In combination with associated gas or biomass as feedstock, microreaction engineering shows high potential for decentralized fuel production [1–3]. For example, application of microreactors for a gas to fuel process may replace gas flaring. Such replacement would result in avoiding greenhouse gas emissions during exploration and facilitate the use of the associated gas. Thereby, a lowering effect to climate change can be expected. The avoidance of greenhouse gas emissions, however, is only accurate under the premise that the greenhouse gas emissions during the entire life cycle of the microreactor are lower than the gas flaring volume, while excluding further use of the produced fuel. With regard to the environmental impact of fabrication processes of new technologies such as microsystems and microprocess engineering , the expected sustainability advantage requires validation.
Life Cycle Assessment (LCA) is an established methodology to analyze ecological impacts of a product or technology along the entire life cycle. Moreover, LCA is gaining importance as an assessment tool for new technologies in early R&D. LCA may be used to minimize the environmental impact of a design during the entire life cycle of a product (manufacturing, use phase, recycling), and to identify weak points and areas requiring special attention and improvement . The application of LCA for interdependent device and process development in general and in microreaction engineering in particular has not thoroughly been discussed yet. So far, only few publications exist regarding LCA in microreaction engineering [6–9]. The case studies utilize existing so-called microstructured reactors, whereas the development of the microreactor is not considered.
Here, we use the Modular Server-Client-Server (MSCS) methodology  to combine technical optimization and LCA in early R&D on the example of a microstructured reactor design and fabrication for offshore natural gas to liquid fuel conversion (GtL) by Fischer-Tropsch synthesis (FTS).
2 Microreactors for fuel production
Although Fischer-Tropsch technology is an established technology, carrying out the process in a microreaction engineering environment is a new approach. At the beginning of the 21st century starting with the Global Gas Flaring Reduction (GGFR) initiative first feasibility studies were undertaken for highly exothermic gas phase reactions for fixed-bed reactors [11, 12] and a simulation study was performed for the comparison of different reactor types for FTS . A test reactor based on a multichannel packed bed microstructured reactor design  and prototype reactors  were developed recently. Further developments have been undertaken at Karlsruhe Institute of Technology-Institute for Micro Process Engineering (KIT-IMVT) in the field of syngas production with heterogeneous catalysts, mainly from methane by steam reforming and partial oxidation in microreactors [16–18]. However, no ‘micro’ GtL plant in commercial scale exists to date.
2.1 Microreactor design
The microreactor design is one part within the whole process and plant design. Compared to other microstructured devices such as micromixers or micro heat exchangers, the microreactor design is more complex due to catalyst integration and necessary homogeneous fluid and temperature distribution . A microreactor consists of four parts: reactor with reaction and cooling channels, flow distribution structures, flanges/connectors, and if necessary a pressure vessel, depending on the safety regulations. In addition, a catalyst is required either by catalyst filling or wall coating for carrying out catalytic reactions in microreactors.
Currently, three main different architectures for microreactors can be distinguished: a monolith, a multiple plate or foil, and a membrane-like architecture . Depending on the application and material, microreactors can also consist of only one plate or foil. In addition to design considerations such as channel diameter and length, wall thickness, catalyst integration method, cooling or heating design, reaction specific parameters are also of major relevance. The catalyst composition, particle size, porosity, pore structure, and bulk density influence the mass transport of reactants, heat transport, and therefore determine productivity and product distribution. Figure 1 shows the microreactor principle as a stack of single metal foils with reaction channels that are coated on the walls with catalyst, cooling channels, and a scheme of educts/products and catalyst representing the interior chemistry. One important microreactor design parameter for catalytic chemical reactions is the catalyst void fraction, defined as catalyst volume per channel volume.
2.2 Microreactor fabrication
Different manufacturing techniques have to be applied for different microreactor parts and materials. In addition, the reactor design and the manufacturing technique are strongly linked in terms of the type of microstructure. Surface roughness, channel aspect ratio, and channel shape (cross-section and shape along the plate) are three important parameters for choosing the appropriate fabrication technique.
In this study, we consider microreactors made from metal plate/foils and therefore we focus on metal processing techniques. In general, the fabrication of a microreactor made from metal foils consists of four fabrication stages named structuring, assembly and bonding, catalyst, and packaging and sealing. Fabrication techniques considered for the individual stages of the microreactor fabrication in metal  are summarized in Figure 2. Figure 2 also shows the dependencies between foregoing considerations in reactor design and fabrication within the design phase. The channel size/shape determination and the fabrication technique are linked, as not all channel designs are appropriate for a specific fabrication technique. For example, for wet chemical etching the channel depth must not exceed half of the width.
