JBA-06911; No of Pages 9 Biotechnology Advances xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

Biotechnology Advances journal homepage: www.elsevier.com/locate/biotechadv

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Research review paper

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Sunil A. Patil a,⁎, Sylvia Gildemyn a, Deepak Pant b, Karsten Zengler c,d, Bruce E. Logan e, Korneel Rabaey a,⁎

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Article history: Received 21 November 2014 Received in revised form 9 February 2015 Accepted 2 March 2015 Available online xxxx

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Keywords: Microbial electrochemical technologies Bioelectrochemical systems Microbial electrosynthesis Cathode Reactor parameters Process parameters Performance indicators

Laboratory of Microbial Ecology and Technology, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium Separation and Conversion Technologies, Flemish Institute for Technological Research (VITO), Boeretang 200, Mol 2400, Belgium Department of Bioengineering, University of California San Diego, La Jolla, CA, USA d The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Allé 6, Hørsholm 2970, Denmark e Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802, USA b

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Microbial electrosynthesis (MES) is a process that uses electricity as an energy source for driving the production of chemicals and fuels using microorganisms and CO2 or organics as carbon sources. The development of this highly interdisciplinary technology on the interface between biotechnology and electrochemistry requires knowledge and expertise in a variety of scientific and technical areas. The rational development and commercialization of MES can be achieved at a faster pace if the research data and findings are reported in appropriate and uniformly accepted ways. Here we provide a framework for reporting on MES research and propose several pivotal performance indicators to describe these processes. Linked to this study is an online tool to perform necessary calculations and identify data gaps. A key consideration is the calculation of effective energy expenditure per unit product in a manner enabling cross comparison of studies irrespective of reactor design. We anticipate that the information provided here on different aspects of MES ranging from reactor and process parameters to chemical, electrochemical, and microbial functionality indicators will assist researchers in data presentation and ease data interpretation. Furthermore, a discussion on secondary MES aspects such as downstream processing, process economics and life cycle analysis is included. © 2015 Published by Elsevier Inc.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Requirements in rational development of MES field: data presentation and essential performance indicators Reporting on MES experiments: reactor and process parameters . . . . . . . . . . . . . . . . . . . . . . . Key production parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Product titer and yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Production rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Coulombic efficiency and electron recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Energetic efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Microbial parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1. Start-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2. Growth yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.3. Turnover per cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Techno-economic evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1. Downstream processes or product recovery . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2. Techno-economic analysis (TEA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3. Life cycle analysis (LCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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A logical data representation framework for electricity-driven bioproduction processes☆,☆☆

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☆ SI-1 Supplementary information (SI) available. ☆☆ SI-2 Excel tool for the calculations of key production parameters of several microbial bioelectrochemical systems available. ⁎ Corresponding authors. Tel.: +32 9 264 5976; fax: +32 9 264 6248. E-mail addresses: [email protected] (S.A. Patil), [email protected] (K. Rabaey).

http://dx.doi.org/10.1016/j.biotechadv.2015.03.002 0734-9750/© 2015 Published by Elsevier Inc.

Please cite this article as: Patil SA, et al, A logical data representation framework for electricity-driven bioproduction processes, Biotechnol Adv (2015), http://dx.doi.org/10.1016/j.biotechadv.2015.03.002

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S.A. Patil et al. / Biotechnology Advances xxx (2015) xxx–xxx

Conflict of interest . . . . . . . . Acknowledgments . . . . . . . . Appendix A. Supplementary data References . . . . . . . . . . .

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Box 1 Definitions (adapted from Harnisch and Freguia, 2012).

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Electrocatalysis: An effect by virtue of a catalyst leading to an increase in the rate of an electrochemical reaction at a given potential. Bioelectrocatalysis: Electrocatalysis driven by biological entities such as microorganisms or enzymes. Microbial electrochemical technologies (METs): All technologies or concepts that exploit microorganisms for the bioelectrocatalysis of anodic and/or cathodic reactions in the electrochemical systems. The electrochemical devices that are used to explore such reactions are usually referred to as bioelectrochemical systems (BESs). Microbial fuel cell (MFC): A BES that exploits microorganisms to facilitate the conversion of chemical energy to electrical energy, generally but not exclusively from the microbially-driven oxidation of organic compounds at the anode and the oxygen-reduction reaction at the cathode. The oxygen-reduction reaction in this case can be either abiotic or microbially catalyzed. Microbial electrolysis cell (MEC): A BES that uses electrical energy and microorganisms for the (reductive) synthesis of chemical products at the cathode. The anodic reaction in this case can be either abiotic water splitting or microbial oxidation of organic matter. Microbial electrosynthesis (MES): A concept based on MEC principle for microbially catalyzed, electricity-driven synthesis of chemicals or fuels from CO2 or organic feedstocks in BESs. Note: The polarity of the anode and the cathode depends on the type and operation of the device (galvanic or electrolysis cell).

