CHEMICAL ENGINEERING TRANSACTIONS VOL. 76, 2019 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Guest Editors: Petar S. Varbanov, Timothy G. Walmsley, Jiří J. Klemeš, Panos Seferlis Copyright © 2019, AIDIC Servizi S.r.l. ISBN 978-88-95608-73-0; ISSN 2283-9216 Graphical Break-Even Based Decision-Making Tool (BBDM) to Minimise GHG Footprint of Biomass Utilisation: Biochar by Pyrolysis Yee Van Fana,*, Jiří Jaromír Klemeša, Raymond R. Tanb, Petar Sabev Varbanova aSustainable Process Integration Laboratory – SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic bChemical Engineering Department, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines fan@fme.vutbr.cz This study develops a graphical decision-making tool inspired by the break-even concept. It facilitates the selection of biomass utilisation where the highest possible profit and lowest GHG emissions indicate the optimal choice. The GHG intensity (kg CO2eq/MWh) and GHG price (USD/kg CO2eq) comprise the y-axis and x-axis of the Break-even Based Decision Making (BBDM) tool. The applicability of the tool is demonstrated by a case study where the biomass (switchgrass and wheat straw) is treated with slow pyrolysis for energy generation and/or biochar production. Burning for electricity is generally a preferable option, especially for countries with high GHG intensity. Biochar application as a form of carbon sequestration is suggested at low GHG intensity. This application becomes the preferred option when the GHG price is higher than 0.03 USD/kg CO2eq for switchgrass and higher than 0.01 USD/kg CO2eq for wheat straw. Wheat straw, as agricultural waste, has the advantage of lower GHG footprint while switchgrass offers a higher energy output. In general, the choice of the best use of biomass can be different under different circumstances. The developed tool is able to provide a rigorous basis for decision support when the GHG intensity and GHG price are defined. 1. Introduction The energy sector is one of the main contributors to GHG and air pollutants footprints. Various measures have been proposed or implemented to tackle this environmentally sustainable issues. The energy transition is one of the pathways toward emission mitigation by minimising the dependency on fossil energy. IRENA (2019) suggests the increased use of renewable energy combined with intensified electrification are decisive for the world to meet key climate goals by 2050. Energy from biomass (bioenergy) is one of the most investigated options. However, there is still an ongoing debate (Sterman et al., 2018) on carbon neutrality or renewable characteristics. Mitigation alone is deemed insufficient to achieve the Paris Agreement target in addressing the global warming/climate change issues (Haszeldine et al., 2018). Negative emission technologies (NETs) have to be deployed within physical and economic limits (Smith et al., 2016). Negative emissions can also be achieved by implementation biochar-based carbon management networks (Tan, 2019), besides the various emissions capture technologies, which have a relatively lower risk and investment cost. Pyrolysis is one of the notable pathways for biomass utilisation as the outputs are bioenergy and biochar with sequestration function. The economic and emission accounting for a sustainable biomass utilisation remains a challenge in decision making due to its dynamic nature (the selection highly depends on the baseline scenario) and still subject to uncertainty (biochar application, biogenic carbon of biomass). Kulas et al. (2018) conducted the techno- economic analysis and life cycle assessment (LCA) of biochar as a soil amendment, as feedstock to produce activated carbon, and as fuel to displaced coal. Activated carbon is identified as the biochar utilisation that provides the highest mitigation benefits than as an energy source. Brown et al. (2011) assess the profitability of two biochar production, the value of biochar as a carbon offset plays a