{"id":1127,"date":"2026-04-12T06:40:55","date_gmt":"2026-04-12T06:40:55","guid":{"rendered":"https:\/\/www.europesays.com\/netherlands\/1127\/"},"modified":"2026-04-12T06:40:55","modified_gmt":"2026-04-12T06:40:55","slug":"portfolio-optimization-for-industrial-cluster-defossilization-in-the-port-of-rotterdam","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/netherlands\/1127\/","title":{"rendered":"Portfolio optimization for industrial cluster defossilization in the Port of Rotterdam"},"content":{"rendered":"

In this section, the proposed portfolio optimization model for the defined scenarios are applied and risk-return relationships are found for the transition from fossil-based feedstocks in the Port pf Rotterdam. The data and parameters provided in \u201cDescription of the Port of Rotterdam<\/a>\u201d section are used, and additional assumptions and constraints (if applicable) specific to each scenario are mentioned accordingly. Therefore, for the market-based economic parameters, values from Tables 3<\/a> and 4<\/a> are fed into the optimization model, and for the re-costed parameters, values from Tables 5<\/a> and 6<\/a> are used. The optimization problems for the various scenarios are modeled in MATLAB solved by a fmincon function within a loop to find the Pareto front on a laptop with an Intel Core i7 1.70 GHz processor and 16 GB RAM. The initialization strategy is considered deterministic, i.e. the total investment budget is initially allocated across all candidate plants proportional to their specific capital costs, ensuring a feasible starting point. The convergence criteria, i.e. optimality tolerance and constraint tolerance are both set to \\(10^{-6}\\).<\/p>\n

Scenario 1: Replacement of fossil-based olefins for ethylene production<\/p>\n

The olefins plant (also known as the naphtha steam cracker) is a critical plant at the beginning of most value chains within the entire cluster of the Port of Rotterdam. Therefore, its defossilization has a significant impact on the overall cluster. During the cracking process, a range of lower hydrocarbons is produced, among which ethylene and benzene are the most prominent in terms of both volume and importance. In this scenario, the potential ACS-based plants for replacing the olefins plant in ethylene production are studied in the context of investment decision-making.<\/p>\n

For the optimization model, two constraints regarding the total investment and demand production of ethylene are defined as follows: <\/p>\n

$$\\begin{aligned}&\\quad \\sum _{n=1}^{3} c_n \\, T_n \\le 1.05 \\,\\,\\text {max}(T_1, T_2, T_3) \\end{aligned}$$<\/p>\n

\n (20a)\n <\/p>\n

$$\\begin{aligned}&\\quad \\sum _{n=1}^3 c_n \\, P_n = D_r \\end{aligned}$$<\/p>\n

\n (20b)\n <\/p>\n

where the first constraint ensures that the total investment does not exceed the capital cost of the most expensive stand-alone plant among the three considered options (as this scenario is replacement), and the second constraint guarantees that the required ethylene production is met (according to (9d<\/a>) and data in Table 2<\/a>). To define the ethylene production demand, two values are used: \\(D_r= 53.55\\frac{\\textrm{kt}}{\\text {month}}\\) (production 1, which is the sum of productions of mathanol-to-olefins and CO2-to ethylene plants at their maximum production capacities), and \\(D_r= 73.18\\frac{\\textrm{kt}}{\\text {month}}\\) (production 2, which is the ethylene production of olefins plant at its maximum production capacity). These two values are used to investigate the impact of the production constraint on the optimization results. Furthermore, since this scenario is the replacement of a fossil-based plant with ACS-based alternatives, the minimum and maximum scaling factors for all options are set to zero and one, respectively.<\/p>\n

