration (CFUF) membrane as a separation unit are promising configurations for the following reasons:

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(i) they achieve homogeneous catalysis, allowing the use of free enzymes and thereby supporting complete biocatalyst retention and recycling; (ii) they allow the rapid separation of high-molecular-weight molecules from low-molecular-weight fission products and undesirable by-products [9, 20] thus limiting the polymerization reaction and preventing additional enzyme losses; (iii) they facilitate the simple replacement of fresh active biocatalysts according to enzyme inactivation rates (kd(obs)), thus maintaining the specified substrate conversion rates [9].

Page 3 of 33

Such reactor systems have been investigated for wastewater treatment using PODs or VPs and polymeric ultrafiltration (UF) membranes [20-23]. In contrast, we have focused on tubular ceramic membranes because they are more suitable for feed streams with a high fouling potential [24] and they achieve greater chemical, thermal and mechanical stability [25], and a longer membrane service

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life [26]. However, proteins and/or polydisperse technical lignins such as LSs are serious foulants for filtration membranes [9, 27-30]. The filtration performance (e.g. permeability, throughput and mass

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transfer) therefore deteriorate, affecting overall bioprocess parameters such as the hydraulic retention time (), H2O2 input and therefore the VP reaction kinetics and substrate conversion rates

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[9, 31].

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Rational bioprocess development and reactor design should include the modeling of both filtration performance and enzyme kinetics. However, due to the lack of relevant data, extensive preliminary

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work was required to investigate the H2O2-dependent reaction/inactivation kinetics of a recently isolated crude VP (from Bjerkandera adusta) as a model enzyme [11] decoupled from CFUF

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experiments focused primarily on the characterization of a 5-kDa tubular ZrO2 membrane (mono-channel) when filtering complex protein–ligninsulfonate mixtures [9, 31]. Here we build on

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our earlier investigations [9, 11, 31] and present a new concept for modeling and designing an EMRS for the VP-catalyzed modification of lignin-related substrates such as LSs. The resulting mathematical model is based on the VP-catalyzed degradation of the representative lignin model compound

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adlerol, focusing on the cleavage of -O-4 bonds, the most frequent linkage in the native lignin biopolymer, including enzyme inactivation kinetics (for more information refer to [11]) as well as the main filtration parameters (retention coefficient Ri [9] and optimum permeate flux Jopt [31]). Accordingly, initial fed-batch experiments were carried out to determine appropriate initial concentrations of the reducing substrate adlerol and H2O2, allowing the dynamic analysis of the kinetics [11] with and without the continuous addition of VP. Subsequent in silico investigations addressed the transient states and productivities of a continuously-operating EMRS. So that real substrates such as LSs can be included in future experiments, initial process simulations were also

Page 4 of 33

demonstrated, assuming constant enzyme kinetics similar to the oxidative degradation of adlerol. Furthermore, and based on the catalase-like activity of PODs and previous publications [11, 20, 21, 32, 33], online measurements of dissolved oxygen (DO) were considered, using optical sensors as a promising process monitoring/control system. The standardization of appropriate process analytics is

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also challenging, not at least because of the complexity of substrates such as LSs.

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2 Materials and methods 2.1. Preparation of the enzyme and other reagents

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For adlerol degradation, all reagents were prepared as previously described [11]. The enzyme used in this study was a lyophilized crude VP from B. adusta with a molecular weight of 43 kDa (Jena

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Bioscience GmbH, Germany) and a Reinheitszahl value of ~0.3 [11]. Based on the Reinheitszahl value of 3.5 reported for a purified B. adusta VP [34], we assumed that the lyophilized product contained

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8.6% pure enzyme when calculating the enzyme/VP concentration c(E).

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2.2 Parameter screening without the continuous addition of VP The enzymatic degradation of adlerol was carried out under aerobic conditions in a 100-mL beaker containing 50 mL 0.1 M sodium tartrate buffer (pH 4.0), an initial lyophilized VP concentration c(EL)0

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of ~0.3 mg mL-1 (equivalent to a c(E)0 of ~0.6 µM) and initial adlerol concentrations c(S2)0 of 0.5, 1, 2.5 or 5 mM. H2O2 was fed continuously (FS1in = 0.060 mL min-1) with addition rates (c(S1)feed) of 5, 10, 15 and 20 µM min-1 using a high-precision IPC eight-channel tubing pump equipped with appropriate two-stop PharMed Ismaprene tubing and standard steel tube connectors (ISMATEC, Germany). Appropriate H2O2 stock solutions (4.17–16.67 mM) were freshly prepared in 0.1 M sodium tartrate buffer (pH 4.0) in cleaned amber glass bottles. The reaction mixture was continuously mixed at a controlled temperature T of 301°C and a stirrer speed of n = 375 rpm using a magnetic stirrer (MR Hei-Standard, Heidolph Instruments GmbH & Co. KG, Germany).