significant role; slow pyrolysis with the substrate cost of 83 USD/t is identified as not profitable. Yang et al. (2016) assessed the GHG emission of biomass-based pyrolysis in China and suggested the GHG intensity as 1.55 x10−2 kg CO2 eq./MJ. It is DOI: 10.3303/CET1976004 Paper Received: 22/05/2019; Revised: 08/07/2019; Accepted: 09/07/2019 Please cite this article as: Fan Y.V., Klemes J.J., Tan R.R., Varbanov P.S., 2019, Graphical Break-Even Based Decision-Making Tool (BBDM) to Minimise GHG Footprint of Biomass Utilisation: Biochar by Pyrolysis, Chemical Engineering Transactions, 76, 19-24 DOI:10.3303/CET1976004 19 recommended that returning 41 % of biochar to the field would contribute to close to zero net GHG emissions. Fidel et al. (2019) studied GHG emissions when biochar ended on the soil. The differences in the results from laboratory incubation experiment and the impact of biochar at the field scale were highlighted as a pitfall. A meta-analysis by He et al. (2017) suggests biochar application significantly increased soil CO2 fluxes by 22 % but decrease N2O fluxes by 31 %. The selection of the baseline scenario, the definition of displaced energy (avoided emissions) and the current understanding of biochar effectiveness and scalability needs to be further assessed for a consensus. The energy displacement is always done in reference to a baseline that needs to be clearly defined as the avoided emissions can change with the energy transition. There is an underlying assumption that the energy is being displaced (by energy with lower intensity). However, in some cases, the generated energy is fulfilling incremental energy demand. The complexity of the bioenergy system and its indirect effect, the selection of the system function and system boundaries that directly affect the results obtained have been highlighted by Lijó et al. (2019). Despite the inconsistent results, data that are changing from time to time according to the technology development, GHG intensity as well as the carbon tax (in this study, termed GHG price), a systematic methodology is a key to facilitate the decision making. To summarise, there has been plenty of economic and environmental assessments on the pyrolysis processes of different biomass types and the biochar utilisation. The consistency of the decision-making methodology needs improvement, and it is generally based on scenario analysis. A systematic decision-making tool which considers the impacts under different circumstances, preferably in graphical form, with the capability of identifying the optimal biomass utilisation deserves further development. This study aims to propose a graphical decision-making tool in facilitating the selection of biomass treatment or utilisation. The novel contributions of this work include: (i) A set of generic equations that form the basis for the decision-making tool, considering both the economic and life-cycle environmental footprint (in this case study GHG). (ii) A novel graphical Break-even Based Decision-Making (BBDM) tool, where the environmental price (GHG) and the GHG intensity of energy are chosen to determine the suitable biomass utilisation. It is efficient in identifying the alternatives with the highest possible profit and lowest GHG emissions. (iii) A pyrolysis case study of biomass, where two types of substrates (a) energy crop and (b) agricultural residue for energy and biochar production, are assessed to demonstrate the applicability of BBDM. BBDM is designed to be feasible in capturing the optimal utilisation under the dynamic change of GHG price and GHG intensity of energy. The developed BBDM tool is not limited to the presented case study, but for a broader range of decision making. A similar concept can be applied to the selection of environmentally sustainable transportation modes. The extended application is further discussed in conclusion. 