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2
Y = 246.32 – 67.86X 1 + 1.446X 2 – 0.158X 3 + 4.504X 1 – 0.04795X 1X 2 + 0.03099X 1X 3
2 2
– 0.04966X 2 + 0.00096X 2X 3 – 0.00363X 3 (3.2)

The model presented a high determination coefficient (R = 0.994) explaining 99% of the
2
2
variability in the response and a high value of the adjusted determination coefficient (adjusted R
2
= 0.986) suggested a high significance of the model as shown in Table 3.4. In a good model, R ,
2
2
adjusted R and R for prediction should not be too different from each other. These results
showed that only NaHCO 3 concentration had significant individual effect on hydrogen
2
production yield (P ≤ 0.05). The quadratic model term of X 1 variable was highly significant (P <
0.0001) as shown in Table 3.5. The statistical analysis was carried out based on the experimental
data using a full quadratic model which was fitted to the data to obtained the regression equation
using the multiple regression tool in Essential Regression software version 2.210 (Saelee, 2010).
High hydrogen production yield achieved from weak and moderate conditions (runs 2, 5,
10, 14 and 15) with the hydrogen production yield ranged from 79.0±3.8 to 91.7±3.9 mL H 2/g-
VS added. The highest hydrogen production yield was 91.7±3.9 mL H 2/g-VS added (run 14) achieved
from moderate additions of NaHCO 3, Na 2HPO 4.12H 2O and EFB ash as shown in Table 3.6. At
the same time, the hydrogen production yield achieved from substrate control of 75.0±4.6 mL
H 2/g-VS added was obtained. Although, the hydrogen production yield achieved from the optimal
conditions (run 14) and achieved from substrate control was different significant with P ≤ 0.05.
Nevertheless, it is increased just only 22% which is still not uneconomic for applying in the
industrial scale when considered the economic cost of the external buffer and nutrients
supplemented. Thus, in this work was chosen co-fermentation of SLS: POME mixing ratio of
55:45 %v/v which without supplementation of additional nutrients as the optimal conditions for
both biohydrogen and biomethane production. The possible reasons for still having low
hydrogen production yield achieved from the optimal conditions is that the mixture would rather
contain sufficient nutrients are comprehensive phosphorus and potassium. Corresponding to the
results achieved from regression model which model coefficients related to X 1 and X 2 had not
significant individual effect on hydrogen production yield which was estimated by multiples
linear regression as shown in Table 3.5. Moreover, the other reason is that not initial pH suitable
for biohydrogen production as summarized in Table 3.6. The optimum pH for hydrogen
production was 5.4-5.7 (O-Thong et al., 2008; Mamimin et al., 2012), It can be concluded that
initial pH adjustment plays an important role in improving co-digestion of SLS to POME by
using dark fermentation for hydrogen production.