The results based on the market-based economic parameters are shown in Fig. 2<\/a>. As shown in Fig. 2<\/a>a, the maximum return is achieved at \\(R = 4.84, \\sigma = 1.77\\), which corresponds to the stand-alone return and risk of the olefins plant. This can also be seen in Figs. 2<\/a>b and c (the rightmost bars), where both the scaling factor and the total investment (in terms of distribution and value) are allocated solely to the olefins plant. When the ACS-based options are included in the portfolio configuration, the overall return decreases due to the negative RoIs of both methanol-to-olefins and CO2-to-ethylene plants. The optimization model configures the portfolio in such a way that reductions in RoI come along with corresponding reductions in investment risk. Consequently, full defossilization does not emerge as an optimal selection due to the negative RoI and high risk associated with ACS-based plants. As can be seen in Fig. 2<\/a>a, the lowest RoI and risk for Production 1 and Production 2 occur at \\(R = 0.68, \\sigma = 1.22\\) and \\(R = -2.58, \\sigma = 1.12\\), respectively. This difference arises because, with a lower ethylene production constraint, the optimization model can fully meet the requirement using only ACS-based options by summing their maximum production capacities. In contrast, when the ethylene production constraint is set to a higher value, partial involvement of the olefins plant becomes inevitable. This increases the overall RoI due to the positive return associated with the olefins plant. Therefore, with the higher value for the ethylene production constraint (i.e. \\(73.13 \\frac{\\textrm{kt}}{\\text {month}}\\)), we still have a positive return by involvement of 0.33, 0.70, and 0.63 of the maximum production capacities of CO2-to-ethylene, methanol-to-olefins, and olefins plants, respectively (as shown in Fig. 2<\/a>b), which highlights 37% defossilization in total. Total investment is based on 1.05 times that of the stand-alone olefins plant (i.e. 1083.5 M\u20ac). The investment distributions and the corresponding scaling factors are shown in Figs. 2<\/a>b and c (ordered from right to left, from the highest to the lowest RoI, according to the return-risk relationship).<\/p>\n

Fig. 2\"Fig.<\/p>\n

Scenario 1: Replacement of olefins for ethylene production with methanol-to-olefins and electrochemical reduction of CO2; Market-based economic parameters are used.<\/p>\n

For the second simulation, we use the re-costed economic parameters to investigate the transition, assuming that ACS-based plants yield positive RoIs. Constraints are assumed to be the same as in the previous simulation, but only for the higher ethylene production capacity, i.e., \\(73.18 \\frac{\\textrm{kt}}{\\text {month}}\\). The results are shown in Fig. 3<\/a>. As can be seen in Fig. 3<\/a>a, the relative RoIs are higher than in the previous case with the market-based economic parameters. The relative risk also increases, as the inclusion of ACS-based plants, which brings their higher RoIs to the portfolio, involves correspondingly higher investment risks into the portfolio selection. The standard deviations of the ACS-based options rise under the re-costed economic parameters. The highest return occurs at \\(R = 5.42, \\sigma = 2.07\\) with scaling factors of 0.50, 1.00, and 0.38 for the olefins, methanol-to-olefins, and CO2-to-ethylene plants, respectively (see the rightmost bar in Fig. 3<\/a>b). The total investment also does not exceed 1083.5 M\u20ac across various portfolio selections, as shown in Fig. 3<\/a>c. Moreover, the lowest RoI under the re-costed parameters is nearly equal to the highest RoI of the previous case, i.e., \\(R = 4.81\\), but with a lower risk, i.e., \\(\\sigma = 1.54\\), and unlike the market-based prices, this portfolio is not 100% fossil-based and includes the CO2-to-ethylene plant in the portfolio selection. In contrast to the previous case, at lower RoIs, the CO2-to-ethylene plant becomes more attractive among the two options of ACS-based plants due to its higher RoI and lower risk. However, as the portfolio shifts toward higher RoIs, the optimization model tends to include more production from the methanol-to-olefins plant, since it has a lower capital cost than the CO2-to-ethylene plant (see Fig. 3<\/a>b and c, from left to right bars, corresponding from the lowest to the highest RoI).<\/p>\n

Besides the detailed investigation discussed above, the following general observations can also be made for the replacement of the olefins plant for ethylene production with methanol-to-olefins and electrochemical reduction of CO2:<\/p>\n

From an investment point of view, full defossilization is not achieved for either the market-based or re-costed economic parameters.<\/p>\n

Even with the market-based economic parameters, defossilization can be applied to some extent by incorporating partial production capacities of both CO2-to-ethylene and methanol-to-olefins plants.<\/p>\n