2. Methodology This section presents the break-even relations that underpin the graphical decision-making tool as well as the algorithms for the construction. The break-even point in this context defines when two pyrolysis/biomass treatment processes (i and j) would generate equivalent profit, see Eq(1). GHG price is applied in order to identify a compromise solution for an economic-environmental decision as well as to reduce the multi-objective problem into a single objective. The total profit is defined as in Eq(2), 𝑃𝑟𝑜𝑓𝑖𝑡𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 considers the earning from the selling of recovered products (energy and biochar) deducted by the operating cost of the entire life cycle, Eq(3). 𝑃𝑟𝑜𝑓𝑖𝑡𝑒𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡 , defined in Eq(4), considers the GHG credit from recovering the energy and applying the biochar to the soil (sequestration) deducted by the emission released along with the processes which incur a penalty cost of GHG. Eqs(5) - Eq(7) show the estimation of GHG credit and GHG penalty incurred by the process. The independent variable is the “break-even” GHG price. Eq(8) shows the estimation of GHG price when the total profit of two pyrolysis processes are equal. 𝑃𝑟𝑜𝑓𝑖𝑡𝑡𝑜𝑡𝑎𝑙 (𝑖) = 𝑃𝑟𝑜𝑓𝑖𝑡𝑡𝑜𝑡𝑎𝑙 (𝑗) (1) 𝑃𝑟𝑜𝑓𝑖𝑡𝑡𝑜𝑡𝑎𝑙 = 𝑃𝑟𝑜𝑓𝑖𝑡𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 + 𝑃𝑟𝑜𝑓𝑖𝑡𝑒𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡 (2) 𝑃𝑟𝑜𝑓𝑖𝑡𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 = 𝐸𝑒𝑛𝑒𝑟𝑔𝑦 + 𝐸𝑏𝑖𝑜𝑐ℎ𝑎𝑟 − 𝑂𝐶 (3) Where 𝐸𝑒𝑛𝑒𝑟𝑔𝑦 are the earnings from recovered energy, 𝐸𝑏𝑖𝑜𝑐ℎ𝑎𝑟 are the earnings from biochar, and 𝑂𝐶 is the operating cost 𝑃𝑟𝑜𝑓𝑖𝑡𝑒𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡 = 𝐶𝑒𝑛𝑒𝑟𝑔𝑦 + 𝐶𝑏𝑖𝑜𝑐ℎ𝑎𝑟 − 𝑃𝑜𝑝 (4) 20 𝐶𝑒𝑛𝑒𝑟𝑔𝑦 = 𝐴𝑚𝑜𝑢𝑛𝑡𝑅𝐸 × 𝐶𝐼 × 𝐺𝐻𝐺𝑝𝑟𝑖𝑐𝑒 (5) 𝐶𝑏𝑖𝑜𝑐ℎ𝑎𝑟 = 𝐴𝑚𝑜𝑢𝑛𝑡𝐵 × 𝑆𝐹 × 𝐺𝐻𝐺𝑝𝑟𝑖𝑐𝑒 (6) 𝑃𝑜𝑝 = 𝐴𝑠 × 𝑂𝑒𝑓 × 𝐺𝐻𝐺𝑝𝑟𝑖𝑐𝑒 (7) Where 𝐶𝑒𝑛𝑒𝑟𝑔𝑦 is the GHG credit from the recovered energy, 𝐶𝑏𝑖𝑜𝑐ℎ𝑎𝑟 is the GHG credit from the application of biochar, 𝑃𝑜𝑝 is the GHG penalty by the operating process, 𝐴𝑚𝑜𝑢𝑛𝑡𝑅𝐸 is the amount of recovered or generated energy (syngas, bio-oil and or biochar) by the pyrolysis process of agricultural waste or energy crops, 𝐶𝐼 is the GHG intensity of energy (e.g. electricity power) where the emission is associated with electricity generation from identified regions based on the energy mix, 𝐺𝐻𝐺𝑝𝑟𝑖𝑐𝑒 = is the cost coefficient (e.g. carbon emission tax, environmental price), 𝐴𝑚𝑜𝑢𝑛𝑡𝐵 is the amount of biochar produced, 𝑆𝐹 is the carbon emission sequestration factor of biochar, 𝐴𝑠 is the amount of substrate, 𝑂𝑒𝑓 is the emission factor of pyrolysis processes. Eq(8) is applied to identify the break-even point/ boundary, where 𝑃𝑟𝑜𝑓𝑖𝑡𝑡𝑜𝑡𝑎𝑙 (𝑖) = 𝑃𝑟𝑜𝑓𝑖𝑡𝑡𝑜𝑡𝑎𝑙 (𝑗). 𝐺𝐻𝐺𝑝𝑟𝑖𝑐𝑒 is identified by varying the 𝐶𝐼. 𝐺𝐻𝐺𝑝𝑟𝑖𝑐𝑒 = 𝐸𝑒𝑛𝑒𝑟𝑔𝑦𝑗 + 𝐸𝑏𝑖𝑜𝑐ℎ𝑎𝑟𝑗 − 𝑂𝐶𝑗 − 𝐸𝑒𝑛𝑒𝑟𝑔𝑦𝑖 − 𝐸𝑏𝑖𝑜𝑐ℎ𝑎𝑟𝑖 + 𝑂𝐶𝑖 𝐴𝑚𝑜𝑢𝑛𝑡𝑅𝐸𝑖 × 𝐶𝐼 − 𝑃𝑜𝑝,𝑖 + 𝐴𝑚𝑜𝑢𝑛𝑡𝐵𝑖 × 𝑆𝐹 − 𝐴𝑚𝑜𝑢𝑛𝑡𝑅𝐸𝑗 × 𝐶𝐼 − 𝑃𝑜𝑝,𝑗 + 𝐴𝑚𝑜𝑢𝑛𝑡𝐵𝑗 × 𝑆𝐹 (8) The generic steps to construct the graphical decision tool BBDM are as follows: (i) Define the functional unit (can be based on carbon content, amount, e.g. 1 t or 1 ha of the substrate) (ii) Define the assessed scenarios (e.g. different pyrolysis setting, type of substrate, the ratio of recovered products), system boundary and assumptions. (iii) Collect the required data. (iv) Define replaceable energy (Optional). 𝐴𝑚𝑜𝑢𝑛𝑡𝑅𝐸 in Eq(5) is equal to the replaceable energy if the recovered/generated energy from pyrolysis are all to displace the current energy generation practice. In some cases, the recovered energy is used to fulfil the increasing energy demand where the current energy generation practice (e.g. fossil fuel) is not being replaced. In this case, there is none or only a partial avoidance/unburdening emission (𝐴𝑚𝑜𝑢𝑛𝑡𝑅𝐸 × 𝐶𝐼). (v) Apply the data to the Eq(8) to identify the GHG price. (vi) GHG price is plotted on the y-axis, and GHG intensity of electricity serves as the x-axis. A graph of the reciprocal function (𝑦 = 1 𝑥 or 𝑦 = − 1 𝑥 ) is obtained, see Figure 1. Figure 1: Generic example of the decision-making tool BBDM (vii) Assign the area (label and colour). The identified border/line divides the space and suggests that under a given GHG intensity and the known GHG price, which accessed scenarios would the highest total profit (economic and environmentally). Figure 1 shows a generic example of the decision-making tool. The blue area is assigned to Case i, suggests Case i provided a high profit and lower emission 21 than Case j in the blue area. The presented scale is adjusted by removing the area where GHG price < 0 (below the blue dotted line in Figure 1) as the GHG price is always ≥0. 