Bio-methane and bio-methanol co-production from biogas: A profitability analysis to explore new sustainable chemical processes

Depósito de Investigación de la Universidad de Sevilla https://idus.us.es/ This is an Accepted Manuscript of an article published by Elsevier in Journal of Cleaner Production, Vol. 265, on August 2020, available at: https://doi.org/10.1016/j.jclepro.2020.121909 Copyright 2019 Elsevier. En idus Licencia Creative Commons BY-NC-ND 1 Bio-methane and bio-methanol co-production from biogas: A profitability analysis to explore new sustainable chemical processes Francisco M. Baena-Moreno a,b*, Laura Pastor-Pérez b, Qiang Wang c, T.R. Reina b*. a Chemical and Environmental Engineering Department, Technical School of Engineering, University of Seville, C/ Camino de los Descubrimientos s/n, Sevilla 41092, Spain b Department of Chemical and Process Engineering, University of Surrey, GU2 7XH Guildford, United Kingdom c College of Environmental Science and Engineering, Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing 100083, PR China *Corresponding authors. E-mail addresses: f[email protected] (Francisco M. Baena-Moreno) / t.ramirez[email protected] (T.R. Reina) Abstract Herein a potential synergy between biogas upgrading and CO2 conversion to biomethanol is investigated. This novel idea arises as an alternative path to the traditional biogas – to – bio-methane route which involves CO2 separation. In this work a technoeconomic analysis of the process was performed to study the profitability for potential investors. A total of 15 scenarios were analysed. Different biogas plant sizes were examined as baseline scenarios: 100, 250, 500, and 1000 m3/h. Furthermore the potential effect of governmental incentives through bio-methane subsidies (feed-in tariffs 2 and investment percentage) was studied. Finally a sensitivity analysis was developed to study the effect of keyparameters. The results of the baseline scenarios demonstrated that not profitable results can be obtained without subsidies. Bio-methane subsidies as feed-in tariffs proved to be effective for the 500 and 1000 m3/h plant sizes. For a feed-in tariff subsidy of 40 €/MW, 500 m3/h biogas production plants are remarkably profitable (net present value equal to 3106 k€). Concerning 1000 m3/h biogas production plants, 20 €/MW of subsidies as feed-in tariffs gives similar net present value result. Our results point out that only big biogas production can produce bio-methanol at profitable margins under 90-100% of investment subsidied. The sensitivity analysis showed that electricity, natural gas and bio-methanol price can affect considerably to the overall profitability, converting predicted positive cases in negative scenarios. Highlights - Novel strategy for synergizing bio-methane and bio-methanol production - Profitability evaluation of the novel green process - Analysis of subsidies effect on the process - Small biogas plants are unprofitable without remarkable subsidies - Large plants are deemed profitable when affordable subsidies are implemented Keywords Bio-methane production; Bio-Methanol; Biogas upgrading; CO2 utilisation; Green- Chemicals; Waste valorization; 3 1. Introduction The growthing trend of renewable energy consumption has remarkably increased during the last decades (Destek and Sinha, 2020; Zafar et al., 2019). This acceleration responds mainly to two factors: depleption of fossil fuels; and greater concentration of greenhouse gases (GHG) in the atmosphere. This scenario has motivated the scientific community to intensify the study of sustainable development policies (Bilgili et al., 2017; García- Gusano et al., 2017). The movement of current government towards more environmental-friendly energy consumption policies relies on two key factors: maturity of technology and economy. Renewable energy technologies have proved their readiness for replacing traditional fossil fuels (Mas’ud et al., 2015; Mondal et al., 2016). Indeed, the technology maturity explains the above mentioned growthing trend on renawable sources. Nevertheless, sustainable energy production costs are still barely competitive in comparison with traditional energy sources (Amran et al., 2020; Gong et al., 2019). Among the renewable energy platforms, biogas production from biomass stands out due to its double-fold benefit: (i) avoiding the treatment of large amount of organic wastes; and (ii) production of