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1. Introduction

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The capability of microorganisms to exchange electrons directly or indirectly with electrodes and thus drive novel oxidative and reductive reactions at electrodes has led to the development of multiple microbial electrochemical technologies (METs) over the last decade (Logan and Rabaey, 2012). Attempts were made during the last decade for scaling-up of microbial fuel cells (MFCs) and microbial electrolysis cells (MECs) from lab-scale to pilot-scale (Logan, 2010), which paved the way for larger-scale applications for these systems. The practical application of these METs has taken considerable time but at present there are several larger-scale demonstrations that are operational for the production of methane (Cambrian Innovation Inc., 2013), for producing power in remote locations (Parry, 2013), for energyefficient wastewater treatment (www.emefcy.com), and for powering LEDs (light emitting diodes) with plant-MFCs (www.plant-e.com). MFCs have also been demonstrated for charging electronic devices such as mobile phones (Ieropoulos et al., 2013). In 2013, a pilot MEC demonstrated the feasibility of this technology for the on-site treatment and conversion of urine into ammonia and hydrogen (Rodriguez Arredondo et al., 2015). Other potential applications that have been envisaged for METs include bioremediation, energy efficient desalination, bioproduction, and biosensors (Patil et al., 2012). In recent years much attention has been focused on the production of other valuable products such as hydrogen (Cheng and Logan, 2007),

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methane (Cheng et al., 2009) caustic soda (Rabaey et al., 2010) and hydrogen peroxide (Rozendal et al., 2009) at the cathode of microbial electrolysis cells (MECs). While MEC-based processes require a modest amount of electrical energy, this is increasingly available from sustainable sources such as wind and solar, or possibly by using salinity gradient energy from natural or engineered systems (Hatzell et al., 2014). When MECs are used for autotrophic processes, the required CO2 is becoming increasingly available on the market and its capture into chemicals for many diverse reasons is desired. This creates an opportunity to use electricity as energy source for the fixation of CO2 into chemicals (Nevin et al., 2010). This approach of using electricity and microorganisms, in concert with fixing CO2 or transforming organic chemicals, is termed microbial electrosynthesis (MES) (Rabaey and Rozendal, 2010). Electrically steered fermentation can lead to better redox balancing and production of more complex or reduced products. Examples include the conversion of acetate to butyrate (Choi et al., 2012) or longer chain fatty acids (van Eerten-Jansen et al., 2013), fatty acids into alcohols (Sharma et al., 2013), glycerol to 1,3-propanediol (1,3-PDO) (Dennis et al., 2013), glycerol to ethanol (Speers et al., 2014), CO2 to butyrate (Ganigué et al., 2015) and the accumulation of polyhydroxyalkanoates (Srikanth et al., 2012). For detailed information on the recent progress in several other BESs, readers are directed to these review articles (Li et al., 2014; Mohan et al., 2014a,b; Wang and Ren, 2013). In comparison to other processes, MES offers novel opportunities for land-independent conversions of wind or solar power to commodity and fine chemicals in a carbon positive process (Lovley, 2011; Rabaey and Rozendal, 2010). Apart from the challenges associated with interdisciplinary research (see Fig. 1 for an overview of key aspects) and scale-up, inadequate reporting of data, poor quality of data representation, and universal acceptance of sufficient data inclusion can in part delay the technological realization of these processes (Logan and Rabaey, 2012; Sharma et al., 2014). In order to address these issues we provide here a framework for reporting on MES research and propose several important performance indicators to describe these processes.

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1.1. Requirements in rational development of MES field: data presentation 158 and essential performance indicators 159 All BESs are unique combinations of a volume-based technology (environmental and industrial biotechnology) with an intrinsically surface-based process (electrochemistry), and thus performance parameters must accommodate both fields (Fig. 2). MES differentiates itself from typical METs such as MFCs as chemical formation is the main product. This requires detailed information on product formation rate, concentration, and specificity. These parameters have a major impact on aspects such as downstream processing and overall process economics. Technical and microbiological hurdles exist towards maturing and developing MES for industrial applications (Logan and Rabaey, 2012) In addition to scientific and engineering breakthroughs, the rational development and commercialization of this technology can be achieved at a faster pace if research experiments and findings are reported in a way that allows cross-comparison of data. In order for this comparison to occur, the broad community needs to agree on what information needs to be reported.

