*3.5. Economical Assessment-Cost E*ff*ectiveness Parameters Definition*

As stated in Section 2.2, CAPEX, OPEX, and COE are the basic parameters for the cost effectiveness comparison. It is important to mention that CAPEX and OPEX are different for both REUs that are prepared for IGCC-CaL and adsorption integration. Apart from the difference in the technological segments, which influence CAPEX data [18,19], also OPEX variables such as fuel costs, solvent/adsorbent cost and final cost of the produced electricity vary for each REU system (Table 6). However, the operational time for both systems is assumed to be equal (7400 h/year). CAPEX and OPEX for the REUs and REUs with CCT systems are summarized in the Table 7.

**Table 6.** Operational expenditures (OPEX) variables for reference energy units (REUs) of both case studies [18,19].


**Table 7.** Capital expenditures (CAPEX) and OPEX for REUs without and with carbon capture technology (CCT) systems [18,19].


COE is based on the following Equation (7):

$$\text{COE} = \frac{I\_0(t=1) + O\_{f\text{ix}} + O\_{\text{vur}}}{P\_t} \tag{7}$$

where

*I*<sup>0</sup> is the modified ratio of capital expenditures that refer to 1 year of the operation (€/year);

*Ofix* are fixed operational costs (e.g., costs for maintenance and repairs) (€/year);

*Ovar* are variable operational costs (e.g., fuel costs) (€/year);

*Pe* amount of produced and delivered electric energy to the net in the first year of operation (MWh/year).

The cost effectiveness is based on Capture cost (*CCo*) and is calculated as follows Equation (8):

$$\text{CCo} = \frac{\text{COE}\_{\text{with }\text{CCT}} - \text{COE}\_{\text{without }\text{CCT}}}{\text{amount of separated }\text{CO}\_2} \tag{8}$$

For REU, the cost effectiveness is based on the price of the *CO*<sup>2</sup> allowance. This study is taking into account the market trend of the price of *CO*<sup>2</sup> allowances for the time frame of 2015–2050 [23–26].

The parameter of avoided cost of emitted *CO*<sup>2</sup> is expressed as follows Equation (9):

$$Av\text{Co} = \frac{\text{COE}\_{\text{with\\_CCT}-\text{COE}\_{\text{without\\_CCT}}}}{\text{ emissions\\_CO}\_{2\text{without\\_CCT}-\text{emission\\_CO}\_{2\text{with\\_CCT}}}} \tag{9}$$

#### **4. Results**

This section presents the results according to the methodology for LCIA and economic methodology. Further analysis of the results is discussed in detail in Section 5.

#### *4.1. Life Cycle Impact Assessment*

The results for both scenarios (Table 8) are divided into three groups of values:


**Table 8.** Results for both scenarios (EIC—environmental impact categories, CHV—characterization values, NV—normalization values, RC—relative contribution); ozone depletion (OD), human toxicity (HT), ionizing radiation (IR), photochemical oxidant formation (POF), particulate matter formation (PMF), terrestrial acidification (TA), climate change (CC), terrestrial ecotoxicity (TET), agricultural land occupation (ALO), urban land occupation (ULO), natural land transformation (NLT), marine ecotoxicity (MET), marine eutrophication (ME), fresh water eutrophication (FE), fresh water ecotoxicity (FET), fossil depletion (FD), metal depletion (MD), water depletion (WD).


The characterization values show results in absolute values (first column for scenario 1 and 2), comparable only within one impact category.

The normalized results (second column for scenario 1 and 2) allow us to compare the severity of environmental impact categories among them, as all the impact categories are calculated in common units (points).

The relative contribution (third column for scenario 1 and 2) helps to identify a contribution of each environmental category in a certain ratio (%) to the sum of normalized values of all impact categories (100%). Relative contribution of each environmental category computes according to Equation (10).

$$\text{Cont}\_{\text{ic}} = \frac{NV\_{\text{ic}}}{SIM\_{NV}} \times 100 \tag{10}$$

where

*Contic* is the relative contribution of each environmental impact category to the sum of environmental impacts;

*NVic* is the normalized value of the specific impact category;

*SUMNV* is the total sum of the normalized values of all impact categories.

The sum of all normalized values indicates that scenario 1 has lower environmental impacts than scenario 2 and, therefore, better environmental performance. From Table 8, it is possible to verify that POF, TA, and PMF show the highest relative contributions regarding the total environmental impact of the systems.

In scenario 1, marine eutrophication and fossils depletion have also (6.71% and 5.46%) notable relative contribution. All other categories have relative contribution below 1%.

Categories of CC and FD are also important to analyze. The assessed systems are dealing with CO2 capture; thus, the category of CC is directly influenced. Also, the brown coal mining and treatment are key processes which influence the category of FD.