a gas product with an acceptable calorific value. Biogas is essentially composed by CH4 (60%) and CO2 (40%) (Baena-Moreno et al., 2019a), although it may also contain impurities such as H2S, CO, H2O, N2, H2, NH3 and siloxanes (le Saché et al., 2019). Biogas can be used in a combined heat and power (CHP) unit for electricity production. This process releases the CO2 contained in the biogas to the atmosphere, making this option a not 100% green electricity production path. Another interesting alternative is to upgrade biogas via CO2 scrubbing, obtaining a high purity bio-methane that can partially replace natural gas minimising the consumption of natural resources (Baena-Moreno et al., 2019b, 2019c; Bose et al., 2019). However, the upgrading costs still hampers its implementation at commercial scale. Only in those countries where governmental incentives are offered, bio-methane production plants are more abundant. There are two kind of governmental incentives typically applied: bio- 4 methane subsidies through feed-in tariffs or via investment percentage. The first one put an extra-price for each megawatt injected to the natural gas grid, whereas the second one provides to the investor a percentage of the total investment cost. Nevertheless, there is much debate about the biogas incentives to favour the technology take off. Indeed, only a few studies can be found dealing with these kind of analyses (Cucchiella et al., 2019; Cucchiella and D’Adamo, 2016; D’Adamo et al., 2019; Ferella et al., 2019). CO2 utilisation from biogas mixture is a promising alternative that may help to circumvent biogas upgrading costs (Baena-Moreno et al., 2019d; Baena-Moreno et al., 2019e; Bennett et al., 2014; Fernández-Dacosta et al., 2018). Multiple CO2 capture and utilisation technologies can be found in the literature (Baena-moreno et al., 2018; Le Saché et al., 2018; Price et al., 2019; Vega et al., 2020; N. Zhang et al., 2020), wherein for example the production of carbonates, syngas, hydrates or methanol take a leading role (Fabián-Anguiano et al., 2019; Lee et al., 2020; Samimi et al., 2018; Stroud et al., 2018; Z. Zhang et al., 2020). Up till now, the implementation of CO2 utilisation from flue gases at industrial scale is not feasible since the worldwide amount of CO2-derived products would be much higher than the market needs. Nevertheless, in the case of biogas upgrading, the amount of CO2 available as raw material (and therefore the CO2- derived products) could be fittable in the market needs. Moreover, the overall economic performance of the biogas upgrading process could be balanced by selling the CO2- derived product. Regrettably, there is a lack of techno-economic evaluations of biogas upgrading and CO2 utilisation costs in the literature. The work herein presented aims to encourage researchers in this area to fill this knowledge gap and contribute to the successful development of biogas technologies. Our proposal deals with the profitability analysis of biogas upgrading to produce bio-methane and the simultaneous production of biomethanol from the extracted CO2. Under these premised and to the best of authors’ knowledge, no works have been performed to date in this direction. Bio-methanol 5 production from CO2 was selected since methanol is an important commodity and its production process via CO2 hydrogenation has been previously addressed (Pérez-Fortes et al., 2016a; Van-Dal and Bouallou, 2013). The overall process scheme is represented in Figure 1. In the process three different stages can be identified: biogas upgrading, electrolysis and bio-methanol production. Biogas upgrading allows the production of biomethane which can be injected into the natural gas grid for replacing natural gas. On the other hand, a high purity CO2 stream is obtained from the upgrading stage. Obtaining bio-methanol from CO2 requires a hydrogen source. In our approach, hydrogen is generated via electrolysis using renewable energy resources. Finally, the last stage aims to combine both the CO2 obtained in the biogas upgrading stage and the hydrogen produced via water electrolysis to obtain bio-methanol as value added ending product. Nowadays, the global methanol production reaches 140 Mt (Statista, 2020), of which 90% is produced from natural gas (Dalena et al., 2018). Natural gas reforming to methanol includes three main steps which are synthesis gas production, syngas conversion into crude methanol and methanol purification through a complex distillation system (Dalena et al., 2018). Even though the conversion – flow rates of this path are high, methanol from natural gas reforming entails a significantly high carbon footprint (Li et al., 2018). Thus, renewable alternatives for methanol production needs to be explored (Rivarolo et al., 2016). In this sense, despiteour proposal deals with lower plant capacities, producing methanol from biomass is still appealing since it might remarkably reduce the environmental impact compared to the traditional fossil fuel route. 