Please cite this article as: Patil SA, et al, A logical data representation framework for electricity-driven bioproduction processes, Biotechnol Adv (2015), http://dx.doi.org/10.1016/j.biotechadv.2015.03.002

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S.A. Patil et al. / Biotechnology Advances xxx (2015) xxx–xxx

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Fig. 1. An overview of the key aspects of microbial electrosynthesis processes, spanning interdisciplinary approaches from various research fields. In the case of an electrosynthesis process at the cathode, the reaction at the anode could be simple, abiotic oxidation of water or microbial oxidation of complex organics such as wastewater. Operating the anode with microorganisms can help to minimize the anode overpotential in microbial electrosynthesis systems. A simplified overview of several other possible reactions at anode and cathode is provided in a recent review by Logan and Rabaey (2012).

2. Reporting on MES experiments: reactor and process parameters

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The most important experimental parameters for MES, categorized as reactor and process parameters, are summarized in Table 1. Further elaboration on these technical parameters is provided in the electronic supporting information (Sections S1 and S2 in SI-1 †), and in a recent review (Logan, 2012). The most important starting point for electricity-driven bioproduction processes is how electrical energy is added to the system. For the MES process, either the electrode potential (chronoamperometry) or the current (chronopotentiometry) must be set at a specific value with a potentiostat or power source. It is easy to control and monitor the anode and the cathode potentials (against the reference electrode) with the potentiostat. However, when voltage is set using a power supply system, the electrode potentials are dynamically changed according to the electrode kinetics in order to reach the equilibrium in electrochemical reactions. Hence, controlling the anode or cathode potential with a power supply is not easy. Furthermore, the set-voltage in the power supply õsystem may not be identical to the actual voltage observed between the anode and the cathode of the MEC. Therefore, when using a power source instead of a potentiostat, the individual electrode potentials need to be monitored using a reference electrode placed in the reactor and a multimeter or datalogger. It is important to consider that the location of the reference electrode, and also the distance of the reference electrode from the working or counter electrode, can also affect the accuracy of the set potentials (Zhang et al., 2014).

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For electricity-driven bioproduction, most often a set electrode potential is used as this allows direct electron transfer (e.g., from the cathode to microorganisms (Nevin et al., 2011; Zhang et al., 2013)) and it avoids perhaps undesired chemical reactions from occurring in the system that could lead to inactivation of the microorganisms. In the case of cathodic bioproduction, the potential is often set at a more positive value than the one that leads to high rates of abiotic H2 production (usually N −400 mV vs. standard hydrogen electrode (SHE); mostly depending upon the electrode materials). In addition to the choice of microorganisms, the selection of the potential value (positive or negative) is important since it can influence the conversions via MES route. For instance, standard electrode potential (E′0 vs. SHE) for HCO− 3 /acetate conversion process at pH 7 is −0.28 V, for acetate/ethanol process E′0 is − 0.39 V and for fumarate/succinate process it is 0.03 V. For more details on theoretical potential values for different processes we refer to the review by Rabaey and Rozendal (2010). The other strategy used for MES, based on fixing the current passing through the electrochemical cell, can enable a constant flow of electrons to the microorganisms (Dennis et al., 2013), but it can lead to substantial evolution of H2 gas. Lower energy inputs are generally required in using more positive set potentials, rather than set currents, as H2 production may require more negative cathode potentials (dependent on electrode or catalyst, and set current density). Indirect, H2 driven bioproduction can be of interest in some cases as it enables participation of planktonic cells to the production process, and aligns well with existing hydrogenotrophic processes (Angenent et al., 2004). Irrespective of

Please cite this article as: Patil SA, et al, A logical data representation framework for electricity-driven bioproduction processes, Biotechnol Adv (2015), http://dx.doi.org/10.1016/j.biotechadv.2015.03.002

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Fig. 2. Major performance indicators of microbial electrosynthesis processes.

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to facilitate the indirect electron transfer from electrodes to microor- 231 ganisms for enabling MES processes. The importance of reporting on 232 process variables (not related to production parameters) such as 233

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Table 1 Recommendations for reporting on minimal reactor and process parameters of electricity-driven bioproduction processes.

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Cathode potential (Ecat) Anode potential (Ean) Current density

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Gas flow and composition, if used Temperature Other electrochemical techniques Chemical reaction(s) Downstream, if any

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Materials, size (empty bed volume; m3), design (geometry), and distances between electrodes (m) Material type (with unique specifications), supplier, geometry, surface areaa per working volume (m2 m−3), porosity, and pre-treatment (if any) Material type, specifications, supplier, geometry, surface areaa per working volume (m2 m−3), porosity and pre-treatment (if any) Type, supplier, and potential value vs. standard hydrogen electrode (V); placement in system; distance from working electrode; indicate if ohmic compensation used. Material type (with unique specifications), supplier, surface area per volume (m2 m−3), and pre-treatment (if any) Composition (preferably molar concentrations; or g L−1), volume (L), pH, conductivity (mS cm−1), electron mediator (if used, and its concentration) same as above