We used parameterized Life Cycle Inventory Analysis (LCI) within an MSCS methodology for the analysis of different microreactor designs for FTS with LCA. LCA, parameterized LCI, and MSCS are explained in the following sections.
3.1 Life Cycle Assessment (LCA)
Life Cycle Assessment, a standardized technique, addresses environmental aspects and potential environmental impacts throughout the life cycle of a product from raw material acquisition through production, use, end-of-life treatment, recycling, and final disposal, and therefore contributes to a better understanding of the impact of a product on the environment [22, 23]. The LCA technique consists of four phases: goal and scope definition, Life Cycle Inventory Analysis (LCI), Life Cycle Impact Assessment (LCIA), and interpretation.
The first phase clearly defines the goal and scope of an LCA and therefore determines the depth and width, the system boundary, and level of detail. The incoming and outgoing material and energy flows of the product system under investigation are collected and modeled in the second phase, the LCI. These results are assigned according to the selected impact category representing environmental issues of concern in the third phase, the LCIA. Finally, the inventory and impact assessment results are summarized and discussed in accordance with the goal and scope definition.
The whole LCA technique follows an iterative approach as illustrated in Figure 3
3.2 Parameterized Life Cycle Inventory Analysis (LCI)
In addition to attributional and consequential LCI, the so-called parameterized LCI is described in the literature . In parameterized LCI the inventory of the whole system or single subsystems is parameterized by specific characteristics of the system. Within parameterized LCI these characteristics, called parameters, enable the following advantages compared with conventional LCI: higher flexibility for database use (parameterized database), sophisticated systems, controlled scenario modeling, and interfaces for mathematical simulations or sensitivity analysis . Therefore, parameterized LCI shows a high potential as an analysis tool in technology development, especially in early design phases . Because in early R&D design dependent material and energy flows are not available for, i.e., the fabrication processes and the use phase of the product especially for new developments such as the microreactor for FTS, these processes are parameterized. This parameterization allows the direct linking between design considerations of the not-yet existing technology and the fabrication processes and therefore the calculation of necessary material and energy flows needed in LCA analysis. In general, different types of data are used within LCI such as generic, measured, calculated, and estimated data – process specific or averaged.
3.3 Modular-Server-Client-Server (MSCS) methodology
For LCA in early R&D of a microreactor, we applied the MSCS methodology . This methodology consists of three major steps: modularization of the system, definition of the clients and servers on content and software level, and definition of the interfaces on the software and content level. Figure 4 illustrates the methodology on the example of microreactor fabrication. Relevant interface parameters for the microreactor design and each single fabrication process, such as catalyst void fraction and catalyst productivity are defined using matrix and dimensional analysis. Within parameterized LCI, material and energy flows are calculated based on design-dependent interface parameters, and therefore LCI and LCIA results are directly linked to the microreactor design.
To successfully evaluate the potential of a microreactor for fuel conversion to replace gas flaring, the greenhouse gas emissions of multiple microreactor designs must be assessed in advance to allow selecting the most appropriate design. It is thus necessary to understand the impact of different fabrication methods and design parameters to achieve the most appropriate design for this application. Within the next sections we explain the modeling setup for parameterized LCI within MSCS, starting with software settings followed by parameterized microreactor design and fabrication on the example of a microreactor made from metal foils for FTS.
4.1 Software and database
4.2 Microreactor design in early R&D
The microreactor is designed for 50 kgproduct h-1 (mproduct t -1). Therefore, the reaction channel volume Vchannel is calculated with the catalyst productivity pcatalyst, catalyst bulk density ρcatalyst, and the catalyst void fraction (fcvf) using Eq. (1). The total channel volume consists of reaction and cooling channels [Eq. (3)], whereas the cooling channels are calculated with a reduction factor f using Eq. (2), which represents the possibility to reduce the cooling channels without losing the heat transfer capabilities of the plate type microreactor. In our example, the cooling channel volume is one-fifth of the reaction channel volume. The channel volume is also described by the channel height h, limited by the used metal foil thickness, width b and length l, and also limited by the metal foil size [Eq. (4)]. We considered metal foils with 400×300×0.15 mm3. Following the MSCS methodology, Vchannel is called an R&D design parameter.