6 Figure 1. Biogas upgrading and bio-methanol production from CO2 process scheme. The main goal of our work is the economic evaluation of the process described in Figure 1 in terms of profitability. Our study was aimed to cover different casuistics, as for example different plant sizes or natural gas – electricity prices. Thus, our work provides useful data for researchers exploring this topic. Profitability study was performed through the well-establised Discounted Cash Flow (DCF) method. DFC method was applied incluiding a exhaust analysis of the typical indicators: Net Present Value (NPV), Discounted Payback Time (DPBT), Internal Rate of Return (IRR) and Profitability Index (PI). Four biogas production plant sizes have been examined. The sizes selected were in agreement with the classification presented in a previous work (Cucchiella and D’Adamo, 2016): small plants (100 m3/h, 250 m3/h), medium plants (500 m3/h), and big plants (1000 m3/h). The hypothetical bio-methane & bio-methanol production plant was assumed to be built in Spain for sake of data selection (i.e. natural gas price, electricity price and labour cost). The influence of governmental incentives through bio-methane 7 subsidies on profitability results are also analysed. Both existing types of subsidies (feedin tariffs and investment percentage) were evaluated. This point aims to serve as a useful guide for policy-makers because it allows: 1) Estimating the subsidies values needed to reach profitability in bio-methane production plants; and 2) Focusing on bio-methane production plant sizes which are more prone to be profitable. Furthermore, an exhaust sensitivity analysis to examine the influence of the main parameters on profitability results was performed in agreement with the scenarios explained in section 2.3. 2. Methodology 2.1 Process description and modelling The process described in Figure 1 was divided into three differenciated stages which are biogas upgrading, electrolysis and bio-methanol production. It was assumed that the biogas production plant was already operating and therefore there was no need of capital investment. Moreover, it was assumed an ideal biogas composition of 60% CH4 – 40% CO2 composition. Biogas upgrading stage produces bio-methane, and the separated CO2 is fed to the bio-methanol production stage. Membrane technology is one of the most promising technologies for biogas upgrading since it allows to reduce the investment cost and obtain a high purity bio-methane stream (Cucchiella et al., 2019). Therefore, membrane technology was selected for the purpose of our work. As there are a wide variety of data available in the literature for the sizes proposed (Cucchiella et al., 2019; Cucchiella and D’Adamo, 2016), they were directly implemented in the economic model. The electrolysis plant selected for the production of hydrogen was an alkaline electrolyser (AE) since it is the one recommended by suppliers because of its commercial availability (Bolat and Thiel, 2014). In agreement with previous works, a small scale electrolyser of 0.6 MW is enough to feed up the needs of the bio-methanol production plant (Pérez-Fortes et al., 2016b). Regarding the bio-methanol production stage, no 8 economic data were found for the sizes herein considered. Therefore, this stage was first modelled techno-economically in agreement with data previously published for CO2 hydrogenation to bio-methanol (Pérez-Fortes et al., 2016a; Van-Dal and Bouallou, 2013). Further information of the data and the process layout used are detailed in:“Appendix I. Modelling and economic data of bio-methanol production stage”. 