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Potentiostatic: Chronoamperometry (CA) technique – fixed potential Galvanostatic: Chronopotentiometry (CP) technique – fixed current Other strategy if potentiostat is not used (e.g. dynamic control (Andersen et al., 2013)) Controls: current in abiotic reactor at set conditions, with control run for same period of time as treatment to examine electrode degradation or corrosion Batch: mixing rate (rpm; shear rate if known, s−1), recirculation rate (if used, provide flow rate and hydraulic retention time (HRT) data (d), duration of cycle or time between medium replacement (h)) Fed-batch or semi-batch: frequency and strategy of medium replenishment Continuous: flow rate, HRT (theoretical and actual based on tracer tests; d) Pre-enrichment experiments, if any and total experimental duration in all cases Statistics: number of reactors or repeated tests (average and standard deviation or standard errors for numbers); use of significant figures (numbers rounded appropriately based on given statistics) Potential value (V) set or resulting potential (V) at which the reduction reactions are occurring Potential value (V) set or resulting potential (V) at which the oxidation reactions are occurring Applied or produced depending on the electrochemical technique used; A m−2 with respect to at least the projected surface area of the cathode and/or A m−3 with respect to working volume of the catholyte/total reactor volume. It can also be reported with respect to the volume of the cathode. Both units should be given for one number, to make conversion factor clear. N2:CO2 (vol. %) OR CO2: Either to maintain anaerobic conditions OR as a continuous source of CO2 (in case of MES starting from CO2), L day−1 Monitoring of effluent gas in order to confirm the H2 production OR if the production is H2-based and other gases such as CH4 are produced Provide mean and standard deviation, condition (room, cabinet, or only reactor), and if set or variable (values needed for room temperature) Cyclic voltammetry (CV): potential window, scan rate, equilibrium time, time-points of CV recordings Linear sweep voltammetry (LSV): same as above (note that CV should be favored) Electrode and homogenous reactions in the electrolyte if any, with electron balances Extraction and purification protocol

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Description; suggestions for reporting on data (units, where applicable)

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the use of the above-mentioned strategies, electron carriers such as redox mediators (Choi et al., 2012; Song et al., 2011) or artificial cell membrane-spanning electrolytes (Bazan et al., 2013), can be used

Notes: When reporting on current densities, production profiles, carbon recovery, coulombic and energy efficiencies, voltammetry and other results use significant figures and/or tables, report standard deviations wherever applicable, and use standard units. Minor data and unit errors can create unrealistic expectations relative to current densities and product concentrations in subsequent studies. It is recommended that reproducible cycles of bioproduction be obtained over at least two-three cycles in duplicate reactors. Failure to demonstrate reproducibility means that the reactor is not sufficiently acclimatized for stable bioproduction. a Must include projected surface area, can additionally define BET (Brunauer–Emmett–Teller) surface area (for porous materials) or (bio)electrochemically active surface area (Sharma et al., 2014).

Please cite this article as: Patil SA, et al, A logical data representation framework for electricity-driven bioproduction processes, Biotechnol Adv (2015), http://dx.doi.org/10.1016/j.biotechadv.2015.03.002

S.A. Patil et al. / Biotechnology Advances xxx (2015) xxx–xxx

3.1. Product titer and yield

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Titer is critical to enable sufficient extraction of the product (Xiu and Zeng, 2008). It is the concentration of product(s), which can be either expressed as g L−1 or as mol L−1. This is a key parameter when purification costs are a major proportion of product costs. When substrates are a major proportion of product costs, the product yield, another production parameter, also needs to be reported. It is the amount of product(s) produced for a given amount of substrate (crude feedstock). A key consideration in bioproduction processes is the effect of end product(s) on the product titer. End product(s) in high concentration can result in toxicity for the microorganisms. For instance, product toxicity in case of fatty acids is due to their presence in the acid form, which can penetrate through the cell membrane and render it permeable to protons (Baronofsky et al., 1984). In order to avoid product inhibition, pH control above the pKa value of the fatty acid or (in situ) removal of the product is necessary. This removal can either be done continuously or at the end of a batch cycle, but consideration of titer emphasizes the need for efficient downstream processing as explained further below.