In Table 1 all microreactor design parameters are listed for the design modeling. With the exception of the catalyst void fraction, all parameters for design A and design B are derived from experimental data . Design parameters for the other designs are chosen as modifications with regard to the questions proposed above (influence of design parameters to GWP of different microreactor designs).
Two structuring processes are considered in this study: wet chemical etching and mechanical micromachining (milling). LCI results are calculated with LCI parameters that differ for the two processes due to the used inventory data types. Each process required a specific type of parameterization and data. We used averaged measured data for the inventory calculation of the wet chemical etching process of metal foils. The process data (energy, raw materials, and chemicals) y are linked by a constant x to the LCI parameter nblank [Eq. (5)]. For example, the wet chemical etching of one blank (stainless steel microstructured plate) produces 1 kg of waste and requires 0.3 kg of stainless steel. The energy data for the milling process are calculated by a function of the R&D interface parameter Vchannel and the process parameters for fabrication such as slope value (1-z) within the Kienzle equation shown in Eqs. (6) and (7). Milling machine design and efficiency losses (η) are considered for the calculated power consumption in Eq. (7c). Furthermore, the milling machine was dimensioned twice as big as the calculated power consumption.
According to the MSCS methodology, interface parameters between R&D design and fabrication processes are of major relevance for direct linking of the design with process parameters. Eqs. (1) to (3) relate the interface parameter Vchannel, containing reaction and cooling channels, with the catalyst density ρ, the catalyst productivity p, and the estimated output mproduct. Based on the metal plate volume Vblank (related to foil geometry) Eq. (8) calculates the interface parameter Vchannel/blank with the help of volume specific process parameters such as cutting losses locut and etching losses loetch. Based on this, the LCI parameter nblank for both structuring processes can be determined from Eq. (9). Compared to wet chemical etching, the milling process uses nblank as the R&D parameter and Vchannel as the LCI parameter.
The identified R&D, interface, and LCI parameters within the MSCS methodology are illustrated in Figure 5 for wet chemical etching and milling.
4.4. Microreactor assembly and bonding
The energy demand W for diffusion bonding of the microstructured plates to form a microreactor is calculated with the specific heat capacity cp of the metal foils (stainless steel 316L), the design-dependent reactor mass mmicroreactor determined from the first fabrication stage microstructuring, the bonding temperature ΔT, and factor f for efficiency loss of the oven as shown in Eqs. (10) to (12). Therefore, this parameterized LCI type belongs to the same type as the milling process, parameterization by a function of the interface parameters.
4.5 Catalyst integration
Wash-coating consists of four process steps: co-precipitation, evaporation, dehydration, and calcination. In this study, we only consider co-precipitation. We are aware of the fact that the neglected processes might have an impact on GWP due to the electricity needed for these processes. Raw material quantities of the two considered catalysts are calculated by the weight fractions in the catalyst extracted from experimental data .
4.6 Packaging and sealing
In packaging and sealing the raw material quantities for the distribution structure, flanges, and additional safety pressure vessels are considered. The amount for the distribution structure is calculated by a factor of 1 based on empirical data in relation to the raw material amount for the reaction and cooling channels at the chosen microreactor size. The raw material amount for the flanges is considered as constant in certain size ranges of 2 kg – even when changing the outer geometry of the microreactor. For the calculation of the safety pressure vessel weight, we used the long-term hydrostatic pressure resistance formula or Barlow's formula shown in Eq. (13), which determines the required thickness of the pressure vessel. Normal stainless steel with a stress limit of 230 N mm-2 and a pressure of 1 bar was assumed while expanding the small inventory of the microreactor to the large pressure vessel volume in case of microreactor failure.
This pressure vessel design is coupled via the required diameter d to the microreactor design multiplied with a security factor of 1.1.
Table 2 summarizes all applied factors in the equations for manufacturing not mentioned previously in the text but essential for modeling.
The GWPs per microreactor exemplarily shown for design A (see Table 1) are illustrated in Figure 6 according to the used structuring process for reaction and cooling channels. In addition to wet chemical etching and mechanical micromachining, the third bar in Figure 6 illustrates a combination of both. Wet chemical etching is used for structuring of the reaction channels and micromechanical machining for the cooling channels. Microreactors manufactured using micromechanical machining (milling) show the lowest GWP for all designs by several orders of magnitude. Based on these results, we assume that the structuring fabrication step by wet chemical etching is inferior. A complete fabrication by milling reaction and cooling channels seems advantageous at a discussion level without considerations of GWP savings in the GtL process due to the application of microreactor technology.