Using re-costed economic parameters meaning that a premium is allocated to the ACS-based plants,, at the extreme case of defossilization, we can reach almost 5 times the RoI in the portfolio during the transition, but this is subject to governmental financial supports.<\/p>\n

Methanol-to-olefins is relatively more attractive for investment in the transition from fossil-based feedstocks in this scenario than CO2-to-ethylene.<\/p>\n

Fig. 3\"Fig.<\/p>\n

Scenario 1: Replacement of olefins for ethylene production with methanol-to-olefins and electrochemical reduction of CO2; Re-costed economic parameters are used.<\/p>\n

Scenario 2: Replacement of fossil-based olefins for benzene production<\/p>\n

Similar to Scenario 1, the olefins plant is intended to be replaced, but in this scenario, it is for benzene production. The potential ACS-based plants to fulfill the required benzene demand for the cluster are methanol-to-olefins and methanol-to-aromatics, with the latter offering approximately 4 times higher benzene production (see Table 2<\/a>). Two constraints for the total investment and the required production demand are defined as follows: <\/p>\n

$$\\begin{aligned}&\\quad \\sum _{n=1}^{3} c_n \\, T_n \\le 1.05 \\,\\,\\text {max}(T_1, T_2, T_3) \\end{aligned}$$<\/p>\n

\n (21a)\n <\/p>\n

$$\\begin{aligned}&\\quad 0.78\\, c_1 \\, P_1 + 0.25\\, c_2 \\, P_2 + c_3 \\, P_3 = D_r \\end{aligned}$$<\/p>\n

\n (21b)\n <\/p>\n

where the second constraint is the extended form of (9d<\/a>), based on the stoichiometric parameters in Table 2<\/a>, for olefins (denoted as 1), methanol-to-olefins (denoted as 2), and methanol-to-aromatics (denoted as 3). The production demand, \\(D_r\\), is set to \\(57.08 \\frac{\\textrm{kt}}{\\text {month}}\\), based on the maximum production capacity of the olefins plant as mentioned in Table 1<\/a>. This value is also approximately equal to the total benzene that can be produced from the two ACS-based plants.<\/p>\n

The first simulation is based on the market-based economic parameters. The results are shown in Fig. 4<\/a>. As expected and similar to the previous scenario, the maximum return is achieved at \\(R = 4.84, \\sigma = 1.77\\), which corresponds to 100% fossil-based feedstocks (see Fig. 4<\/a>aand the rightmost bar in Fig. 4<\/a>b). The transition to non-fossil feedstocks reduces the RoI of the portfolio, as the RoIs of both methanol-to-olefins and methanol-to-aromatics are negative (see Table 3<\/a>). However, a positive portfolio RoI is still achievable with the highest possible level of defossilization i.e. 24% at \\(R = 0.59, \\sigma = 0.88\\). This is also subject to the highest investment value, i.e., 1639.6 M\u20ac, as shown by the leftmost bar in Fig. 4<\/a>c. As can also be seen in Fig. 4<\/a>c, during the transition (from the right bar to the left bar), the portfolio selection tends to include methanol-to-olefins. However, at some point, the inclusion of methanol-to-aromatics becomes more attractive due to its production capacity being almost four times higher than that of the other ACS-based plant. The reason methanol-to-aromatics is not included at the lowest level of defossilization is its significantly higher capital cost compared to methanol-to-olefins (capital costs are 2796.1 M\u20ac and 322.04 M\u20ac, respectively). Another reason is related to the correlation factors between olefins and methanol-to-aromatics, and olefins and methanol-to-olefins. Based on Table 4<\/a>, both correlations are negative, but the correlation between olefins and methanol-to-aromatics is more strongly negative, making methanol-to-aromatics less attractive to include in the portfolio selection, unless methanol-to-olefins production cannot meet the demand.<\/p>\n

Fig. 4\"Fig.<\/p>\n

Scenario 2: Replacement of olefins for benzene production with methanol-to-olefins and methanol-to-aromatics; Market-based economic parameters are used.<\/p>\n