3. Case study The type of substrates/biomass, accessed in this study, are energy crops and agricultural residues. The selected treatment option is pyrolysis, specifically slow pyrolysis system. Figure 2 shows the overall framework of the case study. The emission released during the field operation, including the use of fertiliser for energy crops are considered in this case study. Figure 2: The overall assessment framework of the case study Table 1 presents the assessed scenarios. The functional unit applied in this case study is 1 ha of the substrate (switchgrass or wheat straw). Table 2 shows the applied data. This study assumes all the recovered energy is qualified to replace the conventional energy generation rather than use to fulfil the increasing demand which is not eligible for avoided/unburdening emission accounting, see Section 2 Step iv. The second assumption is that there is available land which is suitable for biochar application. It should be noted that the graphical tool of this case study is based on the specification inputs reported by Gaunt and Lehmann (2008). In practice, localised data inputs are required to feed into the proposed methodology for a customised graphical tool and solutions. Table 1: Scenarios description Scenario Description 1 Switchgrass (energy crop) + Pyrolysis optimised for energy (burn) 2 Switchgrass (energy crop) + Pyrolysis optimised for biochar (bury) 3 Wheat straw (agricultural residue) + Pyrolysis optimised for energy (burn) 4 Wheat straw (agricultural residue) + Pyrolysis optimised for biochar (bury) Table 2: Data to construct a graphical decision-making tool. Extracted from Gaunt and Lehmann (2008) Unit Scenario 1 Scenario 2 Scenario 3 Scenario 4 Amount of electricity MWh ha-1 y-1 17.98 13.67 11.22 8.52 Operating cost USD ha-1 y-1 205.90 205.90 27.81 27.81 Emission from operating process kg ha-1 y-1 6.62 11.41 8.20 14.20 Sequestrated emission kg ha-1 y-1 - 3,768 - 2,119 Price of electricity = 22.4 USD MWh-1 and Price of biochar = 0.047 USD kg-1 4. Results and discussions Figure 3 shows the developed BBDM tool to compare (a) Scenario 1 and 2 as well as (b) Scenario 3 and 4. When there is no GHG charge (GHG price = 0), all the circumstances (energy crop or agricultural residue; small or large GHG intensity) suggest pyrolysis optimised for energy production. Burning the recovered products for energy provides a higher profit. The selection shift to pyrolysis optimised for biochar production with the increase of GHG price (> 0.03 USD/kg CO2eq for switchgrass, >0.01 USD/kg CO2eq for wheat straw), where the biochar is applied to the soil (bury). The GHG price is essential to encourage the application of biochar for GHG footprint reduction. However, 22 burning is preferable with increasing GHG intensity. This is due to the higher footprints offset by displacing the dirty electricity grid mix (higher GHG intensity) with energy generated from pyrolysis. The applicability of the developed tool for decision-making can be demonstrated by using Country A (200 g CO2eq/kWh) and Country B (1,000 g CO2eq/kWh) as an example, at GHG price of 0.025 USD/kg (Plumper and Popovich, 2019). Figure 3a suggests the switchgrass in Country A and B is more suitable for energy production (burn) in order to have a higher total profit (earnings from selling the energy and the GHG credit). Figure 3b suggests the wheat straw in Country A should be utilised for biochar