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Before adding the VP (note, only one certain amount was initially added), the adlerol-buffer solution was warmed to 301°C. The reaction was initiated by starting the H2O2 feed 1 min after the VP was added, and samples were taken over a time period of t = 60 min. The enzymatic activity was measured every 5 min using the ABTS assay [11] with an initial H2O2 concentration of 100 µM and an

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extinction coefficient ε420nm of 36.0 mM-1 cm-1 (based on [35]). In order to evaluate the formation of products, particularly the major fission product veratraldehyde (VAld) [11] and guaiacol, 200-µL

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samples were taken at 1-min (for 0 min ≤ t ≤ 10 min), 2-min (for 10 min < t ≤ 30 min) and 5-min (for t > 30 min) intervals, and immediately inactivated by adding 50 µL 3 M H2SO4. At the end of each

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experiment, 200 µL of each inactivated sample was transferred to a HELLMA quartz glass microplate

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and the absorption was measured at λ = 310 nm for c(VAld) and 456 nm for c(guaiacol), with an extinction coefficient ε310nm of 9300 mM-1 cm-1 [36] and ε456nm of 0.0121 µM-1 cm-1 [37], using a BioTek

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Synergy HT microplate reader. The resulting absorption values were normalized by subtracting the absorption measured at time t = 0 min. The pH was measured before each sample was taken using a

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the experimental period.

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calibrated SG23 pH meter (Mettler Toledo, Germany), resulting in a constant pH of 4.0 throughout

2.3 Experimental setup for dynamic model analysis Fed-batch experiments were carried out as described above, but in a jacketed mini-reactor system

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(working volume VR = 100 mL) custom built by Gerber Feinmechanik GmbH (Germany) as shown in Fig. 1. The stirrer speed was set to n = 400 rpm. The bioreactor was constructed from PS and PEEK and was equipped with a PTFE propeller and an IKA RW16 basic overhead stirrer (IKA-Werke GmbH & Co. KG, Germany), a stainless steel micro-sparger for aeration, a Lauda ECO RE 410 external temperature control unit (Lauda Dr. R. Wobser GmbH & Co. KG, Germany), a pH sensor and a standard PT100 sensor. The dissolved oxygen (DO) was monitored online using a planar oxygen PSt3 mini-sensor and a PC-controlled oxy-4 mini-fiber-optic oxygen meter from (PreSens, Germany, with

Page 6 of 33

OXY 4v2_11 software). The pH was checked as described above resulting in a constant pH of 4.0

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throughout the experimental period.

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Fig. 1. Schematic illustration of the fed-batch reactor system.

Two test scenarios were set up for dynamic analysis. The first test series was carried out without a

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constant enzyme feed. The required amounts of adlerol and buffer were first warmed to 301°C while sparging air up to a dissolved oxygen concentration of 100% (~240 µM) air saturation, then the air supply was disconnected before adding the enzyme. The reaction was initiated by introducing a

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H2O2 stock solution of ~8.3 mM at a volumetric flow rate of FS1in = 0.120 mL min-1, resulting in a feed rate of ~10 µM min-1. Samples were taken and analyzed as described above. The concentration of the H2O2 stock solution was checked before and after each experiment using the photometric analysis method summarized below. In the second test series, the VP was also fed continuously (FEin = 0.066 mL min-1, therefore FS1in was also set to 0.066 mL min-1, resulting in an overall input fed stream of Fin = 0.130 mL min-1) based on the maximum observed VP inactivation rate in the first test scenario. Samples were taken and analyzed as described above, but due to the accumulation of protein in the reactor system all Page 7 of 33

absorption measurements were corrected before calculating any concentrations. A calibration curve was established at 310 and 456 nm to approximate the enzyme-dependent increase in absorbance. The H2O2 concentration in the reaction mixture was randomly measured using the photometric

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analysis method below.