The second simulation is based on the re-costed economic parameters. The results are shown in Fig. 5<\/a>. As can be seen in Fig. 5<\/a>a, although the relative RoIs of the various portfolios are higher than those based on the market-based economic parameters, the highest achievable RoI occurs at \\(R = 4.87, \\sigma = 1.84\\), which is not significantly higher than the RoI obtained with the market-based parameters. This is because the re-costed RoI of methanol-to-aromatics, which can produce a large amount of benzene, is still low (\\(R = 1.85\\)), making the transition less attractive. This is also the reason why a relatively high level of defossilization (11% and 14%) occurs only at the two extremes of the RoI spectrum. According to Fig. 5<\/a>b, these occur at the rightmost and leftmost bars, corresponding to the predominant inclusion of methanol-to-aromatics and methanol-to-olefins, respectively. However, the scaling factor of methanol-to-olefins is much higher than that of methanol-to-aromatics, due to differences in the amount of benzene produced. In terms of total investment for these two transition cases, although full capacity production of methanol-to-olefins is required in the one of these portfolios, the total investment remains lower than that of the alternative case, with an investment amount of 1235.5 M\u20ac (see Fig. 5<\/a>c).<\/p>\n

In general, the following observations can be drawn regarding the replacement of olefins for benzene production:<\/p>\n

Methanol-to-aromatics is a capital-intensive technology, which makes it less attractive for the optimization model to include it in the portfolio under both market-based and re-costed economic parameters.<\/p>\n

With the market-based economic parameters, defossilization can be applied to some extent by incorporating partial capacities of both methanol-to-aromatics and methanol-to-olefins plants.<\/p>\n

Using re-costed economic parameters, the transition remains unattractive, and the level of defossilization is still low, indicating the need for greater governmental financial support.<\/p>\n

Methanol-to-olefins is a more attractive investment option for the transition from fossil-based feedstocks, as it can contribute a relatively acceptable share to both ethylene and benzene production.<\/p>\n

Fig. 5\"Fig.<\/p>\n

Scenario 2: Replacement of olefins for benzene production with methanol-to-olefins and methanol-to-aromatics; Re-costed economic parameters are used.<\/p>\n

Scenario 3: Integration of methanol-to-aromatics and CO2-to-methanol via hydrogeneration in PGME value chain<\/p>\n

Propylene oxide and propylene glycol are two plants operated by one company at the Port of Rotterdam. These plants represent a value chain, where they are interconnected, and propylene oxide is shared between them for the production of PGME. In addition to PGME, other products such as tert-butyl alcohol and excess propylene oxide are also produced and shared with other plants in the cluster. To defossilize this production line, two feedstocks, i.e. butane (fed to the propylene oxide plant) and methanol (fed to the propylene glycol plant), can be sourced from the outputs of two ACS-based plants: methanol-to-aromatics and CO2-to-methanol via hydrogenation, respectively. Therefore, this scenario analyzes the integration of these two ACS-based plants to investigate investment directions.<\/p>\n

To formulate the optimization model for this scenario, the presence of the propylene oxide and propylene glycol plants is fixed with scaling factor of one, while the lower and upper limits of the scaling factors for the methanol-to-aromatics and CO2-to-methanol via hydrogenation plants are set to 0 and 1, respectively. Another constraint regarding total investment is also included. Unlike the replacement scenarios, this scenario represents the integration of additional plants. Therefore, the total investment constraint is written as the sum of the total capital costs of all potential options, as follows:<\/p>\n

$$\\begin{aligned} \\sum _{n=1}^{4} c_n \\, T_n \\le 1.05 \\,\\,\\text {sum}(T_1, T_2, T_3, T_4) \\end{aligned}$$<\/p>\n

\n (22)\n <\/p>\n

The results based on the market-based economic parameters are shown in Fig. 6<\/a>. As can be seen in Fig. 6<\/a>a, it can be observed that the transition from fossil-based feedstocks requires a large amount of investment, ranging from 213.1 M\u20ac to 3045.4 M\u20ac. The majority of the investment during this transition is allocated to the methanol-to-aromatics plant, primarily due to its high capital cost, as previously mentioned.<\/p>\n