#### *4.2. Pareto Analyses and Comparison of the Processes among Scenarios*

As previously stated, Pareto analysis helps to define the environmental categories of the highest significance to the total of environmental impacts. Figures 3 and 4 show the key impact categories for each scenario. Both scenario 1 and scenario 2 identify the most significant environmental categories of POF, TA, and PMF. Both figures show just the visible values on the plot. The remaining impact categories have a relative contribution below 1%.

**Figure 3.** Pareto analysis for scenario 1 (only visible values).

In the next step, it is important to define the hotspots in the processes (most impactful processes) for both scenarios. Therefore, further analysis of the potential contribution of the processes for the most critical impact categories was performed. In this analysis, the categories cannot be compared between each other. The analysis is based on the characterization values, and therefore, it is focused on one impact category at the time, influenced in different ratios (%) by different processes. The results for the processes contribution are summarized in Table 9.

**Figure 4.** Pareto analysis for scenario 2 (only visible values).



In the category of climate change, the flow of captured CO2 is referred to as environmental credit. However, in both cases, the CO2 emissions and clean flue gases are still released to the air and therefore contribute to the environmental impacts. Some of the processes do not have any relative impact to the environmental categories.

#### *4.3. Cost E*ff*ectiveness Comparison*

According to the Equations (7)–(9), COE, CCo, and AvCo for both IGCC-CaL and PCC-A systems are summarized in the following table (Table 10).

**Table 10.** Results of cost of electricity (COE), CO2 capture cost, and avoided CO2 cost.


The cost effectiveness is shown in Figure 3. The figure shows the correlation between the carbon price on the market (black lines) from 2015 up to 2050 [23–26] and CO2 avoided costs of both CCT systems connected to REU. The comparative economic criteria were defined/re-calculated with respect to the inputs and variables based on 2018. Two primary cases were analyzed. The first case (red and green dotted lines) describes CCT utilization as a key economic unit in carbon capture utilization (CCU) scheme with the possibility of using CO2 within the enhanced oil recovery, fuel production, etc. The second case (red and green dashed lines) reflected the CCT as a fundamental unit together with transportation and storage in the carbon capture and storage (CCS) scheme. The Czech Republic considers CO2 transport by pipelines into salt aquifers in the Zatec Basic (North-West Bohemia, the Czech Republic) [27,28].

The carbon price curve demonstrates the possible development of the market of carbon price in 2015–2050. The proposed estimation in Figure 5 was defined as a combination of the real average annual data from the market (black line) and an estimate based on CAKE/KOBiZE forecast (dashed black line). Moreover, the initial CAKE/KOBiZE forecast [25] is also displayed in Figure 5 (dotted black line). This forecast was evaluated based on the Paris Agreement for the Central Europe power sector (more precisely Poland).

**Figure 5.** The cost-effectiveness of selected CCT integration into power plants.

#### **5. Discussion**

The discussion part follows the sections of the results. At first, the environmental assessment with processes analysis will be discussed. In the second part, the economical assessment will be analyzed. In the last part, the combination of environmental and economic results will be concluded.

#### *5.1. Environmental Assessment*

The results of the characterization, normalization, and relative contribution of the environmental impacts are shown in Table 8. The absolute values of the characterization process enable the comparison of the same environmental impact category among scenarios. At first glance, scenario 1 has a lower characterization values in comparison with scenario 2 in almost all environmental categories except in the category of ionizing radiation. Ionizing radiation in scenario 1 is influenced by the process of oxygen production via cryogenic separation. This process is the database process and does not reflect the local impact. Moreover, the radiation impact has an insignificant contribution in comparison with other impacts (sc.1 0.16%; sc.2 0.06%).

When we aggregate the environmental impact categories after normalization (see Table 8 sum of normalized values), the scenario 2 has a higher global environmental impact (0.00231) than scenario 1 (0.0042). However, it is important to stress out that the case studies considered within this manuscript are different in several parameters such as (i) different scale of REU, (ii) site specific case studies (iii) different power generation technology. Therefore, the environmental performance for scenario 2 might be improved regarding LCA results if the REU technology would have the same technological basis in both cases.

However, it is important to analyze the contribution of the individual impact categories in the total environmental impact. To analyze the highest significance of the impact categories of each scenario, the pareto analysis was chosen as a decision-making tool (Figures 3 and 4). Both figures are confirming the relative contribution results that impacts category of POF, TA and PMF have the highest contribution to the sum of all normalized values. Nevertheless, in comparison to scenario 2, scenario 1 has in the category of ME a slightly higher contribution of 1%, and in the category of FD, it has a lower contribution of 7.57%. Another difference is seen in the category of NLT, where scenario 2 exceeds scenario 1 by 2.5%. As both scenarios have CO2 capture as primary function, the environmental impact category of climate change (CC) (which measures the contribution of CO2 and other compounds to the global warming) has a lower contribution to the total environmental impact. This was expected since both technologies capture the CO2.