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2.4 Photometric analysis of H2O2 by peroxo-titanium acid

The concentrations of the H2O2 stock solutions were determined using a Dulcotest DT3 B H2O2

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photometer (ProMinent Dosiertechnik, Germany) with a relative error of 7% (when user

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calibrated). To estimate the H2O2 concentrations in the reaction mixture, the same measurement principle was applied at the micro-scale (HELLMA quartz glass microplate). In this case, 10 µL of the

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H2O2 reagent from ProMinent was added to a 190-µL sample (1:1.25 pre-diluted with 25% H2SO4), before the absorption was measured at 420 nm using the BioTek Synergy HT microplate reader. A

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second 190-µL sample was mixed with 10 µL distilled water instead of 10 µL H2O2 as a blank. The H2O2 concentration was calculated based on two independent calibration curves (concentration

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range: 5.9–250 µM). Based on experience, this method is strongly dependent on the buffer system

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and case-by-case user calibration is recommended.

3 Results and discussion

3.1 Parameter screening: Impact of the initial substrate concentration and H2O2 input Figs 2 and 3 show the impact of the initial adlerol concentration c(S2)0 and the H2O2 feed rate c(S1)feed on the substrate turnover X (Eq. (1)) and the turnover number TN. The latter represents the µmoles VAld produced per gram crude enzyme/lyophilisate c(EL) and reaction period t (Eq. (2)). The objective of this screening was to obtain an initial estimate of an appropriate H2O2 feed rate and the initial adlerol concentration resulting in both, high X and TN values. An initial adlerol concentration of

Page 8 of 33

2.5 mM along with a H2O2 feed rate of ~10 µM min-1 was chosen as a good compromise between X and TNor reaction rate and enzyme stability, and was therefore investigated in more detail as described below. H2O2 feed rates of ~5 µM min-1 were less suitable, resulting in low values of X and TN (Figs 2 and 3).



c  V A ld

t

 100

ip t

0

cS2

0

t V R

c  E L V R t

(1)

(2)

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TN

cS2  cS2



cr

X

Fig. 2. The impact of the initial adlerol concentration c(S2) on the substrate turnover X and the turnover number TN under the following conditions: pH = 4.0, T = 301°C, c(EL)0 = 0.30.1 mg mL-1, c(S1)feed = 10 µM min-1, FS1in = 60 µL min-1, VR = 50 mL. Based on the measurements at c(S2)0 = 2.5 mM, a maximum error of 15% can be expected for both X and TN.

Page 9 of 33

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Fig. 3. The impact of the H2O2 feed rate c(S1)feed on the substrate turnover X and the turnover number

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TN under the following conditions: pH = 4.0, T = 30°C, c(EL)0 = 0.30.1 mg mL-1, c(S2)0 = 2.5 mM

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3.2 Dynamic model analysis

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(adlerol), FS1in = 60 µL min-1, VR = 50 mL. The expected maximum error is 15%.

To verify the dynamic model, the following assumptions were made: (i)

ideal mixing

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(ii) isothermal conditions

(iii) a constant reaction volume VR (iv) no product input (e.g. VAld) (v) the ping-pong mechanism (Eq. (7)) from previous studies [11] is valid (vi) enzyme inactivation follows a time-dependent irreversible inactivation mechanism [11] (refer to Eq. (9), first term on the right hand side) (vii) no product inhibition (viii) the production of H2O can be ignored

Page 10 of 33

(ix) only H2O2 and adlerol serve as reducing substrates (x)

non-enzymatic reactions for final product formations are not rate limiting (for detailed information refer to [11])

(xi) catalase-like activities are negligible

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(xii) additives or impurities of the crude VP lyophilizate do not affect the reaction mechanism. The best predictions of the oxidative degradation of adlerol were achieved using the ordinary

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differential system (ODE) or initial value problem (IVP) in Eqs (3-6), including Eqs (7-9), which were

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optimized for programing (by MATLB R2014b) while minimizing the error function in Eq. (10):

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in  dc  S 1  F S1  c S1  2r  in dt VR 

 c S  c S1 0   1  t0

0

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 d c  V A ld   r  dt   c  V A ld   c  V A ld t0 

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 dc  S 2    r  dt  c  S   cS2 2 t0 0 

t

with

k cat c  E

r  K

k cat 

S1 m

c S2  K t

t c  S 1 t c  S 2 t

S2 m

c S1  c S1 c S2  t

t

v m ax cE

app

ki K

app i

(5)

