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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, βi, 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.
Original languageEnglish
Title of host publication32. National Meeting and First International Congress AMIDIQ
Publication date2011
StatePublished - 2011
EventAMIDIQ 32nd National Meeting and 1st International Congress - Riviera Maya, Mexico


ConferenceAMIDIQ 32nd National Meeting and 1st International Congress
CityRiviera Maya

Bibliographical note

© 2011 Academia Mexicana de Investigación y Docencia en Ingeniería Química AMIDIQ

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