Introducing uncertainty quantification to techno-economic models of manufacturing field-grown plant-made products

There is a growing demand for large-market natural and biotechnological products driven by shifting consumer preferences in food and calls for decentralized vaccine and medication production capabilities. The current paradigm of bioreactor-based biomanufacturing faces difficulties of scalability and a high entry barrier of capital intensity and workforce specialization. Field-grown plant-based manufacturing, as an inexpensive and readily scalable platform, is a promising strategy to meet this call. Despite some successes in field-grown bioproducts manufacturing, concerns, including process variability, have largely stymied adoption. Here we report on the development of techno-economic modeling coupled with Monte Carlo simulation as an effective tool to quantify, and mitigate, the impact of variation in field-grown plant-based manufacturing on profitability-related (internal rate of return, cost of goods) and process performance (product purity, annual throughput) forecast variables. In the base case, we observe 80.8% certainty of meeting all forecast variable specifications, defined generically to represent those of a high-volume food-grade commodity product. We observe an internal rate of return (with a selling price of $2275/kg bioproduct) as low as 10.7% and as high as 47.9% across facility scenarios. We also demonstrate optimization under uncertainty in a facility retrofitting to find a profitability-optimal chromatography column diameter of 1.2 m.