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3.2. Production rate

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Production rate is important particularly for industrial applications, as it determines the reactor size (Desloover et al., 2012). For industrial applications, and economic evaluation, rates are usually expressed on a mass basis, while production rates on a molar basis are more useful for mass balances. In order to make comparisons between different reactor systems, the production rate (per unit time, for instance per day) can be reported in three different ways: per unit surface area of electrode (g m−2 d−1), per unit working chamber or total working or empty bed volume (g m−3 d−1), and per unit membrane exchange area (g m−2 d−1). In industrial biotechnology, it is usually expressed per unit biomass (dry weight) per unit time (g gcell dry weight−1 d−1) as the rate is mainly limited by the metabolic rates of microbial catalysts (Rosenbaum and Franks, 2013). For electrochemical processes such as MES, the most relevant production rate is that normalized by electrode surface area, for example g m−2 d−1, due to the limitation of electrode surface for directing these processes. Production rates per electrode surface area (PESA; Eq. (1)) for batch or fed-batch mode reactor operation can be calculated as:

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½C −C t¼n‐1  ¼ t¼n As ðt n −t n‐1 Þ

P vol ¼

½C t¼n −C t¼n‐1  ðt n −t n‐1 Þ

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3.3. Coulombic efficiency and electron recovery

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MES relies on the consumption of current by microorganisms. As the investment of electrical energy contributes to an operational cost, high conversion efficiency is advantageous for overall cost benefit and to avoid adverse side reactions. For cathodic processes, coulombic efficiency (εC) is equal to the percentage of electrons that is recovered (electron recovery) as product(s). For the anodic side, εC is calculated based on the current output and the removal of the reduced compound (or bulk concentration of organic matter, usually reported on the basis of chemical oxygen demand, COD), while electron recovery is based on the current output and the amount of reduced product(s) in the influent. Coulombic efficiency (εC; Eq. (3)) is quite easily described for the process of converting current into products as: F Mp Δe εC ¼ Z i dt

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ð3Þ

with F, Faraday's constant; Mp, product(s) (mol); Δe, the difference in degree of reduction between the substrate and the product; and ∫i dt, the integration of current produced/supplied over time. Some exemplary conversions from CO2 based on Wood–Ljungdahll pathway and reverse β-oxidation are provided below.

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2CO2 + 8H+ + 8e− → CH3COOH + 2H2O

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ð1Þ

with C, the product concentration (g m−3) at time n and n-1; As, the area of electrode per volume of the liquid (working volume) in the electrode compartment (m−2 m−3); and t, the time between two measurements (days). Together with the area of electrode per working volume of reactor (As), PESA allows a complete evaluation of the system (irrespective of the nature of bioproduction process i.e., direct electron transfer-based, H2-based or redox mediator-based) based on production rates. It needs to be noted that often the projected surface area of electrode

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with C, the product concentration (g m−3) at time n and n-1; and t, the time between two measurements (days). Multiplying PESA values by electrode surface area or working volume of the liquid (m−2 m−3) also gives volumetric production rate in g m−3 d−1. It is important to specify whether anolyte or catholyte volume, instead of both is considered for calculating volumetric production rates. Crossover of product(s) through the membrane can make the counter electrode volume important. High volumetric productivity minimizes the contribution of fixed costs in the cost of the product(s). The production rates for continuous flow though systems can be calculated under the steady state production at a constant volumetric influent flow rate per unit time.

2CO2 + 12H+ + 12e− → CH3CH2OH + 3H2O 280

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The production of (bio)chemicals via MES route brings along a number of parameters that are not always reported, or not needed for METs: (i) product titer; (ii) production rate; (iii) coulombic efficiency and electron recovery; and (iv) energetic efficiency.

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will be taken rather than the true surface area, as not all surface may participate in the electrocatalysis and the projected parameter relates more to system geometry. Since planktonic cells in bulk can also actively contribute in the bioproduction process, particularly where H2 or other intermediates channel electrons, the volumetric production rate (g m − 3 d − 1) should also be reported. It is the amount of product(s) produced per unit working volume in the electrode compartment per unit time. For batch or fed-batch mode reactor systems it can be calculated as:

E

current density and potential values, pH, and voltammetry in MES processes is discussed in Section S2 in the SI-1†.

T

234 235

5

2CO2 + CH3COOH + 12H+ + 12e− → C3H7COOH + 4H2O

2CO2 + C3H7COOH + 12H+ + 12e− → C5H11COOH + 4H2O

323 324 325

329 330 332 331 333 334 336 335 337 338 339

As an example of a cathodic process, reduction of CO2 to acetic acid 340 requires 8 electrons. For a daily production of 4 kg acetic acid with 341

Please cite this article as: Patil SA, et al, A logical data representation framework for electricity-driven bioproduction processes, Biotechnol Adv (2015), http://dx.doi.org/10.1016/j.biotechadv.2015.03.002