Fig. 6\"Fig.<\/p>\n

Scenario 3: Integration of methanol-to-aromatics and CO2-to-methanol via hydrogeneration in PGME value chain; Market-based economic parameters are used.<\/p>\n

For the second simulation, based on the re-costed economic parameters, the results are shown in Fig. 7<\/a>. As can be seen in Fig. 7<\/a>a, the RoI and standard deviation of the various portfolios do not vary significantly, which makes the investment decision straightforward, favoring the portfolio that offers the highest level of defossilization. This is because the inclusion of the propylene oxide and propylene glycol plants is fixed, and the positive re-costed RoIs of the methanol-to-aromatics and CO2-to-methanol via hydrogenation plants make them attractive candidates for including into the portfolio. The highest level of defossilization of butane occurs at \\(R = 2.36, \\sigma = 1.56\\), with 0.13 of the maximum production capacity of methanol-to-aromatics, and 0.48 of the maximum production capacity of CO2-to-methanol via hydrogenation (see the mostleft bar in Fig.\u00a07<\/a>b). This corresponds to an investment of 769.7 M\u20ac, with an allocation of 46% for methanol-to-aromatics, and 27% for CO2-to-methanol via hydrogenation (see the mostleft bar in Fig.\u00a07<\/a>c). Moreover, CO2-to-methanol via hydrogenation is able to fulfill 100% of the demand for the propylene glycol plant, as the required amount is relatively low, i.e. \\(3.06 \\frac{\\textrm{kt}}{\\text {month}}\\) (see the mostright bar in Fig.\u00a07<\/a>b).<\/p>\n

Based on the aforementioned discussion, the following key points regarding the integration of methanol-to-aromatics and CO2 hydrogenation into the PGME value chain can be highlighted:<\/p>\n

The transition from fossil-based feedstocks is not attractive for investment using market-based economic parameters; however, it can be profitable with re-costed prices.<\/p>\n

With the re-costed economic parameters, partial deffossilization is economically feasible by integrating both methanol-to-aromatics and CO2-to-methanol via hydrogenation.<\/p>\n

Fig. 7\"Fig.<\/p>\n

Scenario 3: Integration of methanol-to-aromatics and CO2-to-methanol via hydrogeneration in PGME value chain; Re-costed economic parameters are used.<\/p>\n

Scenario 4: Integration of methanol-to-aromatics and biomass-to-isobutylene in MTBE value chain<\/p>\n

This scenario investigates the MTBE value chain. Here, the propylene oxide plant shares tert-butyl alcohol with the MTBE production plant. To some extent, this can be defossilized by methanol-to-aromatics fulfilling the butane requirement, as in the previous scenario, and biomass-to-isobutylene supplying isobutylene for the MTBE production plant. The latter integration is suggested by Stepchuk et al.11<\/a>, which also assessed the impacts of deploying bio-based isobutene for MTBE production at both the process and cluster levels. For this scenario, the presence of the propylene oxide and MTBE plants is fixed, while the scaling factors for the methanol-to-aromatics and biomass-to-isobutylene plants are determined by the optimization model. A constraint regarding total investment is included as follows:<\/p>\n

$$\\begin{aligned} \\sum _{n=1}^{4} c_n \\, T_n \\le 1.05 \\,\\,\\text {max}(T_1, T_2, T_3, T_4) \\end{aligned}$$<\/p>\n

\n (23)\n <\/p>\n

where the numbers 1 to 4 represent MTBE, propylene oxide, methanol-to-aromatics, and biomass-to-isobutylene plants, respectively.<\/p>\n

The results for the market-based economic parameters are shown in Fig. 8<\/a>. As can be seen, the inclusion of biomass-to-isobutylene is chosen with the maximum production capacity for all portfolios at the Pareto front. which meets the the isobutylene demand of the MTBE plant and the extra amount is considered as the product to sell. As methanol-to-aromatics is included in the portfolio, the portfolio RoI decreases due to the negative RoI of this plant. Therefore, the highest level of defossilization occurs at the lowest RoI, i.e., \\(R = -0.34, \\sigma = 1.14\\), with full defossilization of isobutylene and 75% of butane by including 0.50 of the maximum production capacity of methanol-to-aromatics (see the leftmost bar in Fig. 8<\/a>b). This also corresponds to the highest investment value of 3632.2 M\u20ac based on Fig. 8<\/a>c, with investment allocation of 55% and 38% for biomass-to-isobutylene and methanol-to-aromatics, respectively.<\/p>\n