The impact categories are influenced by the environmental impact of the different processes in the life cycle. Table 9 shows that the significant impact categories taken from the pareto analysis are influenced by specific processes in both scenarios. According to Table 9, the categories of POF, TA, PMF, and ME are influenced in 95% to 99.6% by the emissions of CO2 capture process (carbonate looping, adsorption) in both scenarios (Tables 2 and 4). However, the characterization values, for instance in category POF, are very small (scenario 1—0.039 kg NMVOC eq./1 kWh; scenario 2—0.062 kg NMVOC eq./1 kWh).

In the category of FD, the production chain of the lignite from Slovak lignite mix (which has a similar thermal efficiency as Czech lignite of 11 MJ/kg [12]) results as the most significant process in both scenarios. In scenario 1, the lignite mix for the power unit contributes almost 86.40% to FD (Table 9). In scenario 2, the hard coal mix for active carbon production increases the FD contribution by 40.70%, whereas lignite mix for power unit contributes 19.10%. Moreover, in scenario 2, the category of FD is influenced 40% by the utilization of thermal energy from natural gas mix for activated carbon production.

An interesting result is shown for the category of NLT. In scenario 1, major land transformation would be impacted by the construction of an air separation unit for oxygen production. In scenario 2, the hard coal production chain with all the mining process necessary for active carbon production turns out to be the process with the highest impact to the natural land transformation. Moreover, in comparison with scenario 1, hard coal would need to be mined and transported to the power unit, which creates an additional environmental burden. In scenario 1, the air separation unit would need to be built right in the local area of the power unit.

The category of CC is mainly influenced by the ratio of captured CO2. It is obvious that adsorption process would require higher amount of active carbon to be able to capture 95% of CO2 such as an IGCC-CaL system. That would lead to the increase of the total environmental impact. Also, the thermal energy from the natural gas combustion as a primary energy for activated carbon production, is influencing category of CC in 72%.

The primary goal for the CCT solution under Czech energy conditions was to design and compare post combustion and pre combustion systems for the same REU. However, during the research, problems occurred with the technological requirements (such as quality of the lignite for each REU) of each system. Thus, the input parameters had to be optimized, which led to different scale of REU, different lignite quality and different technological segments. Therefore, the specific case studies considered in this manuscript do not have the same basis for fair comparison. Still, in LCA analysis relating all the environmental impacts to 1 kWhe, the aforementioned differences are still present in particular environmental impacts.

This paper considers the specific case of activated carbon production from hard coal. However, activated carbon can be produced in several options from biomass or other organic waste that would decrease total environmental performance of the process. Also, different adsorption process

configurations may lead to different results. This specific case of the PCC-A points out the problem with Na salts production after flue gas purification, which are currently considered as waste material with no other use.

The main advantage of PCC-A against IGCC-CaL is the thermal efficiency of the whole system. For the process of the CO2 capture and compression, PCC-A requires a consumption of 28 MWe from the power unit, whereas IGCC-CaL requires for the same process 119.31 MWe (Table 5). Thus, PCC-A decreases the thermal efficiency of the power unit by 4.67%, and IGCC-CaL decreases the thermal efficiency of the power unit by 12.5%. The thermal efficiency decrease would require a higher energy production that might also influence the environmental performance of IGCC-CaL system as well. Moreover, the thermal efficiency decline may be significant for the further operational costs increase. On the other hand, the specific energy consumption (MWe/t CO2) in Table 5 states that PCC-A (1.3 MWe/t CO2) would require a slightly higher energy demand than the IGCC-CaL process (0.9 MWe/t CO2).

The following table (Table 11) shows the comparison of this study with other studies of [8–10]. The environmental results of this study for pre-combustion IGCC-CaL system shows a lower kg CO2 eqv./MWh than in a similar study of Petrescu et al. [8]. The lower impact of climate change of this study (global warming potential) corresponds to the smaller size of the reference energy power plant and higher capture ratio. For eutrophication potential this study is resulting in much lower values that are comparable with the study of Clarens et al. [10]. However, the impacts of the acidification potential in this study are the highest among of all studies. This might be influenced by the production of the used sorbent in the form of CaCO3 + CaSO4 as non-utilized waste product of high-temperature desulphurization. Moreover, the study shows the highest drop in net energy efficiency due to CCT implementation. The reason might be that the IGCC-CaL design of this study does not consider utilizing the heat losses due to lack of commercially viable heat exchangers for such amount of heat. In the case of post-combustion capture, PCC-A has the lowest CO2 capture rate among all the studies. It corresponds to higher values in climate change in comparison with similar study of Clarens et al. [10]. Also, in this study of PCC-A, specific emissions are the highest, which refers to the low CO2 capture rate.
