Model-based plant-wide optimization of large-scale lignocellulosic bioethanol plants
| Autor: | Jon Geest Jakobsen, Gürkan Sin, Remus Mihail Prunescu, Mogens Blanke |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
Engineering Environmental Engineering Uncertainty and sensitivity analysis 020209 energy Biomedical Engineering Lignocellulosic biomass Bioengineering 02 engineering and technology Raw material 01 natural sciences Profit (economics) Second generation bioethanol plant Nonlinear model-based optimization 010608 biotechnology Enzymatic hydrolysis 0202 electrical engineering electronic engineering information engineering C5 and C6 co-fermentation Process engineering Uncertainty analysis Waste management business.industry Biorefinery Process automation system Steam pretreatment Biofuel business Biotechnology |
| Zdroj: | Prunescu, R M, Blanke, M, Jakobsen, J G & Sin, G 2017, ' Model-based plant-wide optimization of large-scale lignocellulosic bioethanol plants. ', Biochemical Engineering Journal, vol. 124 . https://doi.org/10.1016/j.bej.2017.04.008 |
| Popis: | Second generation biorefineries transform lignocellulosic biomass into chemicals with higher added value following a conversion mechanism that consists of:pretreatment, enzymatic hydrolysis, fermentation and purification. The objective of this study is to identify the optimal operational point with respect to maximumeconomic profit of a large scale biorefinery plant using a systematic model-based plantwide optimization methodology. The following key process parameters areidentified as decision variables: pretreatment temperature, enzyme dosage in enzymatic hydrolysis, and yeast loading per batch in fermentation. The plant is treated in an integrated manner taking into account the interactions and trade-offs between the conversion steps. A sensitivity and uncertainty analysis follows at the optimal solution considering both model and feed parameters. It is found that the optimal point is more sensitive to feedstock composition than to model parameters, and that the optimization supervisory layer as part of a plantwide automation system has the following benefits: (1) increases the economical profit, (2) flattens the objective function allowing a wider range of operation without negative impact on profit, and (3) reduces considerably the uncertainty on profit. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|