ð96485Þ ð67:79Þ ð8Þ ¼ 0:595: εC ¼ 86400000 346

3.4. Energetic efficiency

363

Energetic efficiency is one of the most important parameters in the context of BESs. It is affected by numerous factors, including substrates, materials, catalysts, electrode spacing and products. For instance, consider acetic acid production process in a cathode chamber in either a flat plate BES or an H-type BES. The cathode potential can be the same in both cases, however, due to the difference in distance between anode and cathode for a similar setting the ohmic losses will be larger for the H-type BES leading to a higher cell voltage and thus a lower energetic efficiency. For any electrochemical system there are two electrode reactions and thus at least two products are generated in every process. For example, this can be acetate from CO2 reduction at the cathode, and O2 from H2O oxidation at the anode. We can compare this with the chloralkali industry, where Cl2, NaOH and H2 are the initial products in the electrochemical cell. In this case, it is considered that 1 tonne of Cl2, 1.1 tonne of NaOH and 0.03 tonne of H2 are produced by the supply of 1 Electrochemical Unit which is about 3.3 mWh. Hence, a single energy input delivers several products. Therefore, in the chloralkali industry the energetic efficiency is not discussed per unit product. The price setting of the product is in this case ultimately determined by the product most in demand, typically Cl2, and not with the perceived energy spent for the production of this specific chemical. In case of cathodic bioproduction processes, the anodic reaction can be made favorable using a supply of organic matter, otherwise it is likely to be water splitting as in the chloralkali industry. Energetic efficiency of an MES process has a dependency on the individual reactions as well as on the whole process. In order to enable reliable comparison between different production processes independently of aspects such as system geometry and production of secondary or tertiary products, energetic efficiency of product formation (εE,product; Eq. (4)) should be made based on the relationship between amount of energy in the product (Gibbs free energy) and the input energy calculated from the standard hydrogen electrode (Fig. 3):

364

P

D

E T C E

359 360

R

357 358

R

355 356

O

353 354

C

351 352

N

349 350

U

347 348

The εC for the above conversion process is 59.5%. For additional calculations please refer to the excel tool (S2). In this process, the amount of CO2 fixed in acetic acid will be 5.86 kg. From 1 kg CO2 basis, 1.045 kg of formic acid, 0.682 kg of acetic acid and 0.5 kg of butyric acid could be produced at 100% conversion efficiency. If other high value by-products, such as H2, are recovered and used, an overall εC can be calculated with respect to the sum of electrons fixed in the different valuable products. This εC can be an underestimation of the individual anodic or cathodic efficiency, for example, if part of the produced H2 diffuses towards the anode where it is oxidized, leading to lower product harvest. In this process, the εC and electron recovery will be equal. Process imbalances, e.g. in form of undesired formation of residual H2 or O2, are best quantified by including biomass growth and secondary metabolites to close the electron balance. In the case of acetate oxidation at the anode, the εC will be based on the acetate

removal and the current output, while electron recovery will be calcu- 361 lated from the influent acetate concentration and the current output. 362

F

344

continuous supply of 1000 A, the εC with Eq. (3) can be calculated using F = 96485 C/mol e-, Mp = 66.61 mol, Δe = 8 e- and ∫ i dt is 8.64 × 107 C, as:

O

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S.A. Patil et al. / Biotechnology Advances xxx (2015) xxx–xxx

R O

6

εE;product ¼

G f ;i M p;i I t Eel

εE;process ¼

G f ;i Mp;i

I t Ecell

370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395

397 398 399 400 401

403

ð6Þ

with Ean and Eca the anode and cathode potential (as measured). Evidently, in this equation other energy inputs into the production process, such as (i) energy embedded in supplied substrate (e.g. glycerol to 1,3-PDO process) (ii) aeration, mixing and pumping, and (iii) downstream processing are not considered. To accommodate all these factors, one could calculate an overall energetic efficiency as: X

εE;overall ¼

368 369

ð5Þ

with Ecell the voltage over the BES representing: Ecell ¼ Ean –Eca

Fig. 3. Key considerations for calculating energetic efficiency of microbial electrosynthesis processes. These include mainly the energy input at both electrodes. Ean and Eca are the “measured” potentials of the anode and the cathode, respectively. These are the resultant potential values of individual electrodes after considering energy losses such as ohmic, activation and concentration related losses. Other energy inputs, such as (i) energy embedded in supplied substrate (ii) aeration, mixing and pumping (power sources), and (iii) downstream processing that are not depicted in the figure can also be considered for calculating overall energetic efficiency of the production process as explained in Section 3.4.