According to the first finding on a strong influence of the wet chemical etching, existing designs A and B were compared regarding the GWP impact of each fabrication step using wet chemical etching as structuring process. Results are shown in Figure 7. The structuring shows the highest impact as assumed. A detailed analysis (Figure 8) of the materials and energy used in the structuring process show a significant impact of the electricity followed by iron(III)chloride for wet chemical etching and stainless steel is the major source of GWP impact for mechanical microstructuring. The level of GWP impact of stainless steel is, however, similar for wet chemical etching and mechanical microstructuring.
We further analyzed the influence of R&D parameters by comparing designs with different parameter settings by varying one parameter at a time. Designs A, C, D for Co/Ni catalyst and B, G, H for Co catalyst are compared using parameter variations of the catalyst void fraction. Both options with the highest catalyst void fraction of 0.6 show the lowest GWP impact, whereas a Co/Ni catalyst has a higher GWP compared to a Co catalyst as shown in Figure 9. Variations of the R&D parameter catalyst productivity show the same tendency; the higher the productivity the lower the GWP. To receive information about the relevance of both parameters, we compared both parameter variations on the example of the Co catalyst in Figure 10. Obviously, increasing the catalyst void fraction shows a stronger reduction of the GWP than increasing productivity.
6 Conclusion and outlook
In conclusion, we demonstrated the advantages of parameterized LCI and the MSCS methodology. We identified critical parameters affecting environmental impact. As such, it is a versatile method to analyze the ecological impact of the design options for construction of the FTS microreactor with parameters applied from established manufacturing processes at an early R&D stage.
Critical parameters are, for example, manufacturing by wet chemical etching. This was affecting the total GWP of microreactor fabrication much more than other factors. This is not an effect of the required stainless steel, but is dependent on electricity and etching media consumption. The productivity of the catalyst was identified as changing the GWP less than the catalyst void fraction.
In the future, we plan an extension of the model to different operation methods of the microreactor and offshore application of the technology. Because an influence of the microreactor design on the whole process chain of gas conversion is to be expected, we believe that the MSCS method is a valuable tool towards comprehensive evaluation of novel technologies in early R&D.
List of abbreviations
Global Gas Flaring Reduction
Gas to liquid
Global Warming Potential
Institute for Micro Process Engineering
Karlsruhe Institute of Technology
Life Cycle Assessment
Life Cycle Inventory Analysis
Life Cycle Impact Assessment
Research & Development
catalyst void fraction
productivity, kgproduct kgcatalyst-1 h-1
feed rate, mm
cutting speed, mm min-1
density, kg m-3
- reaction channel
catalyst void fraction
Velocys. Catalyst Development. Available at: http://www.velocys.com/ocge07.php
Elvidge CD. Energies 2009, 3, 595–622Google Scholar
NOAA – National Geophysical Data Center. Global Gas Flaring Estimates. Available at: http://www.ngdc.noaa.gov/dmsp/interest/gas_flares.html
Kunari E, Valkama J, Keskinen M, Mansikkamaeki P. J. Clean. Prod. 2009, 17, 791–799 Google Scholar
Abele E, Anderl R, Birkhofer H, Eds. Environmentally-friendly Product Development. Methods and Tools, Springer: London, 2005 Google Scholar
Kralisch D, Kreisel G. Chem. Eng. Sci. 2007, 62, 1094–1100 Google Scholar
Huebschmann S, Kralisch D, Hessel V, Krtschil U, Kompter C. Chem. Eng. Technol. 2009, 32, 1757–1765Google Scholar
Huebschmann S, Kralisch D, Loewe H, Breuch D, Petersen J, Dietrich T, Scholz R. Green Chem. 2011, 13, 1694–1707Google Scholar
Hessel V, Kralisch D, Krtschil U. Energy Environ. Sci. 2008, 1, 467–478 Google Scholar
