##### Minimum Selling Price (MSP) Estimator

This tool uses an algorithm called pyTEA to
estimate MSP of biofuels and bioproduct. Details of pyTEA
and its prediction error are provided here.

pyTEA is a process-based simulation model designed to
approximate costs
of major supply and production stages for a given molecule.
While lacking the nuance and precision of
industry-grade software such as SuperPro Designer
or Aspen Plus, it aims to roughly emulate the methods of
these programs and generate comparable outputs
in a fast and lightweight manner.

To validate the accuracy of pyTEA and train spLearn,
we first ran 2,000
simulation trials of a SuperPro model.
For each trial, values of the
input parameters were drawn randomly from their respective
probability distributions. We treat the MSP values generated
SuperPro in these trials as the 'ground truth' as they are
derived from the most precise mechanistic method available.
We then used pyTEA to make predictions on each of the
2,000 simulation trials. The scatterplots below show the
correlations between the SuperPro MSP values for each trial
and the corresponding pyTEA prediction
for the same set of input parameter values.

##### pyTEA

A greater clustering of points around the y = x line
(dashed red) in these plots indicates closer agreement between
predicted and actual values, and thus higher prediction accuracy.
Quantitatively, we rely on
Mean Absolute Error
(MAE) as our primary metric of model prediction error.
Conceptually, MAE simply measures the average difference between
predicted and ground truth values and therefore offers easily
interpretable error margins in the same units as the target
variable. In terms of total MSP, pyTEA achieves an MAE of
1.18 $/gal.