Fig. 8\"Fig.<\/p>\n

Scenario 4: Integration of methanol-to-aromatics and biomass-to-isobutylene in MTBE value chain; Market-based economic parameters are used.<\/p>\n

The results based on the re-costed economic parameters are shown in Fig. 9<\/a>. As can be seen in Fig. 9<\/a>a, RoIs of the various portfolios increase due to the re-costed RoIs of the considered plants, and the transition from fossil-based feedstocks based on the re-costed economic parameters is similar to the market-based economic parameters, i.e., lower RoI corresponds to a higher level of defossilization. This is because the economic parameters for the fixed plants are still more favorable than those of the ACS-based plants, which highlights that even if the re-costed economic parameters of ACS-based options shift towards positive values, they still remain lower than the market-based of the fossil-based ones to be selected in the portfolios of higher RoIs. While the overall RoIs are increased, but the percentage of defossilization is relatively lower than market-based simulation, which shows re-costed prices in this scenario is only favorable for the return of investment, not defossilization.<\/p>\n

Therefore, the following highlights regarding the integration of methanol-to-aromatics and biomass-to-isobutylene into the MTBE value chain can be pointed out:<\/p>\n

The transition from fossil-based feedstocks is not very attractive for investment using re-costed economic parameters.<\/p>\n

A relatively high deffossilization based on both market-based and re-costed economic parameters are achievable in this scenario.<\/p>\n

Fig. 9\"Fig.<\/p>\n

Scenario 4: Integration of methanol-to-aromatics and biomass-to-isobutylene in MTBE value chain; Re-costed economic parameters are used.<\/p>\n

Scenario 5: Integration of methanol-to-olefins and methanol-to-aromatics<\/p>\n

This scenario investigates the portfolio of a value chain with more plants. It is also aligned with Scenarios 1 and 2, but considers both ethylene and benzene production simultaneously by replacing the olefins plant through the integration of methanol-to-olefins and methanol-to-aromatics. Therefore, the ethylbenzene production plant and the styrene production plant, which are connected through the exchange of ethylbenzene, are considered in this scenario in addition to the olefins plant that supplies ethylene, propylene, and benzene to both. To replace these resources, methanol-to-aromatics (as the main benzene provider) and methanol-to-olefins (as the main ethylene and propylene provider) are potential ACS-based plants. To formulate the optimization problem, in addition to the investment constraint (expressed as the summation of the capital costs of the fixed plants and the maximum capitals of the replacement options) a constraint on benzene production is also included as follows: <\/p>\n

$$\\begin{aligned}&\\quad \\sum _{n=1}^{5} c_n \\, T_n \\le 1.05 \\,\\,(T_1 + T_3 + \\text {max}(T_2, T_4, T_5)) \\end{aligned}$$<\/p>\n

\n (24a)\n <\/p>\n

$$\\begin{aligned}&\\quad -0.75\\, c_1 \\, P_1 + 0.78\\, c_2 \\, P_2 + 0.25\\ c_4 \\, P_4 + c_5 \\, P_5 = 0 \\end{aligned}$$<\/p>\n

\n (24b)\n <\/p>\n

in which the numbers 1 to 5 represent ethylbenzene, olefins, styrene, methanol-to-aromatics, and methanol-to-olefins, respectively. The reason for including the second constraint is that benzene is entirely closed within this value chain, with no external sales or exchanges with other companies within the cluster. Therefore, we impose this constraint to ensure that the internal demand for benzene is totally met.<\/p>\n

