Results

3.1 E-factor

The E-factor can be defined as the mass (kg) waste produced per kg product. In Fig. 3 the E-factor of the 8 production steps individually is visualized. This means that the waste produced in earlier production steps is not taken into account. Changing process step 6 from a batch into a continuous process results in a drop of the E-factor from 29kg waste/kg F to 19.5 kg waste/kg F. This corresponds to a reduction of almost 50%. In addition, small reductions due to the improved yields (see Figs. 1 and 2) can be seen in steps 7 and 8. However, Fig. 3 also illustrates that step 4 as is the production step with the highest E-factor. This E-factor is mostly covered by the high amount of wastewater produced as well by the low efficiency of the process.

I BATCH E-factor non-cumulative kg/kg

CONTINU E-factor non-cumulative kg/kg

THfMm^LntDi^co cccccLCCLC

CDCDCDCDCDCDCDCD

Figure 3: Non-cumulative E-factor of eight consecutive production steps.

In Fig. 4, the E-factors are cumulated. For each production step, the waste produced in the previous production step is taken into account. It can be seen that the reduction in the E-factor at step 6 by changing from batch to continuous is relatively small compared to the non-cumulative results. However, the difference increases again when taking into account steps 7 and 8. From these cumulative

■ BATCH E-factor cumulative kg/kg

■ CONTINU E-factor cumulative kg/kg

■ BATCH E-factor cumulative kg/kg

■ CONTINU E-factor cumulative kg/kg

Figure 4: Cumulative E-factor of eight consecutive production steps.

results, it can be stated that in total 26% less waste is produced by using the continuous alternative in step 6.

3.2 ETH Finechem tool

The Finechem tool from ETH is an estimation tool for the prediction of the cumulative energy demand (CED), the global warming potential (GWP) and the eco-indicator '99 (EI99) based on the group contributions of the chemicals under consideration. This tool cannot be used for the estimation of the life cycle impact assessment (LCIA) of enantiomers and components containing bromine atoms. This means it is not a useful EEMM for the evaluation of this case. This is also clear from the results presented in Fig. 5 where the tool is used for illustration. From step 5 (molecule E) on, the environmental impacts do not increase anymore, which is impossible regarding the efficiencies in Figs. 1 and Fig. 2. The ETH Finechem tool however remains a very good estimation tool if no other data is available and as long as the guidelines are followed correctly, which is clearly not the case for this illustration.

3.3 Exergy analysis (process and plant level)

Next to the relatively quick EEMMs (E-factor and ETH finechem tool), more detailed but also more time consuming EEMMs can be used for the evaluation of chemical production processes. One example is the exergy analysis of the eight process steps at the process level and at the plant level. The focus here will only be on the results of plant level exergy analysis. Non-cumulative results at the plant level are presented in Fig. 6 and cumulative results are presented in Fig. 7. Those figures are similar to the ones presented for the E-factor (Figs. 3 and 4) because the main contributor of all the environmental impacts in these processes is the use of fossil chemicals (visible in Figs. 6 and 7). However, when Figs. 3 and 6 are put next to each other, the importance of step 4 in Fig. 3 has

300,00

250,00

T5 200,00 E

14,00 12,00 10,00 8,00 6,00 4,00 2,00 0,00

13

o

E

*

U"

"o

IV

E

ÎN

O

u

c

M

'o

a

a

w

en

13

♦ CED_Prediction_Mean MJ/mol □ GWP_Prediction_Mean kg CO2-eq/mol A EI99_Prediction_Mean (Points/mol) * 10

Figure 5: ETH prediction of ten intermediate molecules in the synthesis route of galantamine.

Figure 5: ETH prediction of ten intermediate molecules in the synthesis route of galantamine.

Figure 6: Non cumulative exergy losses at the plant level for eight consecutive production steps and divided over seven impact categories.

Figure 7: Cumulative exergy losses at the plant level for eight consecutive production steps and divided over seven impact categories.

disappeared in Fig. 6. The reason is that in the E-factor EEMM, one kg water has the same impact as one kg organic solvent. This is not the case using exergy analysis. The waste stream of step 4 is mainly water based and thus scores worse for the E-factor than for an exergy analysis. Coming back to the comparison of batch and continuous however, in Figs. 6 and 7, again the improvement of changing the process is clear and lies in the same order of magnitude as for the E-factor EEMM results.

3.4 Data intensive life cycle based evaluations (CEENE, CED, EI'99, IPCC 2007, EF)

The last evaluated EEMMs are grouped as life cycle based EEMMs and this includes EEMMs taking into account the full cradle-to-gate of the pharmaceutical production steps. Taking into account the full cradle-to-gate means more intensive data inventory is required. Similar as the results of the exergy analysis at the plant level, results can be presented (Fig. 8) by using the CEENE method at the cradle-to-gate level. In the exergy analysis at the plant level (Fig. 7), the resource consumption (exergy losses) were attributed to the sinks were they are used (lost). In the CEENE method, however, the resource consumption can also be attributed to the source the resources are coming from. As stated before, the highest impacts in Fig. 7 are linked to the use of fossil chemicals. This is confirmed in Fig. 8. The four other life cycle based EEMMs evaluated here show similar profiles with similar ratios between the process steps as presented in Fig. 8. In Table 1, the results of all the life cycle based EEMMs are presented and the improvements made by changing from batch to continuous production is given for the cumulative results of 1 mol F (stopping the evaluation after step 6) as well as for the cumulative results of 1 mol H (stopping the evaluations after step 8). First, it is clear that the improvements expressed in

Table 1: Impact reductions at step 6 and at step 8 for the five life cycle impact assessment methods.

BATCH CONTINU Impact BATCH CONTINU Impact

_1 mol F 1 mol F reduction_1 mol H 1 mol H reduction

CEENE (kJ) 2,97E+06 2,54E+06 16,75% 5,64E+06 4,39E+06 28,54% Carbon footprint IPCCGWP 2007 100a (kg CO2-eq) 9,99E+01 8,39E+01 19,10% 1,88E+02 1,43E+02 31,24% Cumulative energy demand (total) (MJ-eq) 2,48E+03 2,12E+03 16,96% 4,71E+03 3,65E+03 28,89% Ecoindicator EI'99 (H/A) total (points) 1,01E+01 8,61E+00 17,03% 1,88E+01 1,46E+01 29,33% _Ecological footprint (total) (mza) 2,66E+02 2,25E+02 18,40%_5,03E+02 3,86E+02 30,41%

Figure 8: CEENE of the eight production steps (cumulative results).

percentages are similar for all 5 life cycle impact assessment methods. Reason is the use of organic solvents in all production steps. Second, it is clear that the improvements quantified in percentages can change significantly (from 18% up to 30%) if more consecutive production steps (step 6 up to step 8) are taken into account. This is related to the cumulative effect of taking into account the yields of the consecutive production steps.

0 0

Post a comment