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Predictive models for the monomeric yields of xylose, EH glucose and TFS as response variables for dilute-acid pretreatment-hydrolysis of triticale straw were developed based on the values of the input variables. The measured yields of xylose, EH glucose and TFS (Table 5-3) were fitted into quadratic model expressions to assess the effect of the independent process variables (factors) on the responses. The statistical significance of models and the effect of each factor on the responses were determined by an extended ANOVA analysis at 95% of confidence interval. Likewise, the fit of the models to describe suitably the experimental data was assessed by the lack-of-fit analysis. The ANOVA showed statistical significance (P ˂ 0.05) for all the predictive models of the responses and second order polynomial expressions were fitted to describe the experimental data according to the lack-of-fit analysis, except for the yield of xylose for 00T20 straw which could only be described by a linear model. Table 5-4 shows the results of the extended ANOVA and predictive models of the responses monomeric xylose, EH glucose and TFS yields The model expressions were simplified to the model terms with statistical significant for which “Prob > F” was lesser than 0.05 according to the P-values at significance level 95% of confidence (ANOVA). Predictive models in Table 5-4 depict the interaction effects of the independent parameters Temperature, acid concentration and residence time on the yields of xylose, glucose from EH and total fermentable.

Table 5-4: RSM based predictive models for the yields of monomeric xylose (XY), EH glucose (GY) and TFSy. Temperature (T), acid concentration (c) and residence time (t) are given in coded form.

Model equations Model equations

R² represents the determination coefficient.

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The lack of fit for the entire modeled responses was not significant, which imply that the experimental data could reasonably be described by the models. Additionally, the coefficients of determination (R²) of the models for EH glucose and TFS were satisfactory (0.84–0.96) to attribute the variability in the results to the variables of the process for all the responses except for the model describing xylose yield for 00T207 straw (R² 0.55. Table 5-4). In this case other factors such as sugar degradation products or oligomeric yields of sugars from pretreatment liquor could have had influence on the experimental data of monomeric xylose to be not consistent by the model depiction. The statistical significance of temperature, acid concentration and time, as simple factors, and their interaction on the responses were examined by the lack-of-fit analysis. Acid concentration was the factor that exerted the main influence for the responses of all the straw samples.

5.3.5.1 Xylose yield in pretreatment liquor

The summary of the ANOVA for monomeric xylose is given in Table-5.5. The model equations that predicted xylose yields (Table 5-4) based on the experimental data were plotted in three-dimensional surface responses (See Figure 5-1) as function of two independent variables whilst holding the third variable at a constant value (stationary point). Acid concentration showed to enhance xylose yield for all straws, particularly for BACCHUS (Figure 5-1 C). Residence time impacted the xylose yield in lesser extent but only on 01T43 and BACCHUS (Figure 5-1. Inserts A and C). The maximization of monomeric xylose in pretreatment liquor seemed to differ between straws regarding the pretreatment requirements. The maximal of xylose yield predicted by the models was comparable (11.7 – 12.3 g/100 g DRM) for 01T43 and BACCHUS, although BACCHUS straw seemed to demand higher acid concentration (0.6% (w/w)) than 01T43 if pretreatment temperature is held at 180C. BACCHUS straw appeared to require double the acid (0.6% (w/w)) and longer residence times (18 min) to reach the highest predictive yield of xylose (nearly 12 g/100 g DRM) at a pretreatment temperature of 180C (Figure 5-1. C) while 01T43 and 00T207 straws showed predictive yields lesser than 8 g/100 g DRM (Figure 5-1. Inserts A and B).

5.3.5.2 EH Glucose yield

The predictive model equations developed for EH glucose yield are summarized in Table 5-4. The respective results of the ANOVA for EH glucose is given in Table 5-6. The pretreatment parameters influencing the yield of glucose depended on the type of straw (Table 5-6). For example the glucose yield on BACCHUS was positively influenced mainly by temperature, but also by acid and residence time (Table 5-6). Higher values of glucose yield were therefore obtained by increasing these parameters up to certain values of temperature, after which point is a reduction in the glucose yields (quadratic negative effect). On the other hand, acid concentration was a major influence on the

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response for 00T207, whilst time did not have statistical significance effect (p 0.1556) for this straw (Table 6). Time and acid concentration significantly affected the glucose yield for 01T43 (Table 5-6).

Table 5-5: Summary of the estimate of model term significance (P-values; PF) at significance level 95% of confidence for monomeric xylose yield after pretreatment

Factor Xylose yield

P-values lesser than 0.05 indicate that model terms are statistically significant.

Table 5-6: Summary of the estimate of model term significance (P-values; PF) at significance level 95% of confidence for the yields of glucose from enzymatic hydrolysis (EH glucose) and total fermentable sugars (TFS).

Factor EH Glucose TFS

01T43 00T207 BACCHUS 01T43 00T207 BACCHUS

Model 0.0247 0.0112 0.0010 0.0062 0.0387 0.0005

P-values lesser than 0.05 indicate that model terms are statistically significant.

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Figure 5-1: Contour plots for the yield of monomeric xylose from pretreatment liquor for the straw samples (A) 01T43, (B) 00T207, and (C) BACCHUS.