2.2 Economic model assessment Eqs. (1)-(4) describe the profitability indicators selected for the development of DFC method. DFC method is based on the difference between the cash inflows (It) and the cash outflows (Ot). The discount rate parameter (rd) has the function of reflecting the time effect. More parameters included in these equations are: the project lifetime (n); the present time (t); and the investment cost (Cinv). It calculation is described in Eq. (5) and it consists on: bio-methane sale (Rbio-methane); governmental incentives obtained through bio-methane subsidies (Rsubsidies) (only in the scenarios needed); and bio-methanol sale (Rbio-methanol). Rbio-methane is obtained through the bio-methane produced (Qbio-methane) and the natural gas price (pNG) (Eq. (6)). In those scenarios where bio-methane subsidies are considered, they are calculated considering the specific price of subsidy (psubsidies) (Eq. (7)). A similar ecuation is employed for obtaining the revenues of bio-methanol selling (Eq. (8)), which depends on bio-methanol produced (Qbio-methanol) and bio-methanol price (pbio−methanol). NPV = ∑𝐼𝑡−𝑂𝑡 (1+𝑟𝑑)𝑡 𝑛 𝑡=0 (1) ∑𝐼𝑡−𝑂𝑡 (1+𝑟𝑑)𝑡 DPBT 𝑡=0 = 0 (2) ∑𝐼𝑡−𝑂𝑡 (1+IRR)𝑡 𝑛 𝑡=0 = 0 (3) 15 the contrary, electricity cost clearly increases from 12% (100 m3/h) to 49% (1000 m3/h). This fact is caused by two actions: there is a higher biogas flow to be treated; and the need of producing more hydrogen in the electrolysis stage to fulfil the bio-methanol production requirements. This makes electricity costs to be higher than M&O costs for the largest capacities. It is also the reason why NPV and PI show different tendencies. Figure 3. Cost breakdown for each biogas plant size. Regarding the revenues, injecting bio-methane to the natural gas grid supposes the 72% of the total whereas selling the produced bio-methanol represents the 28%. The revenues for selling bio-methanol are lower than the bio-methanol production costs for a typical year. Therefore, the production of bio-methanol from the separated CO2 does not seems to be profitable in the biogas context. Hence one may argue that there are no reasons to synergise CO2 utilization and biogas upgrading to bio-methane under the studied circumstances. Nevertheless, as explained in the Introduction section, two kinds of governmental subsidies are typically applied. Bio-methane subsidies as feed-in tariffs Investment M&O Electricity Insurance Labour Others 0 5 10 15 20 25 30 35 40 Percentage (%) 100 m3/h Investment M&O Electricity Insurance Labour Others 0 5 10 15 20 25 30 35 Percentage (%) 250 m3/h Investment M&O Electricity Insurance Labour Others 0 5 10 15 20 25 30 35 40 Percentage (%) 500 m3/h Investment M&O Electricity Insurance Labour Others 0 10 20 30 40 50 1000 m3/h Percentage (%) 16 would not reduce the difference between bio-methanol revenues & costs, but biomethane subsidies as percentage of investment can balance the benefits obtained in this stage. We will analyse these inputs in section 3.3. 3.2 Influence of governmental incentives 3.2.1 Subsidies through feed-in tariffs Governmental incentives through feed-in tariffs are a common tool of policy-makers to boost the use of renewable energy. These policies are well-extended both in already developed countries (i.e. Germany (Baur and Uriona M., 2018; Böhringer et al., 2017) or Italy (Patrizio et al., 2017)), and in developing countries (i.e. South Africa (Eberhard and Kåberger, 2016) or Sri Lanka (Shi et al., 2018)). The reason is simple: the use of renewable energy reduces the energy dependence from big energy producers. Apropos the subsidies for bio-methane production some European countries have already implemented a strong strategy to utilise this renewable energy vector. For example, Austria bio-methane’s subsidies are around 12.51–16.51 €/MW while Slovakia subsidies’ are around 10.75 €/MW (Pablo-Romero et al., 2017). From the profitability analysis carried out in section 3.1 of the present work, it seems clear that the existence of subsidies is required. As discussed in the Introduction section, the value of subsidies is a topic of discussion and it depends on multiple country-dependant parameters (i.e. national salaries, natural gas price and electricity price). In this sense, this section provides an estimation of the feed-in tariffs subsidies that would be needed for reaching profitability by plant size. Figure 4 showcases the NPV evolution with subsidies value for each plant size and Table 4 collects the PI results obtained. 