Productivity Optimization

In this chapter, the results for the achieved productivity of the continuous CPE of CA are discussed. The productivity was calculated according to Equation 2-15 for all conducted experiments. Hence, the response within the system varied between 34.8 and 113.6 (see table in appendix A 18). The fit of four different models was investigated for the productivity. As for the yield, the different model statistics are presented in appendix A 20.In order to model the productivity, a linear model was

recommended by Design-Expert. The r-squared value of the quadratic model was with 0.920 slightly higher than the r-squared value of the linear model with 0.903.

However, the linear model excelled in a lower standard deviation, PRESS and especially in a much better predicted R-squared value. Overall, the linear model had the better quality and was chosen as the model for the productivity. Although the cubic model had the highest R-squared, it was aliased and cannot be applied.

The “Sequential Model Sum of Squares” and “Lack of fit”-tests confirm the quality of the linear model for the productivity and are listed in appendix A 20 as well.

Hence, s linear equation was developed to model the productivity (Equation 6-3).

5_ˆ‰ = 37.3 ∙ ? − 0.1 ∙ − 9.0 ∙ ν + 90.7

Equation 6-3: Model equation for the productivity of cinnamic acid FCA

b: capacity; n: agitation speed; ν:feed-to-solvent ratio

According to the model equation, the productivity increases significantly with the column capacity. That was due to the elevated feed and solvent flows, and to the resulting increase in the raffinate and extract flows. Regarding the feed-to-solvent ratio, a high solvent flow positively affected the productivity. The reason for this was the yield since a higher surfactant concentration significantly increased the amount of extracted CA. Likewise the yield, a high surfactant concentration was beneficial for the productivity.

For assessment of the quality of the model, the normal probability plot and the residual vs. run plot were observed (appendix A 20). Both plots were similar to yield diagnostics. The normal probability plot indicated satisfactory results regarding linearity. However, a light S-shaped curve was be observed. That was due to the result of the max-to-min ratio of the productivity. Only a max-to-min of greater than 10 was an indicator for required transformation. However, only max-to-min values of 3 or lower had no significant influence on the model. That indicated that the max-to-min ratio of the productivity model with 3.26 had a small but negligible effect. There was one outlier in the measured data, which can be observed in both diagrams. However, according to the residual vs. run plot, this outlier was within the confidence level. Thus, all residuals show, as for the yield, only common-cause variation.

As already described, the column capacity and feed-to-solvent ratio had a significant influence on the productivity. This relation is illustrated on Figure 6.7.

Figure 6.7: Contour plots of the productivity (g·h^-1) as a function of the feed-to-solvent ratio (ν) and capacity (b) at agitation speed (n) = 40 rpm (plot a) and as a function of n and b at ν = 8 (plot b).

Colored for the productivity; at T = 40°C.

As in the model equation, the pronounced influence of the capacity on the productivity can be observed in Figure 6.7 a. However, an increased agitator speed influences the productivity (Figure 6.7 b) negatively. A high surfactant concentration and high column capacity could be maintained in case of low agitation. The agitator speed did not directly influence the productivity, but the impact on the stress limit was indirectly responsible for a decrease in the productivity.

Overall, the highest productivity was found along the border of the system approaching the stress limit. In contrast to the yield, which has a maximum that can not be exceeded, the productivity was ultimately depending on physical limitations when designing the column. At the highest surfactant concentration in the mixing zone, the multilinear constraints restricted a considerable area of the investigated system. Thus only a narrow window for the location of the optimal operating conditions remained. As a result, the highest productivity value was calculated at the lowest stirring speed and highest capacity. Hence, an optimum for the productivity could also be registered within the used parameter field.

The highest productivity was achieved at a column capacity of 2 l·h^-1, an agitator speed of 20 rpm and a feed-to-solvent ratio of 6. Whereby, a productivity of 109.3 g·h^-1 CA was obtained. An experiment, which was performed under the described operation conditions, was able to validate the model optimum by obtaining a

productivity of 113.6 g·h^-1. Thus, it was possible to develop an accurate linear model for estimation of the productivity in the chosen process window.

Finally, the statistical experimental design could be applied to optimize not a single response factor but also for locating operating conditions, which simultaneously assure a high productivity and yield. In case of an equal weighting for both responses, the optimized operating conditions were equivalent to the optimal productivity parameter combination. In this case, the yield was equal to 39.3 % and the productivity to 109.3 g/h, respectively. This parameter combination was defined as optimum for the tested process window of the continuous cloud point extraction of cinnamic acid.

Based on the results in this chapter, a stable operation of the continuous extraction could be maintained with an established cloud point system. Moreover, the optimal combination of the process parameters ensured mild temperature, low exposure to heat and stirring of the feed and minimized Triton X-114 loss with the raffinate.

Thus, these conditions were applicable for the continuous in situ extraction of dissolved biomaterials with Triton X-114.

However, Triton X-114 biphasic systems had a general drawback as a solvent for biomaterials. The surfactant has no permission for food, cosmetic or drugs.

Additionally, the aromatic ring in its molecule raised the environmental awareness regarding the amphiphile. Therefore, a potential cloud point extraction of a natural product with Triton X-114 would be disadvantageous. These issues lead to the need of alternative surfactants, which are permitted in market goods and commercially available. Therefore, the utilization of commercial-grade surfactant systems, suitable for the cloud point extraction, is presented in the next chapter.