Yields are plotted as a function of acid concentration and residence time (holding pretreatment temperature at 180C) in (A) and (C), and pretreatment temperature and acid concentration (holding residence time at 12 min) in (B). Yields are expressed in gram per 100 grams of dry raw material (DRM).

Acid conc. % (w/w)

time (min)

Temperature (C)

Acid % (w/w) time (min)

Acid conc. % (w/w)

Xylose yield (g/100 g DRM) Xylose yield (g/100 g DRM) Xylose yield (g/100 g DRM)

A B C

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The maximum predictive values for EH glucose yields and the pretreatment conditions that enable these yields can be examined from the contours plotted for the response (See Figure 5-2, inserts A, B and C). A complete recovery of EH glucose from 01T43 straw is predicted by the analysis (highest predictive yield near 39 g/100 g DRM; Figure 5-2. A). The predicted conditions leading to optimize EH glucose yield are temperature of 180C, acid concentration near 0.4% (w/w) and residence time of 12 min (Figure 5-2. A). Predictive maximal of EH glucose yields from 00T207 and BACCHUS straws are reasonably comparable between them (31.1 – 32.7 g/100 g DRM), corresponding to maximal recoveries of 72 and 79% of theoretical, respectively. Pretreatment conditions that result in the highest predictive yields from 00T207 and BACCHUS are temperatures between 185 and 190C, acid concentration between 0.4 and 0.5% (w/w) and residence time between 12 and 15 min (Figure 5-2).

Total fermentable sugars yield.

The model equations for the prediction of TFS yield are given in Table 5.4. The acid concentration (linear effect) exerted the main influence (P ˂ 0.008) on the TFS yield of all the straw samples (Table 5-6). The TFS response for BACCHUS was mainly affected by acid concentration, followed by temperature and time in linear fashion, as well as quadratic effects of time (Table 5-6). The effect of time was also statistically significant together with the quadratic term of acid concentration TFS response of 01T43 straw (Table 5-6). TFS yield for 00T207 was only influenced by acid concentration in a linear fashion significant at p ˂ 0.05 and pretreatment temperature p ˂ 0.1 (Table 5-6).

The predictive models generated could represent reasonably the measured TFS yields for all the straw samples as indicated for the not significance of the lack-of-fit (Table 5-6). However, the coefficients of determination (R²) that measured the variability in the outcome of the prediction around the mean (Table 5-4) indicated that 4, 9 and 16% of the variation of the models for BACCHUS, 01T43 and 00T207 could not be attributed to the process variables.

The unexpected variability of the prediction by the models could have possibly be generated by the presence of other components as oligomeric sugars and sugar degradation products that were present in the pretreatment liquor as well as xylose production after enzymatic hydrolysis but not quantified in the study. Levels of EH xylose yields up to 11.3 g/100 g DRM have been found using the same enzyme combinations at similar enzyme and solid loading with steam exploded triticale straw [36].

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Figure 5-2: Contour plots for yields of EH glucose and total fermentable sugars (TFS) for the feedstocks (A and D) 01T43, (B and E) 00T207, and (C and F) BACCHUS. EH glucose yield as a function of temperature and acid concentration (holding residence time at 12 mi) (A) and (C), and acid concentration and

residence time (holding pretreatment temperature at 190C) in (B). TFS yield as a function of acid concentration and residence time (holding pretreatment temperature at 180C) in (D) and (holding temperature at 190C) in (E), and pretreatment temperature and acid concentration (holding

residence time at 18 min) in (F). Yields expressed in gram per 100 grams of dry raw material (DRM).

Temperature (C)

Acid conc.% (w/w)

Temperature (C) EH Glucose yield(g/100 g DRM) EH Glucose yield (g/100 g DRM) EH Glucose yield (g/100 g DRM)

Acid conc.% (w/w)

A B C

Acid conc. % (w/w)

time (min)

Acid conc. % (w/w)

time (min)

Acid conc. % (w/w) Temperature (C)

TFS yield (g/100 g DRM) TFS yield (g/100 g DRM) TFS yield (g/100 g DRM)

Acid conc.% (w/w)

D E F

time (min)

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In general, the maximization of TFS yield from 01T43 reached higher values and required less severe pretreatment conditions, followed by 00T207 and BACCHUS. Pretreatment conditions that predictably maximize the TFS yield for each straw sample, as the main goal in the study, could be obtained by inspection of the contour plots for the TFS (Figure 5-2. Inserts D, E and F). Acid concentration of 0.4% (w/w), temperature of 180C and residence times between 13 and 15 min will maximize TFS yield from 01T43 straw (Figure 5-2. D). Acid concentrations near 0.5% (w/w), temperature and residence time of 190C and 12 min respectively will result in maximum yield for 00T207 straw (Figure 5-2. E). In the case of BACCHUS straw, acid concentration near 0.55% (w/w), temperature between 185 and 190C Pretreatment temperatures between 185 and 190C, 0.6%

(w/w) acid concentration and residence and of 18 min will maximize the response TFS (Figure 5-2. C).

Numerical optimization was performed on the second order model equations obtained for TFS yield to find the optimum dilute-acid pretreatment conditions that enable the release of the maximum yield of total fermentable sugars from pretreatment-hydrolysis of each straw under study.

The differentiation in pretreatment requirements to maximize each of the sugar yields (xylose, EH glucose and TFS) observed by the contour plots of the predicted model between straws was expected to be accentuated by optimization.