366 367

ð4Þ

with Gf,i the free energy content in the product i; and Mp,i molar quantity of product i; and Eel the electrode potential (as measured) relative to the SHE. This approach allows calculation for each electrode reaction in terms of efficiency, and allows crosswise comparison of all production processes in terms of individual, electrode-based production. The overall electrochemical process efficiency is (Eq. (5)): X

365

G M X f ;i p;i X I t Ecell þ P add :t þ P sub :t

ð7Þ

Please cite this article as: Patil SA, et al, A logical data representation framework for electricity-driven bioproduction processes, Biotechnol Adv (2015), http://dx.doi.org/10.1016/j.biotechadv.2015.03.002

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S.A. Patil et al. / Biotechnology Advances xxx (2015) xxx–xxx

4.1. Microbial parameters

432 433

Retaining stable metabolic activity over time is a key factor for achieving an efficient bioproduction process. Microbial functionality can be monitored following mainly the growth and the formation of specific product, and other products that might not be desired. From an industrial processing perspective, start-up (phase) of bioproduction, growth yield, and turnover per cell are important microbial parameters. In the case of use of mixed microbial inoculum sources, several parameters that can be assessed in order to monitor and maintain their functionality in MES processes are described in Section S3 (SI-1 †).

438 439 440 441

442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 Q6 459

460 461 462 463 464 465 466 467

with Y, the growth yield (g gCODsubstrate−1); Bbulk, biomass or insoluble fraction of COD in bulk (g); Belectrode, biomass from the electrode surface (g); and CODsubstrate, initial substrate concentration as COD (g). For exclusively biofilm-based MES processes, protein content on the electrode surface can be estimated to determine biomass. Alternatively, biovolume of the biofilm grown on electrodes (which relates to total biomass) can be calculated for instance by using confocal microscopy images and correlated with the productivity. It can be calculated as the number of biomass pixels in all images of a stack multiplied by the voxel size [X × Y × Z pixel sizes] divided by the substratum area of the image stack (Heydorn et al., 2000).

470

4.1.3. Turnover per cell The rate of substrate turnover per cell provides important information for characterizing the performance of the microorganisms. It is dependent mainly on the metabolic efficiency of the microorganisms. The turnover number, which can influence the product titer and productivity is the maximum number of substrate molecules that a microbial cell can convert to product(s) per unit time. It can be determined by calculating the rate of substrate consumption or product formation per cell over a period of time. A sluggish turnover rate, for instance, due to slow growth of the microbes, could be a liability for the production process. It is important to emphasize here that this approach is well established for pure culture systems, but it has not been applied for mixed populations.

480

4.2. Techno-economic evaluations

493

4.2.1. Downstream processes or product recovery Cost-effective downstream processing is the key to commercializing an MES process (Agler et al., 2011). For example, in fermentation processes the final recovery and purification of products are energyintensive and costly (Rogers et al., 2006), with downstream processing often accounting for 50–80% of the total costs of chemical production (Orjuela et al., 2011). Most of the MES processes so far have focused on proof-of-principle experiments and scant attention has been given to product separation (Patil et al., 2015). In case where MES processes use organic waste streams or CO2 as starting materials, the cost can be negatively affected by higher downstream processing costs due to lower conversion efficiencies and the presence of impurities and sideproducts. In addition to the widely used distillation process, some selective downstream processes that could possibly be used for MES products include: in situ product recovery methods such as crystallization, electrodialysis, membrane filtration, and electrochemically induced co-crystallization. At present, MES products are typically produced at low titer (Patil et al., 2015), which hampers the use of conventional recovery techniques. Membrane electrolysis creates new possibilities for instance for acetate recovery at low concentrations when using a fixed applied current (galvanostatic approach) to drive simultaneous production and extraction (Andersen et al., 2014).

494

4.2.2. Techno-economic analysis (TEA) The application of TEA can assist researchers in determining which bioproduction processes are most likely to succeed in the market. TEA is used to focus on the economic viability of a technology, and can be performed to compare existing technologies with emerging METs (e.g. anaerobic digestion versus H2-producing MEC) or to compare different types of METs (e.g. H2 production in an MEC versus acetate production in an MES process) (Foley et al., 2010; Pant et al., 2011). A critical aspect of the TEA is to link production with demand, i.e. to clearly identify the need of a chemical industry to purchase the product. The TEA analysis

516

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T

434 435

C

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E

424 425

4.1.1. Start-up The start-up of product formation directly influences the initial energy investment, production rate, and thus overall process economics. Inoculum size and growth phase of the microorganisms in the inoculum, together with possible transition of growth media, affect this start-up. The start-up phase of product formation is usually less with the use of pure cultures (Nevin et al., 2011). In case of autotrophic MES processes, pre-growth of the microorganisms on heterotrophic substrate appears attractive but it entails a risk of a long lag phase. In case open systems are used, community adaptation and dynamics will be critical to limit start-up time. In this case, the choice of inoculum will be critical to ensure operational stability, similar to anaerobic digestion (De Vrieze et al., 2014). Microbial inoculum from systems close to the process will lead to faster and safer startup and also more robustness. In order to tackle the start-up issue with mixed microbial inoculum sources, prior enrichment strategies can be applied in electricitydriven bioproduction processes (Marshall et al., 2013; Patil et al., 2014; van Eerten-Jansen et al., 2013).