17 Figure 4. NPV evolution of scenarios 5-8 with bio-methane subsidies through feed-in tariffs. Table 4. PI results obtained for bio-methane subsidies through feed-in tariffs. Scenarios 5-8. Subsidies (€/MW) 100 m3/h 250 m3/h 500 m3/h 1000 m3/h 0 -2.74 -2.63 -2.48 -2.21 10 -2.55 -2.22 -1.67 -0.77 20 -2.35 -1.81 -0.86 0.67 30 -2.16 -1.40 -0.05 2.1 40 -1.97 -0.98 0.76 3.54 50 -1.77 -0.57 1.56 4.98 60 -158 -0.16 2.37 6.42 70 -1.38 0.25 3.18 7.86 80 -1.19 0.67 3.99 9.3 The subsidies values chosen for performing the analysis were in a wide range (10-80 €/MW) to provide a wider perspective. As can be seen from the results herein presented, the subsidies effect on profitability is strongly dependent on the biogas plant size. This dependence is boosted when subsidies are increased. For example Figure 4 reveals an almost null difference between small plants and big plants for the lowest subsidies, 020 40 60 80 -20000 -10000 0 10000 20000 30000 40000 50000 NPV (k€) Subsidies (€/MW) 100 m3/h 250 m3/h 500 m3/h 1000 m3/h 18 whereas for high subsidies the difference is remarkable. Indeed, the smallest plants (100 m3/h, scenario 5) does not reach profitable results even at 80 €/MW. Increasing the size plant to 250 m3/h (scenario 6) would not solve the profitability problems since a subsidy of around 64 €/MW would be needed, which is hardly tolerable. Reasonable values for subsidies as feed-in tariffs are 10-40 €/MW, altought this may depend on several factors. For these values only medium and big plants would be profitable. For 500 m3/h (scenario 7) the first NPV positive value is obtained for 30.66 €/MW of subsidie. For this value, a DPBT of 20 years would be needed to recover the investment. For the same plant size at 40 €/MW, 3106 k€ of NPV are obtained, with a PI of 0.76 and a DPBT of 5 years. These conditions are much more appealing for investors. Concerning the 1000 m3/h biogas production plant (scenario 8), 20 €/MW represents a compromise solution between investors and subsidies from governements. For this subsidies value, a PI value of 0.67 and NPV of 3095 k€ are obtained, which is both attractive and risky for the investor since a fluctuation for example in electricity price could reverse the benefits. Risky scenarios related to electricity, natural gas and bio-methanol prices are included in section 3.3. 3.2.2 Subsidies through investment percentages The other type of governmental incentives for rewarding the production of renewable energy are subsidies through a percentage of investment. Albeit this kind of subsidies are unfrequent compared to feed-in tariffs, they could be helpful in those cases where the investment is the biggest part of the overall cost. Figure 5 and Table 5 present the profitability results of this work for the scenarios 9-12. 19 Figure 5. NPV evolution of scenarios 9-12 with bio-methane subsidies through percentage of investment. Table 5. PI results obtained for bio-methane subsidies through percentage of investment. Scenarios 9-12. Subsidies (%) 100 m3/h 250 m3/h 500 m3/h 1000 m3/h 0 -2.74 -2.63 -2.48 -2.21 10 -2.77 -2.65 -2.47 -2.18 20 -2.80 -2.67 -2.47 -2.13 30 -2.85 -2.69 -2.46 -2.08 40 -2.90 -2.73 -2.46 -2.01 50 -2.99 -2.77 -2.45 -1.92 60 -3.11 -2.84 -2.4 -1.77 70 -3.32 -2.94 -2.43 -1.53 80 -3.73 -3.18 -2.39 -1.05 90 -4.96 -3.86 -2.3 0.39 100 n.a. n.a. n.a. n.a. *n.a is a direct consequence of the PI formula. Please see section 2.2, Eq. (4) for a better understanding Scenarios 9-12 (investment percentage) yield worse results than scenarios 5-8 (subsidides percentage). This observation confirms the reason why feed-in tariffs are much more frequently implemented than percentage of investment. Moreover, the use of feed-in tariffs forces the investor to run the bio-methane production plant to mantain 020 40 60 80 100 -12000 -10000 -8000 -6000 -4000 -2000 0 2000 100 m3/h 250 m3/h 500 m3/h 1000 m3/h NPV (k€) Percentage of investment (%) 20 subsidides. Indeed, the overall efficiency of the plant is projected to be as higher as possible to obtain the maximum quantity of bio-methane. This way we could substantially improve the overall performance of this production plants. Focusing on the results obtained in scenarios 9-12, NPV values (Figure 5) are only positive for the highest percentage of investment for medium and big plant sizes. From the results obtained in Table 5 a curious discussion arises. As the percentage of investment subsidied increases, PI decreases for small and medium plant sizes (scenarios 9 and 10). This fact is due to the mathematical formula which defines PI (see Eq. (4), section 2.2). In these cases the investment decrease is higher than the NPV increase in percentage, which makes the approach less profitable at higher subsidides (according to PI indicator). For medium and bigger plants this unusual behavior is corrected as seen in Table 4. 