Modelling Framework for the Identification of Critical Variables and Parameters under Uncertainty in the Bioethanol Production from Lignocellulose

This study presents the development of a systematic modelling framework for identification of the most critical variables and parameters under uncertainty, evaluated on a lignocellulosic ethanol production case study. The systematic framework starts with: (1) definition of the objectives; (2) Collection of data and the implementation of dynamic models for each unit operation in the process; (3) Uncertainty and sensitivity analysis, performed to identify the critical operational variables and parameters in the process. The uncertainty analysis is carried out using the Monte-Carlo technique. Sensitivity analysis employs the standardized regression coefficient (SRC) method, which provides a global sensitivity measure, β i , thereby showing how much each parameter contributes to the variance (uncertainty) of the model predictions. Thus, identifying the most critical parameters involved in the process, suitable for further analysis of the bioprocess. The uncertainty and sensitivity analysis identified the following most critical variables and parameters involved in the lignocellulosic ethanol production case study. For the operating cost, the enzyme loading showed the strongest impact, while reaction volume showed a significant impact on the ethanol/biomass ratio. The results showed also that it is possible to find a better alternative operation of the plant in comparison with the base case. 3.3. For the sensitivity analysis, the standardized regression coefficient (SRC) method was used. Figure 5.a illustrates the linear model fit obtained when regressing the Monte-Carlo simulation outputs with respect to uncertainty parameters. Notice that the linear model determination coefficient (R 2 ) is equal to 1 which is quite high, meaning that the time-averaged model outputs could be linearized to a high degree, hence satisfying the requirement for β i to be used as a reliable index of the sensitivity measure (R 2 > 0.7). As far as ethanol/dry-biomass ratio is concerned, R 2 was found to be lower than 0.7 (R 2 = 0.271), indicating that β i cannot be used as a reliable index of sensitivity. Nevertheless, the correlation between reacting volume and ethanol/dry-biomass ratio was analyzed using scatter plots (see Figure 6.a), since it is qualitatively identifying the critical variables and parameters present in the process model. The ethanol/dry-biomass ratio increases when the reaction volume increases indicating a positive impact of reaction volume (within the studied range) on the ethanol yield. Another important point to highlight for some samples is that, even though the reacting volume in the SSCF section was reduced, the ethanol/dry-biomass ratio was not significantly compromised which requires further investigation in view of process optimization. Figure 6.b shows the correlation between enzyme loading type 1 with the ethanol/dry-biomass ratio, where a systematic trend was not found, thus implying that the impact of enzyme loading on ethanol yield was not significant within the studied range. For both operating variables (reaction volume and enzyme loading), a few outliers were observed in the scatter plots. This is probably due to a specific combination of the sampled input uncertainties which requires closer investigation.