(7) t

(8)

0

k d obs 

(4)

(6)

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 dc  E    k d obs c  E   dt c  E   c  E 0 t0 

(3)



1 

c S1

t

cS2  t S2   c  S 1 t Km 

 ki

0

(9)

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where r is the reaction rate (µM min-1), kcat is the catalytic reaction constant (min-1) and kd(obs) is the observed enzyme inactivation rate (min-1). The kinetic parameters vmax, KmS1, KmS2, kiapp, Kiapp and ki0 denote the maximum reaction velocity (µM min-1), the dissociation constant (µM) of H2O2 (S1) and

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adlerol (S2), the apparent maximal rate of enzyme inactivation (min-1), the apparent inhibitor (here H2O2) concentration for achieving the half-maximal rate of inactivation (µM) and the rate of

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inactivation (min-1) caused by factors apart from H2O2, respectively. The index t is the time (min). FS1in

i

 j1



i,j_ s im

 c C i 

i,j_ e x p



2

(10)

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i1

c C i 

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M

N

error 

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and VR are the incoming volumetric flow rate of H2O2 (mL min-1) and the reaction volume (mL).

where N, Mi, c(Ci)i,j_sim and c(Ci)i,j_exp are the number of data sets, the number of observations in the i-th

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data set, and the simulated and experimental concentrations of compound i.

Based on Eqs (3-5), the overall reaction rate followed Eq. (11), with the anticipation of additional H2O2 consumption as previously assumed for horseradish peroxidase-catalyzed reactions [38].

1 dc  S 1 

1 dc  S 2 

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r  

2

dt

 

1

dt



1 d c  V A ld

1



(11)

dt

Furthermore, an (initial) enzyme inactivation rate ki0 caused by factors other than H2O2 (e.g. the buffer system) was introduced (Eq. (9)). As a result, a KmS2 value of ~2506 µM was required for appropriate modeling of the overall enzymatic reaction (Table 1, Fig. 4 C, D), which was confirmed by the previous parameter screening (Figs 4 A, B, E, F and 5) and experiments in which the VP was also added continuously (Fig. 6). In the latter case, Eq. (6) was extended by the convective term

Page 12 of 33

FEin/VR·c(E)in, while setting ki0 to 0.013 min-1 and taking into account the fact that products like guaiacol (~5%) and ketones (~15%) are produced in addition to VAld (~80%) (for more detailed information regarding product formation refer to [11]) and that the kinetic parameters (Table 1, first row) remain constant. When H2O2 alone was fed, minimal amounts of guaiacol were detected and

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VAld production of 100% was therefore assumed. By including the data from parameter screenings (Figs 4 A, B, E, F and 5), the mathematical model

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outlined was found to be valid over a broad concentration range of the reducing substrate, at least

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when H2O2 was fed at a rate of 10–20 µM min-1. The overall model (Eqs 3-5, 11) is based on our previous work demonstrating that the used VP follows a time-dependent irreversible inactivation

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mechanism via the less active enzyme intermediate compound III. That irreversible inactivation is related to reactions with H2O2. (for more details refer to Fig. 19 in [11] and [45]). As initially

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introduced, the reaction mechanism is very complex and strongly dependent on the reaction and operating conditions. Reactions with superoxides as well as non-enzymatic radical reactions

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consuming or producing H2O2 and O2 amongst others must also be taken into account (for more details refer to Fig. 19 in [11] and [45]). When considering the normal (without enzyme inactivation)

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aerobic POD reaction cycle/mechanism one molecule H2O2 will be required for the conversion of two substrate molecules. At anaerobic conditions, one H2O2 molecule was required for conversion of one

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substrate molecule [39].

