(35e) Developing a Systemic Methodological Framework for the Sustainability Assessment of Biobased Fuels and Chemicals
AIChE Annual Meeting
Sunday, November 13, 2016 - 5:10pm to 5:35pm
From this perspective, the aim of this study is (i) to present the methodological steps for the development of a model-based database of LCA related sustainability impacts in biomass based production, (ii) to demonstrate the use of this database in single- and integrated multi-production lines and (iii) to provide a first generation of surrogate models that correlate available input to desirable output process parameters and assessment metrics with computationally inexpensive algebraic forms.
In particular, this study presents detailed cradle-to-gate inventory analysis in biomass based production using protocols for process flowsheeting and filling of data gaps (e.g., for cradle-to-gate production steps and waste treatment of process effluents), considers various environmental impact allocation methods for the resulting joint and co-production biorefinery systems, applies a wide list of sustainability impact assessment metrics and tests multiple surrogate modelling approaches (i.e., from simple linear regression to partial least squares, Kriging and neural network approaches) on the basis of model generated data and sampling plans.
The overall methodological framework comprises an accessible, modular and expandable database of biobased production lines, including mass and energy balances, other process related information and various allocation approaches for the calculation of life cycle inventories in single- and multi-production systems. The resulting data structures are integrated with a reporting mechanism which offers information about the environmental impacts expressed by relevant indicators proposed by life cycle impact assessment methods such as the RECIPE and Cumulative Energy Demand methods and provides an environment for benchmarking alternative choices.
Molreover, metrics of â??green chemistryâ?Â assessing the efficiency of processes (yield, mass intensity, E-factor etc.) as well as other process-related variables areÂ extracted from the data structuresÂ aiming at the development of a first generation of surrogate modelsÂ to provide guidance forÂ a fast screening ofÂ innovative biobased processes and products.
The data structures, assessment results and surrogate modelling performance is demonstrated for a series of platform chemicals (e.g., syngas, sugars and lignin) and biofuels (e.g., biodiesel, biogas, and alcohols), starting from diverse biomass sources (e.g., wood chips, wheat straw, vegetable oil) and biomass availability scenarios, in single and integrated multi-production lines.