3.3 Sensitivity analysis Finally and to cover as many economic variables as possible, a sensitivity analysis of selected parameters was performed. The parameters studied were electricity price (scenario 13), natural gas price (scenario 14), and bio-methanol price (scenario 15). These parameters were chosen in agreement with the results obtained in the baseline scenarios (scenarios 1-4), which highlighted the importance of electricity costs and revenues. Figures 6, 7 and 8 show the NPV results obtained for scenarios 13, 14 and 15. As indicated in Table 2, the sensitivity analyses were performed on 500 m3/h and 1000 m3/h biogas production plants, assuming a feed-in tariffs subsidy of 20 €/MW. The reason is to examine cases which are close to a zero NPV value and then discuss the risk – opportunities of certain parameters. 21 Figure 6. Influence of electricity price on NPV. Scenario 13. Electricity costs were proved to be very important in scenarios 3 and 4 analysed in section 3.1. Inasmuch electricity price suffers important variations yearly, Figure 6 shows the effect of this parameter on the NPV in our combined bio-methane/bio-methanol production route. A reduction of 50% in the electricity price makes 500 m3/h plants almost reach profitability, whereas an increase of 50% would reverse to negative 1000 m3/h biogas production plants. Therefore, the influence of electricity price is very strong and some measures could be taken by policy-makers to prevent negative results in cases of electricity market price oscillation. Stability on electricity price would be the desirable scenario for investors to reduce risks. 0,065 0,13 0,195 -8000 -6000 -4000 -2000 0 2000 4000 6000 8000 10000 12000 NPV (k€) Electricity price (€/kWh) 500 m3/h 1000 m3/h 22 Figure 7. Influence of natural gas price on NPV. Scenario 14. As for the influence of the natural gas price, an obvious reverse trend can be observed in Figure 7. A notable negative impact on NPV value for all the cases is observed when natural gas price is reduced. In some cases this reduction results in negative NPV as in scenario 14 depicted in Figure 7. Thereagain investors may lose or spark their apetetive for this combined bio-methane/bio-methanol technology depending on natural gas market oscilations. The policies and prices for natural gas are closely linked to international relationships and they are quite difficult to predict for a 20-year future. Moreover, the countries which lead the natural gas production (United States, Russia and Iran) have evidenced political differencies in many occasions. For intermediary countries which receive natural gas from producer countries, this is a hardly controllable factor. 9,24 18,48 27,72 -8000 -6000 -4000 -2000 0 2000 4000 6000 8000 10000 NPV (k€) 500 m3/h 1000 m3/h Natural gas price (€/MW) 23 Figure 8. Influence of bio-methanol price on NPV. Scenario 15. Methanol price has varied considerably during the last years, presenting constant fluctuations. Even though 350 €/t is a representative value, methanol price has reached 450 €/t and in some cases this value has diminished to 225 €/t (Methanex, 2019). For this very reason a sensitivity analysis of bio-methanol price was carried out in our work, which corresponds to scenario 15. The reference values were ±50% of 350 €/t for sake of analyzing the worst case (175 €/t) and the best case (525 €/t). As depicted in Figure 8 no profitable results are reached for a 500 m3/h plant. Even at a bio-methanol price of 525 €/t and considering 20 €/MW of subsidies as feed in tariffs, NPV is -2039 k€. A critical thought against this result can be extracted since methanol price is strongly unstable. The integrated process for biogas upgrading and bio-methanol production herein analysed may be more appropriated for the biggest capacitiy plants. For the previous parameters, some political actions could be taken to foster investors’ appetite, but 175 350 525 -4000 -2000 0 2000 4000 6000 NPV (k€) 500 m3/h 1000 m3/h Methanol price (€/t) 24 methanol context is international and its price is linked to the raw materials. Thus, even if there are political interests on this kind of renewable energy production plants, investors would always carry a risk when supporting this initiative. Alternatively, government incentives could be linked to methanol price for medium plants. Thus, higher subsidies would be provided when methanol price falls. This fact could promote the implementation of this renewable production strategy in rural areas. On the other hand, larger biogas plants would be profitable even at the lowest methanol price. 