Table 1 Kinetic parameters based on dynamic analysis without the addition of enzyme. c(S1)feed -1 µM min

c(S2)0 µM

c(EL)0 -1 mg mL

From batch experiments [11]

Kinetic parameters vmax -1 µM min

S1 Km

a

16.4

12.2

S2

app

Km µM

µM a

a

418

b

app

ki -1 min

Ki µM

-

b

0

ki -1 min -

b

-

-

2506

0.28

188

-

10

500

0.4

19.2

15.7

2597

0.26

204

0.013

10

2500

0.3

11.8

15.8

2532

0.27

189

0.013

10

5000

0.2

11.3

15.9

2799

0.28

194

0.021

20

2500

0.3

15.2

17.2

2449

0.27

203

0.010

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b

from steady-state kinetics, from inactivation studies (for more detailed information refer to [11])

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cr

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a

Fig. 4. Fed-batch process without continuous VP addition at different initial adlerol concentrations c(S2)0 and a constant H2O2 feed rate c(S1)feed of 10 µM min-1. Experimental and simulated concentration-time courses of (A), (C), (E) VAld and (B), (D), (F) the corresponding active enzyme

Page 14 of 33

concentration c(E). The simulated data were obtained as described in the text using the kinetic parameters listed in Table 1. (A), (B), (E), (F): Data set from parameter screening experiments (n = 1) in the 50-mL fed-batch process. (C), (D): Data set from fed-batch experiments (n = 6) in the 100-mL bioreactor. Data from the parameter screening experiments showed similar results and are therefore

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omitted.

Fig. 5. Fed-batch process without continuous VP addition at an initial adlerol concentration c(S2)0 of

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2500 µM and a constant H2O2 feed rate c(S1)feed of 20 µM min-1. Experimental and simulated concentration-time courses of (A) VAld and (B) the corresponding active enzyme concentration c(E). The simulated data were obtained as described in the text using the kinetic parameters listed in

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Table 1. The data are from parameter screening experiments (n = 1) in the 50-mL fed-batch process.

Page 15 of 33

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Fig. 6. The 100-mL fed-batch process with continuous VP addition (n = 2). Experimental and

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simulated concentration-time courses of (A) VAld, (B) guaiacol, (C) H2O2 (S1) and (D) the active VP. The simulated data were obtained as described in the text using the following parameters: vmax = 12.2 µM min-1,

KmS1 = 16.4 µM,

KmS2 = 2506 µM

or

KmS2 = 418 µM,

kiapp = 0.28 min-1,

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Kiapp = 188 µM, ki0 = 0.013 min-1. Addition process conditions: VR = 100 mL, n = 400 rpm, pH = 4.0, T = 301°C, c(EL)0 = 0.3 mg mL-1, c(E)0 = 0.6 µM (~0.5 U mL-1), c(S1)0 = 0 mM, c(S2)0 = 2.5 mM (adlerol), FS1in = 0.066 mL min-1, c(S1)in = 11ˑ103 µM, c(S1)feed = 7.3 µM min-1, FEin = 0.066 mL min-1, c(E)in = 24 µM, c(E)feed = 1.6ˑ10-2 µM min-1.

When a constant VP feed was provided, the H2O2 level remained at a suitable concentration of ~10 µM (Fig. 6), whereas a considerable increase in H2O2 levels would be expected if only H2O2 were added (Figs 4 and 5) as simulated in Fig. 7. The accumulation of H2O2 is unfavorable given that the Page 16 of 33

addition of more enzyme increases the risk of undesirable enzyme losses and unwanted reactions, i.e. reactions via compound III (an enzyme intermediate with less catalytic activity), thus limiting the catalytic turnover (Figs 4 and 5). The development of appropriate feeding strategies (e.g. VP/enzyme and H2O2) and the design of a suitable bioprocess therefore require an adequate process monitoring

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ed

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cr

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system as discussed below.

Fig. 7. Theoretical H2O2 accumulation in the fed-batch reactor for the case studies in Figs 4 and 5.

Ac

3.3 Dissolved oxygen measurements as a promising online monitoring approach One promising approach for online monitoring is the measurement of dissolved oxygen (DO) [20, 21], because O2 consumption indicates the normal catalytic cycle of POD or reducing substrates capable of bond cleavage (e.g. adlerol, or lignin-derived β-O-4 substructures in general) [33] and oxygen generation indicates POD inactivation triggered by an excess of H2O2 (for detailed information refer to [11]). Such effects are illustrated in Fig. 8, which shows the corresponding time-dependent DO courses of the experiments described above (Figs 4 C, D and 6), including a worst-case H2O2 overload for comparison. Interestingly, when VP was fed continuously, the DO achieved a steady state by the Page 17 of 33

end of the experiment, although sufficient concentrations of adlerol and VP were present, and H2O2 remained within a comfortable range (Fig. 6). Moreover, there was no substantial drop in the production rate (Fig. 6). These findings confirm previous observations suggesting (i) a high substrate turnover, (ii) a sufficient quantity of enzyme, and (iii) an appropriate H2O2 addition rate under

ip t

steady-state conditions [20, 21]. In contrast, the generation of O2 became dominant in fed-batch experiments without the continuous addition of VP (Figs 4 C, D and 6) once the VP activity was ≤ 60%

cr

(Fig. 9) and hence a certain H2O2/enzyme ratio was exceeded (Figs 4 and 7).