The results based on the market-based economic parameters are shown in Fig. 10<\/a>. Since the RoIs of the ethylbenzene and styrene plants are negative, the optimization model tends to include the olefins plant as the only positive RoI plant to compensate for the return of the portfolio instead of selecting ACS-based alternatives. Moreover, between the two available ACS-based options, methanol-to-olefins has a higher standard deviation; therefore, it is less likely to be included in lower RoI portfolios. If the model is not explicitly constrained to integrate it, methanol-to-olefins is not selected in any portfolio (the red line in Fig. 10<\/a>a). However, by imposing a minimum integration constraint of at least 0.20 of the maximum production of methanol-to-olefins (implemented via a lower bound on its scaling factor in the optimization model) the relative RoI decreases as expected (the blue line in Fig. 10<\/a>a). The inclusion of methanol-to-aromatics also varies between 0 and 0.15 of the maximum production capacity, resulting to the reduction in portfolio return and also increasing total investment (from right to left bars in Fig. 10<\/a>c).<\/p>\n

Fig. 10\"Fig.<\/p>\n

Scenario 5: Integration of methanol-to-olefins and methanol-to-aromatics in a value chain; Market-based economic parameters are used.<\/p>\n

Using the re-costed economic parameters, the results are shown in Fig. 11<\/a>. These results are fully consistent with those obtained under the re-costed economic parameters in Scenario 2. Therefore, from the lowest to the highest RoI, the inclusion of methanol-to-aromatics is initially more attractive, but at a certain point, it shifts toward the inclusion of methanol-to-olefins (from left to right bars in Fig. 11<\/a>b) with full production capacity of methanol-to-olefins at \\(R=3.99, \\sigma = 1.63\\). Furthermore, similar to Scenario 2, a relatively high level of defossilization occurs at both extremes of the RoI spectrum. This increase is due to the inclusion of the ethylbenzene and styrene plants and their correlation characteristics, i.e. their negative correlation with the negative RoI of the ethylbenzene plant and their positive correlation with the positive RoI of the styrene plant. This highlights the significance of the availability of multiple viable options when constructing an investment portfolio. A decision between the two highest levels of defossilization can also be made in favor of the inclusion of methanol-to-aromatics, as it requires a relatively lower investment cost compared to the alternative case, i.e. 1403.9 M\u20ac (see the rightmost and leftmost bars in Fig. 11<\/a>c<\/p>\n

Taking the aforementioned technical discussions into account, the following notes can also be drawn for the integration of methanol-to-olefins and methanol-to-aromatics in a medium-stage value chain:<\/p>\n

The fixed inclusion of plants in a longer value chain impacts the defossilization from an investment point of view, based on the economic characteristics of those fixed plants; for instance, less defossilization based on re-costed economic parameters in comparison with market-based ones, while increasing return.<\/p>\n

Methanol-to-olefins is still a more attractive investment option for the transition from fossil-based feedstocks, as it can contribute a relatively acceptable share to both ethylene and benzene production with a cheaper capital cost.<\/p>\n

Fig. 11\"Fig.<\/p>\n

Scenario 5: Integration of methanol-to-olefins and methanol-to-aromatics in a value chain; Re-costed economic parameters are used.<\/p>\n

\\(\\epsilon -\\)constraint method for distributed solutions<\/p>\n

The above results are derived based on the original solution method, the weighted-sum approach, as discussed by Lhabitant23<\/a>. As can be seen in the obtained results, the method is somewhat sensitive to weight changes, which might result in some gaps in the return-risk trade-offs. Therefore, to provide a more distributed solution and mitigate the arbitrariness of the weight selection, the results of Scenario 1 (representing a short value chain) and Scenario 5 (representing a long value chain) are re-derived based on the \\(\\epsilon\\)-constraint method, given in (10a<\/a>)\u2013(10e<\/a>). The bound on \\(\\epsilon\\) varies between the minimum possible risk and the maximum possible risk. The minimum risk is obtained by solving a single-objective optimization problem focused solely on risk minimization. Conversely, the maximum risk is obtained by solving a single-objective optimization problem focused on return maximization. Therefore, a distributed Pareto front can be obtained by varying the bound of risk.<\/p>\n