4. Conclusions We have performed a profitability analysis to upgrade biogas and simultanouesly produce bio-methanol in order to assess the economic interest of this renewable route for potential investors. The following points summarize the conclusions of our work: - The results obtained for the baseline scenarios are not commercially appealing and none of the biogas plant sizes studied are deemed profitable. The cost analysis showed domination of the investment cost and M&O for the smallest plants, whereas electricity cost was the highest one for medium and big plants. - Feed-in tariffs does not seem to solve the profitability handicaps for small plants. Nevertheless, this kind of subsidy can be a valid solution for medium-large biogas plants. - Concerning the effect of bio-methane subsidies as percentage of investment – this action is not as effective as feed-in tariffs. Our results invite to reflect about new policies to balance the potential negative impacts that these parameters may have over the profitability of the process analysed. Overall, our approach for an integrated green process to generate bio-methane & bio-methanol would be profitable under the studied circumstances for large biogas plants when smallmedium subsidies are applied. Future research will be leaded to study the influence of 31 https://doi.org/10.1016/j.jclepro.2018.11.073 Fernández-Dacosta, C., Stojcheva, V., Ramirez, A., 2018. Closing carbon cycles: Evaluating the performance of multi-product CO2 utilisation and storage configurations in a refinery. J. CO2 Util. https://doi.org/10.1016/j.jcou.2017.11.008 García-Gusano, D., Iribarren, D., Garraín, D., 2017. Prospective analysis of energy security: A practical life-cycle approach focused on renewable power generation and oriented towards policy-makers. Appl. Energy. https://doi.org/10.1016/j.apenergy.2017.01.011 Gong, D.C., Kao, C.W., Peters, B.A., 2019. Sustainability investments and production planning decisions based on environmental management. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2019.03.256 le Saché, E., Johnson, S., Pastor-Pérez, L., Amini Horri, B., Reina, T., 2019. Biogas Upgrading Via Dry Reforming Over a Ni-Sn/CeO2-Al2O3 Catalyst: Influence of the Biogas Source. Energies 12, 1007. https://doi.org/10.3390/en12061007 Le Saché, E., Santos, J.L., Smith, T.J., Centeno, M.A., Arellano-Garcia, H., Odriozola, J.A., Reina, T.R., 2018. Multicomponent Ni-CeO2 nanocatalysts for syngas production from CO2/CH4 mixtures. J. CO2 Util. https://doi.org/10.1016/j.jcou.2018.03.012 Lee, J., Ryu, K.H., Ha, H.Y., Jung, K.D., Lee, J.H., 2020. Techno-economic and environmental evaluation of nano calcium carbonate production utilizing the steel slag. J. CO2 Util. https://doi.org/10.1016/j.jcou.2019.12.005 Leonzio, G., 2017. Optimization through response surface methodology of a reactor producing methanol by the hydrogenation of carbon dioxide. Processes. https://doi.org/10.3390/pr5040062 Li, J., Ma, X., Liu, H., Zhang, X., 2018. Life cycle assessment and economic analysis of methanol production from coke oven gas compared with coal and natural gas routes. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2018.02.100 Mas’ud, A.A., Vernyuy Wirba, A., Muhammad-Sukki, F., Mas’ud, I.A., Munir, A.B., Md Yunus, N., 2015. An assessment of renewable energy readiness in Africa: Case study of Nigeria and Cameroon. Renew. Sustain. Energy Rev. https://doi.org/10.1016/j.rser.2015.06.045 32 Methanex, 2019. Methanol price [WWW Document]. URL https://www.methanex.com/sites/default/files/methanol-price/MxAvgPrice_Dec 27%2C 2019.pdf Mondal, M.A.H., Hawila, D., Kennedy, S., Mezher, T., 2016. The GCC countries RE- readiness: Strengths and gaps for development of renewable energy technologies. Renew. Sustain. Energy Rev. https://doi.org/10.1016/j.rser.2015.10.098 Pablo-Romero, M. del P., Sánchez-Braza, A., Salvador-Ponce, J., Sánchez-Labrador, N., 2017. An overview of feed-in