us

The measurement of DO using optical O2 sensors therefore achieves the sensitive, user-friendly and low-maintenance online monitoring of the instantaneous active enzyme/VP concentration in relation

an

to the amount of H2O2. However, the detailed stoichiometric analysis of DO is challenging [33] and further investigations are necessary for mathematical modeling and the development of efficient

Ac

ce pt

ed

M

process control or feedback control strategies as previously recommended [20].

Fig. 8. Time-dependent DO course illustrating the impact of a continuous VP addition (100-mL bioreactor) as well as a substantial excess of H2O2 (50-mL reaction vessel; here only H2O2 was fed) for comparison. The DO was measured every 30 s. Page 18 of 33

ip t cr us an

M

Fig. 9. The instantaneous active VP concentration c(E) in percentage terms as a function of the normalized DO concentration. The simulated data represent the simulated enzyme concentration

ce pt

ed

(Figs 4 and 5) in relation to the corresponding DO measurement point.

3.4 In silico investigations: Performance of a continuously-operating EMRS as a function of the hydraulic retention time 

Ac

The mass balance of a promising, isothermal EMRS (Fig. 10) is defined by Eq. (12) including the retention characteristics of the UF membrane. Taking the systems boundary (Fig. 10) into account, the EMRS behaves like a single continuous stirred-tank reactor, keeping both the VP and highmolecular-weight molecules (retention coefficient Ri = 1) in the reaction system similar to the fed-batch experiments above. To maintain a specific reaction rate and turnover, fresh active enzyme is added (Eq. (13)) according to the current expected maximum deactivation rate kd(obs)_max of 0.017 min-1 (based on Fig. 6). The adlerol concentration must also be maintained at a constant ~2.5 mM to ensure the continuous influence of the reducing substrate. To overcome variations in

Page 19 of 33

throughput Fout and permeability, e.g. caused by protein accumulation [9], the UF unit or the reactor system should operate under mass transfer-controlled conditions. The resulting limited/optimal

M

an

us

cr

ip t

permeate flux Jopt is assumed to be 4 L m-2 h-1 [31].

ed

Fig. 10. Schematic drawing of a promising EMRS configuration consisting of a baffled CSTR coupled to an external tubular UF module. F = volumetric flow rate, VR = reaction volume, c(Ci) = concentration

ce pt

of compound i, ri = reaction rate of compound i,  = hydraulic retention time, DO = dissolved oxygen;



Ac

Jopt = optimal permeate (filtrate) flux, Am = active membrane area.

d c  C i V R dt



 Fi c C i  in

in

 Fi

out

 c  C i   1  R i    ri d V R out  

(12)

where c(Ci)in and c(Ci)out are the concentration of compound i (µM) in the input Fiin and output Fiout streams (mL min-1). VR, Ri and ri denote the reaction volume (mL), the retention coefficient of compound i and the reaction rate of compound i (µM min-1).

Page 20 of 33

cE

 in

 k d obs _m ax c  E

0

τ

(13)

Furthermore, the following assumptions were made: (i)

ideal mixing

(iii) the reaction/inactivation mechanisms as outlined above are still valid

cr

(iv) no product input

ip t

(ii) VR remains constant and therefore Fin = Fout = F

us

(v) the retention coefficient for H2O2 (S1) is 0, whereas for adlerol (S2) and fission products it is 0.3 (based on [9])

(vii) the generation of H2O can be ignored

M

(viii) no inhibitory effects by any fission product

an

(vi) retention coefficients and kinetic parameters remain constant

(ix) adlerol is the only reducing substrate, other than H2O2 no protein-protein interactions

ed

(x)

(xi) non-enzymatic reactions are not rate limiting and are therefore negligible

ce pt

(xii) the degradation of the reducing substrate will be only caused by the enzyme (xiii) catalase-like activities are still negligible (xiv) additives or impurities of the crude VP lyophilizate do not affect the reaction mechanism.