As Fig. 12<\/a> shows, the risk-return trade-off curve obtained using the \\(\\epsilon\\)-constraint method is more distributed, although the two extreme points of the spectrum remain unchanged compared to the weighted-sum method. In other words, changing to the \\(\\epsilon\\)-constraint solution method does not change the maximum possible defossilization. However, it provides investors with more intermediate options to choose from. These additional options can also be observed from, scaling factors and the corresponding investment distributions, shown in Fig. 12<\/a>. It should also be noted that the conclusion drawn from the primary solution method remains valid, i.e. the direction of ACS-based technology inclusion in the portfolio is unchanged. The \\(\\epsilon -\\)constraint method solution method can only offer more intermediate options to investors interested in such trade-offs.<\/p>\n

Fig. 12\"Fig.<\/p>\n

Solutions of the \\(\\epsilon -\\)constraint method for Scenarios 1 and 5.<\/p>\n

A holistic discussion of the transition from fossil-based feedstocks from an investment perspective in practice and future directions<\/p>\n

Practical challenges for the transition from fossil-based feedstocks have been identified for two case studies at both process and cluster levels in the Port of Rotterdam by Manalal et al.12<\/a> and Stepchuk et al.11<\/a>. These challenges mainly include additional utility requirements such as electricity and water supply, significantly larger bare land needed for deploying ACS-based plants, and downstream implications due to major changes in (by-)product outputs. However, in this study, a further investigation has been conducted from a decision-maker\u2019s perspective to highlight the investment challenges associated with such a transition in the Port of Rotterdam. Therefore, five cases have been analyzed based on the proposed optimization model to investigate the profitability and further feasibility of the transition by identifying optimal portfolios. Based on the aforementioned explorations and analyses, the following implications regarding the transition in the Port of Rotterdam can be noted:<\/p>\n

As calculated, stand-alone ACS-based plants exhibit negative RoIs (sometimes with high risk), making their full deployment within the cluster economically unreasonable. This highlights the need for a gradual replacement and integration with adjusted capacities to ensure investment viability (as done in this paper).<\/p>\n

According to recent historical market prices, shifting towards ACS-based plants, which are highly capital-intensive, reduces the RoI of the portfolios (in some cases even resulting in negative values), consequently preventing full defossilization and highlighting the need for governmental financial supports.<\/p>\n

Utilizing the allocation method based on the bare minimum price and the added values of fossil-based counterparts to re-cost the (by-)products of ACS-based plants can make defossilization somewhat attractive for investment. However, it still requires substantial governmental subsidies.<\/p>\n

Reaching to full defossilization based on both market-based and re-costed prices is not totally economically reasonable (see Table 8<\/a> and Table S.2 in supplementary materials B<\/a>). As it is in line with other environmental and techno-economic analyses, a reconsideration including all the aspects together may be required.<\/p>\n

Table 8 The maximum percentage of defossilization under the defined scenarios based on market-based and re-costed economic parameters.<\/p>\n

Therefore, the transition from fossil-based feedstocks faces practical investment challenges, mainly related to adjusting the current capacities of fossil-based plants and providing governmental subsidies to compensate for profitability gaps.<\/p>\n","protected":false},"excerpt":{"rendered":"In this section, the proposed portfolio optimization model for the defined scenarios are applied and risk-return relationships are…\n","protected":false},"author":2,"featured_media":1128,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[1243,1244,1245,1246,1248,1249,41,1247],"class_list":{"0":"post-1127","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-rotterdam","8":"tag-climate-sciences","9":"tag-energy-science-and-technology","10":"tag-environmental-sciences","11":"tag-environmental-social-sciences","12":"tag-humanities-and-social-sciences","13":"tag-multidisciplinary","14":"tag-rotterdam","15":"tag-science"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/netherlands\/wp-json\/wp\/v2\/posts\/1127","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/netherlands\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/netherlands\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/netherlands\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/netherlands\/wp-json\/wp\/v2\/comments?post=1127"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/netherlands\/wp-json\/wp\/v2\/posts\/1127\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/netherlands\/wp-json\/wp\/v2\/media\/1128"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/netherlands\/wp-json\/wp\/v2\/media?parent=1127"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/netherlands\/wp-json\/wp\/v2\/categories?post=1127"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/netherlands\/wp-json\/wp\/v2\/tags?post=1127"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}