Techno-economic process modelling and Monte Carlo simulation data of uncertainty quantification in field-grown plant-based manufacturing

Published: 16-04-2021| Version 1 | DOI: 10.17632/h5s7rz29vg.1
Contributors:
Kirolos Kelada,
Debashis Paul,
Somen Nandi,
Karen McDonald

Description

The data can be grouped as follows: 1.1 Generation of techno-economic data under uncertainty The input parameter assumption distributions and associated Monte Carlo sampling-based trial data for the base case techno-economic process model are described in the data file, 0.1 Assumption distribution & trial data. These assumptions distribution trial data feed into the techno-economic process model (publicly available at http://mcdonald-nandi.ech.ucdavis.edu/tools/techno-economics/) to generate the forecast variable output data in the base case scenario and facility oversizing scenarios (in which the equipment of the facility is sized larger to accommodate the uncertainty of production). This is described in data file, 02. Simulation trial data. The details of the equipment oversizing scenarios of the techno-economic process model to accommodate the uncertainty of production are described in the data file, 03. Equipment oversizing specification. 1.2 Analysis of techno-economic forecast variable outputs The forecast variable output data are compared between the base case and facility oversizing scenarios using two-sample t-tests for evaluation of the means and Kolmogorv-Smirnov tests for evaluation of the distributions, which is summarized in the data file, 04. Statistical test results. Box plots and quantile-quantile plots are shown in the data file, 05. Forecast variable normality, as assessments of normality. Univariate sensitivity of the forecast variables to the input parameters is investigated using tornado plots and spider charts in the data files, 06. Forecast univariate sensitivity, data, and 07. Forecast univariate sensitivity, charts. The contribution to variance of each input parameter to each forecast variable is calculated by rank correlation coefficient using Monte Carlo-based techno-economic simulation run data for the base case in which Pearson correlation coefficients are not included, the results of which are described in the data file, 08. Contribution to variance. Techno-economic output metrics are generated in the techno-economic modelling software using input parameter values associated with Monte Carlo-based techno-economic simulation trials that yielded the minimum, mean, and maximum values of internal rate of return after tax for the base case and facility oversizing scenarios, as described in the data file, 09. Cost breakdowns. 1.3 Techno-economic optimization under uncertainty A facility retrofitting case which presumes that the cation exchange chromatography is a new addition to an existing facility is approached with the base case facility sizing assumed to be fixed and the cation exchange chromatography column diameter is set as a decision variable to minimize internal rate of return after tax, as described in the data file, 10. Simulation results summary (CEX size optimization).

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