production but energy generation for Country B. To encourage the production of biochar (bury) in Country B, as illustrated in Figure 3b, the GHG price have to be increased, e.g. to 0.04 USD/kg. Figure 3: Graphical decision-making tool (burn or bury) for (a) Scenario 1 vs 2 and (b) Scenario 3 vs 4 Figure 4 shows the impact of substrate types to the break-even based decision-making tool. At 200 g CO2eq/kWh (Country A), wheat straw is a better substrate for pyrolysis unless the GHG price is set to be higher than 0.02 USD/kg CO2eq, see Figure 4a. Switchgrass, the dedicated biomass, has a higher net GHG footprint compared to wheat straw (agricultural residue) due to the burdening effect of field production in growing the switchgrass. However, switchgrass has a higher net GHG footprint with increasing GHG intensity, as reflected in Figure 4a (preferable options than wheat straw), due to the higher amount of energy from pyrolysis to displace the high GHG intensity energy mix. Figure 4: Graphical decision-making tool (types of substrate) for (a) Scenario 1 vs 3 and (b) Scenario 2 vs 4 5. Conclusions The BBDM tool has been proposed as a means to determine the suitable biomass utilisation with highest possible profit and lowest GHG emission for a particular context with known GHG intensity and a defined GHG price. The tool provides rapid and effective decision support capability via an intuitive graphical display, in contrast to mathematical programming models. It is feasible for the comparison of different treatment options (e.g. gasification, pyrolysis and other waste to energy), technologies (pyrolysis in different setting e.g. temperature), utilisation (energy, soil amendment, activated carbon), types of substrate and useful to capture 23 the impacts contributed by the changes of GHG intensity and GHG price. The results of the pyrolysis case study show that burn is generally a preferable decision (at GHG price = 0) especially with the increase of GHG intensity, for both switchgrass and wheat straw. At GHG intensity = 200 g CO2eq/kWh and GHG price = 0.025 USD/kg, pyrolysis of wheat straw is suggested to be optimised for biochar production and application to the soil (bury). However, at GHG intensity = 1,000 g CO2eq/kWh, pyrolysis of wheat straw is suggested to be optimised for energy generation unless the GHG price is increased, e.g. to 0.04 USD/kg. Future studies should consider (i) a better accounting framework of biogenic and non-biogenic carbon as carbon neutrality cannot be assumed for all biomass energy a priori (ii) additional footprints (e.g. NOx, SO2, particulate matter) (iii) land availability and suitability for biochar application to improve the fidelity of the decision tools. The BBDM tool has a broader potential and flexibility for an extended application in decision-making. The presented case study shows one of the applications (biomass utilisation/treatment) however the similar concept/foundation can be applied to the different field; for example, transportation modes and fuel selection by using load and distance as the axes. The BBDM tool transforms the treatment/utilisation selection problem into an easily understandable format from which arises sound solutions. Acknowledgements The EU project Sustainable Process Integration Laboratory – SPIL, funded as project No. CZ.02.1.01/0.0/0.0/15 003/0000456, by Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research in the collaboration agreement with De La Salle University based on SPIL project have been gratefully acknowledged. References Brown T.R., Wright M.M., Brown R.C., 2011. Estimating profitability of two biochar production scenarios: slow pyrolysis vs fast pyrolysis. Biofuels, Bioproducts and Biorefining, 5(1), 54-68. Fidel R.B., Laird D.A., Parkin T.B., 2019. Effect of biochar on soil greenhouse gas emissions at the laboratory and field scales. Soil Systems, 3(1), 8. Gaunt J.L., Lehmann J., 2008. 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