Ac

This results in the set of IVPs as described in Eqs (14-17).

 dc  S 1  c S1  c S1 in   2r  dt τ  c S  c S1 0   1  t0

(14)

 dc S c  S 2    c  S 2   1  R S2    2 in t      r  dt τ  c S  c S2 0   2 t0

(15)

Page 21 of 33

 dc  E  c  E  in   k d obs c  E  τ  dt c  E   c  E 0 t0 



(16)

 r

(17)

ip t

 dc  P  c  P t 1  RP    τ  dt c  P   c  P 0 t0 

t

where the hydraulic retention time  is VR

cr

τ 

(18)

us

F

an

and r and kd(obs) are defined in Eqs (7) and (9), respectively. c(P) is the concentration of the product. At steady state, Eq. (15) can be rewritten as Eq. (19), whereas Eqs (20) and (21) can be used to



in

  c  S 2   1  R S2   t   τ

r τ c S2

cP τ



c S2

in

 c S2



(19)

τ

(20)

(21)

Ac

PE M R S 

in



ed

X

c S2

ce pt

r 

M

calculate the corresponding substrate turnover X and the productivity PEMRS.

where c(S2), r and c(P) are the adlerol concentration, reaction rate and product concentration at steady state, whereas r is equivalent to r in Eq. (19). Based on Figs 11,  = 2 h offers both constant substrate conditions and an optimal productivity of PEMRS  281 µM h-1 (under steady-state conditions: 0.95·c(P) at t = 26 h) and X  19%. Hydraulic retention times > 2 h are ineffective. When investigating the VP-catalyzed, Mn2+-mediated removal of Orange II from wastewaters, a similar hydraulic retention time of 1.5 h was observed [20, 21]. However, for  = 50 min and at a H2O2 feed rate of 25 µM min-1, steady-state productivities of 800– Page 22 of 33

1100 µM h-1 were previously observed for Mn3+-complexes when the same VP concentration (~100 U L-1) was used in a similar EMRS for the Mn2+-mediated removal of pollutants such as

Ac

ce pt

ed

M

an

us

cr

ip t

nonylphenol from wastewaters [22, 23].

Page 23 of 33

Fig. 11. (A)-(D) Simulation of the transient states/performance of a continuously-operating EMRS for the VP-catalyzed degradation of adlerol (S2) using the initial value problem defined in Eqs (14-17). Parameters: c(S1) = 0 µM, c(S2) = 2500 µM, c(E)0 = 0.6 µM (0.3 g L-1 lyophilisate), c(P)0 = 0 µM, c(S1)feed = 7 µM min-1, c(S2)feed = 18 µM min-1, c(E)feed = 0.01 µM min-1. (E) The adlerol turnover and (F) EMRS

cr

ip t

productivity under steady-state conditions.

us

If real substrates like LSs are included in future experiments, a substrate retention coefficient of 0.85 must be taken into account [9]. For LS concentrations of ~50 g L-1 or ~1.3 mM (internal worst case,

an

for detailed information refer to [9]) and if the previous reaction and inactivation kinetics (including the kinetic parameters) are still valid, transient states similar to Fig. 12 (A-D) may be observed. The

M

corresponding substrate turnover X and the EMRS productivity PEMRS are shown in Fig. 12 (E-F) as a function of . Again, a hydraulic retention time of  = 2 h is optimal to maintain a constant

ed

substrate/LS level but the H2O2 and LS feeds must be maintained at ~5 µM min-1 and ~4 µM min-1,

~200 µM h-1.

ce pt

respectively. This increases the substrate turnover to ~61%, but reduces the reactor productivity to

By substituting Fout in Fig. 10 with F in Eq. (18) the required filter area Am (m2) can be calculated for a certain reaction volume using Eq. (22), allowing the determination of the corresponding volumetric

Am 

Ac

flow rate F.

VR

τ J opt

(22)

For  = 2 h and a small laboratory scale of VR = 0.5 L, Am must be ~0.0625 m2 and a volumetric flow rate F of at least ~4.1 mL min-1 is required. The membrane surface area and the volumetric flow rate should be increased to 3.75–12.5 m2 and 15–50 L h-1 for larger scales of 30–100 L reaction volume,

Page 24 of 33

which may become economically feasible for sufficiently high productivity (PEMRS) e.g. by including a more efficient VP (via protein engineering) and an optimized bioprocess. The critical or maximum concentration of protein, and a required reactor shut-down or maximum process time, can be estimated as previously described [31]. The accumulation of the total protein

ip t

concentration can be calculated according to the IVP in Eq. (22) resulting in a linear protein increase

cr

(here, c(EL) = 25 g L-1 at t = 80 h).