Zschieschang E, Pfeifer P, Schebek L. In Proceedings of the 19th CIRP International Conference on Life Cycle Engineering, Dornfeld DA, Linke BS, Eds., Springer: Berkeley, CA, 2012, pp. 119–124 Google Scholar
Güttel R, Turek T. Chem. Ingen. Techn. 2009, 4, 495–500 Google Scholar
Güttel R, Turek T. Chem. Eng. Sci. 2010, 65, 1644–1654 Google Scholar
Güttel R, Turek T. Chem. Eng. Sci. 2009, 64, 955–964 Google Scholar
Myrstad R, Eri S, Pfeifer P, Rytter E, Holmen A. Catal. Today 2009, 147S, 301–304 Google Scholar
Tonkovich AL, Mazanec T, Jarosch K, Fitzgerald S, Yang B, Taha R, Kilanowski D, Lerou J, McDaniel J, Atkinson D, Dritz T. Gas-to-Liquids Conversion of Associated Gas Enabled by Microchannel Technology. Available at: http://www.velocys.com/press/wp/wp091504_%20associated_gas_white_paper_may09.pdf
Tonkovich ALY, Yang B, Perry ST, Fitzgerald SP, Wang Y. Catal. Today 2007, 120, 21–29 Google Scholar
Kolb G. In Micro Process Engineering, Vol. 2, Hessel H, Renken A, Schouten JC, Yoshida J, Eds., Wiley-VCH: Weinheim, 2009, pp. 421–443Google Scholar
Pfeifer P. In Micro Process Engineering, Vol. 2, Hessel H, Renken A, Schouten JC, Yoshida J, Eds., Wiley-VCH: Weinheim, 2009, pp. 445–464 Google Scholar
Schirrmeister S, Brandner JJ, Kockmann N. In Micro Process Engineering, Kockmann N, Ed., Wiley-VCH: Weinheim, 2006, pp. 203–234 Google Scholar
Güttel R, Kunz U, Turek T. Chem. Ingen. Techn. 2007, 5, 531–543 Google Scholar
Brandner JJ, Gietzelt T, Henning T, Kraut M, Moritz H, Pfleging W. In Micro Process Engineering, Kockmann N, Ed., Wiley-VCH: Weinheim, 2006, pp. 267–320 Google Scholar
DIN EN ISO 14044:2006 Google Scholar
DIN EN ISO 14040:2006 Google Scholar
Mueller KG. Int. J. LCA 2004, 4, 227–235 Google Scholar
Schmidt M, Hedemann J, Kreissig J. AP 3 – Parametrisierung. Projektbericht. im Rahmen des Forschungsvorhabens FKZ 01 RN 0401 im Auftrag des Bundesministeriums fuer Bildung und Forschung. Institut fuer Technikfolgenabschaetzung und Systemanalyse – Zentralabteilung Technikbedingte Stoffstroeme, Karlsruhe, 2007 Google Scholar
CML 2001. Handbook on Impact Categories, Institute of Environmental Sciences, Leiden University: Leiden Google Scholar
Weidema B, Hischier R. ecoinvent, ecoinvent Centre c/o EMPA St. Gallen, Swiss Centre for Life Cycle Inventories, St. Gallen, 2010 Google Scholar
About the article
Dipl.-Ing. Eva Zschieschang is pursuing her PhD at the Institute of Technology Assessment and Systems Analysis (ITAS) at the Karlsruhe Institute of Technology (KIT), Germany on the development of sustainable applications for energy conversion using microprocess engineering. She performed her diploma thesis in microsystem engineering developing a novel concept for the arrangement and production of PEM fuel cells at the Fraunhofer Institute for Solar Energy Systems and the University of Freiburg, Germany.
Dr.-Ing. Peter Pfeifer studied chemical engineering at University Erlangen-Nürnberg and performed his PhD at Karlsruhe Institute of Technology (KIT). He became group leader for Gas and Multiphase Catalysis at the Institute for Micro Process Engineering at KIT in 2001. Since 2008 he gives Master courses about Micro Process Engineering at KIT and has already published more than 40 peer reviewed papers.
Liselotte Schebek is Head of the Department of Technology-Induced Material Flow, Institute for Technology Assessment and Systems Analysis (ITAS-ZTS) at the Karlsruhe Institute of Technology (KIT). She holds the chair of Industrial Material Cycles at the Technical University of Darmstadt, Faculty of Civil Engineering and Geodesy. She studied Chemistry at the Technical University of Darmstadt (Graduation: Dipl.-Ing.) and graduated with a PhD in 1990 at the University of Mainz. Her main research fields are Life Cycle Assessment, Material Flow Analysis, Carbon Flows within the Technosphere, Biobased Products and Energetic Use of Biomass, Material Flows within the Construction Sector, Sectoral and Process-related Analysis of Waste Flows, Scarce Resources and Material Development, Industrial Ecology and Environmental Management Systems.
Published Online: 2012-08-25
Published in Print: 2012-08-01