tariffs, premiums and tenders to promote electricity from biogas in the EU-28. Renew. Sustain. Energy Rev. https://doi.org/10.1016/j.rser.2017.01.132 Patrizio, P., Leduc, S., Chinese, D., Kraxner, F., 2017. Internalizing the external costs of biogas supply chains in the Italian energy sector. Energy. https://doi.org/10.1016/j.energy.2017.01.033 Pérez-Fortes, M., Schöneberger, J.C., Boulamanti, A., Harrison, G., Tzimas, E., 2016a. Formic acid synthesis using CO2 as raw material: Techno-economic and environmental evaluation and market potential. Int. J. Hydrogen Energy. https://doi.org/10.1016/j.ijhydene.2016.05.199 Pérez-Fortes, M., Schöneberger, J.C., Boulamanti, A., Tzimas, E., 2016b. Methanol synthesis using captured CO2 as raw material: Techno-economic and environmental assessment. Appl. Energy. https://doi.org/10.1016/j.apenergy.2015.07.067 PORDATA, 2019. Electricity prices for households and industrial users (Euro/ECU) [WWW Document]. Fund. Fr. Man. dos Santos. URL https://www.pordata.pt/en/Europe/Electricity+prices+for+households+and+industri al+users+(Euro+ECU)-1477 (accessed 7.18.19). Price, C.A.H., Arnold, W., Pastor-Pérez, L., Amini-Horri, B., Reina, T.R., 2019. Catalytic Upgrading of a Biogas Model Mixture via Low Temperature DRM Using Multicomponent Catalysts. Top. Catal. https://doi.org/10.1007/s11244-019-01216- 8 Rivarolo, M., Bellotti, D., Magistri, L., Massardo, A.F., 2016. Feasibility study of methanol production from different renewable sources and thermo-economic analysis. Int. J. Hydrogen Energy. https://doi.org/10.1016/j.ijhydene.2015.12.128 33 Samimi, F., Karimipourfard, D., Rahimpour, M.R., 2018. Green methanol synthesis process from carbon dioxide via reverse water gas shift reaction in a membrane reactor. Chem. Eng. Res. Des. https://doi.org/10.1016/j.cherd.2018.10.001 Seider, Seader, Lewin, Widagdo, 2009. product and process Design Principles, Journal of Chemical Information and Modeling. https://doi.org/10.1017/CBO9781107415324.004 Shi, X., Chen, H., Yu, Y., Wen, F., 2018. Development of variable renewable energy policy in developing countries: A case study of Sri Lanka. Int. J. Public Pol. https://doi.org/10.1504/IJPP.2018.090682 Sinnott, R.K., Towler, G., 2013. Chemical Engineering Design, Chemical Engineering Design. https://doi.org/10.1016/C2009-0-61216-2 Statista, n.d. Global production capacity of methanol 2018 & 2030 Published by M. Garside, Nov 7, 2019 The global production capacity of methanol is expected to double in the twelve years from 2018 to 2030, from approximately 140 million metric tons in 2018, to around 2 [WWW Document]. URL https://www.statista.com/statistics/1065891/global-methanol-production-capacity/ Stroud, T., Smith, T.J., Le Saché, E., Santos, J.L., Centeno, M.A., Arellano-Garcia, H., Odriozola, J.A., Reina, T.R., 2018. Chemical CO2 recycling via dry and bi reforming of methane using Ni-Sn/Al2O3 and Ni-Sn/CeO2-Al2O3 catalysts. Appl. Catal. B Environ. https://doi.org/10.1016/j.apcatb.2017.10.047 Van-Dal, É.S., Bouallou, C., 2013. Design and simulation of a methanol production plant from CO2 hydrogenation. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2013.06.008 Vega, F., Baena-Moreno, F.M., Gallego Fernández, L.M., Portillo, E., Navarrete, B., Zhang, Z., 2020. Current status of CO2 chemical absorption research applied to CCS: Towards full deployment at industrial scale. Appl. Energy. https://doi.org/10.1016/j.apenergy.2019.114313 Wiesberg, I.L., de Medeiros, J.L., Alves, R.M.B., Coutinho, P.L.A., Araújo, O.Q.F., 2016. Carbon dioxide management by chemical conversion to methanol: HYDROGENATION and BI-REFORMING. Energy Convers. Manag. https://doi.org/10.1016/j.enconman.2016.04.041 Zafar, M.W., Shahbaz, M., Hou, F., Sinha, A., 2019. From nonrenewable to renewable 34 energy and its impact on economic growth: The role of research & development expenditures in Asia-Pacific Economic Cooperation countries. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2018.12.081 Zhang, N., Pan, Z., Zhang, Z., Zhang, W., Zhang, L., Baena-Moreno, F.M., Lichtfouse, E., 2020. CO2 capture from coalbed methane using membranes: a review. Environ. Chem. Lett. https://doi.org/10.1007/s10311-019-00919-4 Zhang, Z., Liu, Z., Pan, Z., Baena-Moreno, F.M., Soltanian, M.R., 2020. Effect of porous media and its distribution on methane hydrate formation in the presence of surfactant. Appl. Energy. https://doi.org/10.1016/j.apenergy.2019.114373