Machine learning for surrogate process models of bioproduction pathways
Problem
Detailed process simulation models for biofuel and biochemical production are costly and computationally intensive, limiting sharing and reproduction.
Why it matters
Technoeconomic and life-cycle analyses guide research and investment decisions for sustainable biofuel production at scale.
What the AI did
Used an automated machine learning approach to build surrogate models that approximate the cost, mass and energy balance outputs of conventional process simulations.
Results
Surrogate models demonstrated to be accurate and effective for approximating cost, mass and energy outputs; no numeric metrics stated in abstract.
Impact
Evidence shows surrogates can approximate process model outputs, but accuracy is only qualitatively stated.
Record
| Venue | Bioresource Technology |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Tyler Tarr Huntington, Nawa Raj Baral, Minliang Yang, Eric R. Sundstrom, Corinne Donahue Scown |
| Citations | 36 (OpenAlex, at import time) |
| References | 40 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1016/j.biortech.2022.128528 |
| Open access | hybrid |
| Keywords | machine learning, surrogate models, technoeconomic analysis, biofuels, life-cycle assessment |
Importance rating
real but narrower advance
Technoeconomic and life-cycle analyses guide biofuel/biochemical research and investment decisions; surrogate models cut computational cost, a real but narrow methodological advance with no scale stated.
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