R

422 423

R

420 421

N C O

418 419

U

416 417

ð8Þ

F

431

415

Bbulk þ Belectrode CODsubstrate

O

4. Other parameters

413 414

Y¼

R O

430

411 412

468 Q7

on the electrode surface. It can be calculated as (Eq. (8)):

P

428 429

with ∑Padd. t the additional energy inputs for downstream processing, pumping and others, and ∑Psub. t the energy influx represented by the substrate. The choice of representation through Eqs. (4), (5) or (7) ultimately depends on the aim of the process. To compare a bioelectrocatalytic process, Eq. (4) is most useful. To compare an MET process balance, Eq. (5) is of primary interest. For comparing complete production processes, Eq. (7) would be chosen. We developed an easy-to-use excel tool that enables calculations of key production parameters mainly for electricity-driven processes at the cathode and also for MFCs (refer an online excel tool SI-2 ‡). It is worked out for the three examples of cathodic bioproduction processes, showing the importance of correctly assessing the energetic efficiency. For example, when considering glycerol to 1,3-PDO process the key is to consider the energy present in the glycerol as an input. For the case of H2 and NaOH production in an MEC, a dual benefit is obtained which should both be taken into account. The approach also shows that acetate production from CO2 via an MES route can occur at a high energetic efficiency, which in combination with the high efficiency of photovoltaic cells, can lead to an unprecedented efficiencies in sunlight to bioproduct(s) conversion.

410

7

4.1.2. Growth yield Cell growth has direct impact on the production profile, titer, and production rates. It also affects both carbon and electron recoveries due to diversion of carbon and electrons into biomass. A major challenge in bioproduction processes is therefore to achieve balance between growth and product formation. Estimation of the net growth yield (Y) can be achieved by determining dry mass or insoluble fraction of COD from the bulk and by determining the biomass (g COD) grown

Please cite this article as: Patil SA, et al, A logical data representation framework for electricity-driven bioproduction processes, Biotechnol Adv (2015), http://dx.doi.org/10.1016/j.biotechadv.2015.03.002

471 472 473 474 475 476 477 478 479

481 482 483 484 485 486 487 488 489 490 491 492

495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515

517 518 519 520 521 522 523 524 525

543

5. Conclusions

544

556 557 Q8

Although proof-of-concept studies have demonstrated the feasibility of several electricity-driven bioproduction processes, volumetric rates of product formation and product titers are still currently quite low. For a rational development of this field, important operational and production parameters, along with techno-economic and life cycle issues, need to be considered. In order to achieve greater success in translating this research concept into marketable technologies, important production parameters such as energetic efficiency of the process, product yield, and most importantly downstream processing costs (which so far have generally been neglected) need to be fully considered and reported. The data representation framework proposed here will lead to a better agreement among different studies on the issues critical for evaluation of electricity-driven bioproduction processes, and will allow crosswise comparison of research findings.

558

Conflict of interest

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545 546 547 548 549 550 551 552 553 554 555

559

C

539 540

The authors declare no conflict of interest.

E

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R

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T

542

4.2.3. Life cycle analysis (LCA) LCA is used to evaluate the environmental impacts of a technology, often taking a cradle to grave approach (Singh et al., 2013). LCA is a well-established technique to assess the influence of new technologies, and its use is well-accepted for biofuels (Singh et al., 2010). Most suggestions for conducting the LCA of METs (Pant et al., 2011) are also valid for electricity-driven bioproduction processes. Choice of a functional unit in this case is an important aspect, for example, mass of production per 1000 A m− 3. A system boundary should consider integrated process operation, reactor and component design.

Acknowledgments

561 562

570

SAP acknowledges the financial support via a Marie Curie IntraEuropean fellowship (Grant agreement no. PIEF-GA-2012-326869) within the 7th European Community Framework Programme. SG is supported by the Special Research Fund (BOF) of the University of Ghent (Belgium). KR is supported by the European Research Council via Starter Grant ELECTROTALK and the Multidisciplinary Research Partnership Ghent Bio-Economy. BEL acknowledges the support from the Francqui Foundation. Authors thank Dr. Antonin Prévoteau and Dr. Hugo Roume for fruitful discussions especially on electrochemical and molecular biology aspects (refer SI-1†) of the paper.

571

Appendix A. Supplementary data

572 573

Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.biotechadv.2015.03.002.

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consists of identifying expenses for capital and identifying performance lifetime, substrates and downstream processing costs, energy uses, and waste disposal (Angenent and Rosenbaum, 2013; Desloover et al., 2012). Although a full TEA has not yet been conducted on any MES system, it is reasonable to assume that it will need to be competitive with well-established fermentation or electrochemical production processes (Desloover et al., 2012; Rabaey et al., 2011).

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