(22)

an

us

 dc  E L  cEL in   dt τ  c E  cEL 0   L  t0

Ac

ce pt

ed

(simulating the most challenging scenario).

M

where c(EL) is the concentration of the VP lyophilisate (g L-1) assuming a protein content of 100%

Page 25 of 33

ip t cr us an M ed ce pt Ac Fig. 12. (A)-(D) Simulation of the transient states of a continuously-operated EMRS for VP-catalyzed LS degradation using the initial value problem defined in Eqs (14-17). Parameters: c(S1) = 0 µM, c(S2) = 1300 µM, c(E)0 = 0.6 µM (0.3 g L-1 lyophilisate), c(P)0 = 0 µM, c(S1)feed = 5 µM min-1, c(S2)feed = 4 µM min-1, c(E)feed = 0.01 µM min-1. (E) Estimated substrate turnover and (F) EMRS productivity under steady-state conditions when a real substrate like LS is included in future experiments.

Page 26 of 33

4 Conclusions The VP-catalyzed modification of lignin-derived compounds such as technical lignins (e.g. LSs) in a membrane-based reactor system is a promising approach. An EMRS equipped with a continuous

ip t

stirred-tank reactor and external ultrafiltration unit as discussed herein is also useful for the treatment of wastewater using the same class of enzymes. However, the major drawbacks of these

cr

systems include membrane fouling and the fact that PODs are susceptible to their co-substrate H2O2

us

at an acidic pH. Here, a mathematical model was derived that addresses both membrane fouling and enzyme inactivation by H2O2. The model is based on the cleavage of the most frequent linkage in the

an

native lignin biopolymer and may be used in the future to simulate the dynamic behavior of fed-batch and continuous processes for optimization and/or bioprocess development and design.

M

The reactive term in the model is also useful to evaluate the efficiency of new or modified VPs or other PODs, e.g. so the kinetic parameters can be adapted to the reducing substrate and reaction

ed

system.

ce pt

Furthermore, the constant addition of enzyme helps to maintain a sufficient reaction rate r as well as a comfortable H2O2 concentration. In this context, DO measurements taken by optical O2 sensors provided sensitive and user-friendly process monitoring. An increase in DO indicated an unbalanced H2O2/enzyme ratio and/or a remaining enzyme activity of ≤ 60%. DO measurements will also be

Ac

beneficial when real substrates such as LSs are included, thus placing high demands on the process analytics. However, further stoichiometric analysis is required for the development of efficient process control and feeding strategies. This will require the introduction of an appropriate online H2O2 sensor and the prevention of unnecessarily high enzyme losses. PODs are nature’s most efficient lignin-degrading enzymes [40] but exposure to low H2O2 concentrations of ~1 µM [41, 42] does not facilitate sufficient substrate conversion rates for industrial processes. Higher H2O2 concentrations are therefore needed, which may cause time-dependent irreversible enzyme inactivation as shown herein. Currently, the most suitable commercial online sensor for H2O2 is

Page 27 of 33

probably the amperometric PEROX sensor from ProMinent Dosiertechnik, Germany. The first promising reports of sensor-controlled peroxidase-catalyzed reactions were published in the late 1990s [43, 44], but proof-of-concept has yet to be demonstrated and we will publish more data to address this in the near future.

ip t

Future investigations must also consider:  further process optimization (e.g. feeding strategy, O2 level in the reactor system) including

cr

process analytics, monitoring and control (e.g. online H2O2 and DO measurements) maximizing

us

the amount of product generated per hour and per quantity of enzyme used;

 long-term studies with real substrates, thus requiring the determination of an appropriate LS

an

type (e.g. sulfonation, polydispersity and average molecular weight);

 evaluation of new and/or modified VPs with increased catalytic efficiency and H2O2/pH-related

M

stability [12];

ce pt

Acknowledgment

ed

 re-evaluation of the economic efficiency of EMRSs, as discussed herein.

The authors would like to thank the Hessen State Ministry of Higher Education, Research and the Arts

Ac

for financial support within the Hessen initiative for scientific and economic excellence (LOEWE).

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