Valuation of Chemical Operations Under Uncertainty

This dissertation uses process synthesis formulations to develop models that explain how to value chemical processes and minimize financial risk. One of the main goals of this work is to transition from traditional deterministic models for price and demand into ones that capture the presence of uncertainty to provide plant owners and investors with financially sound managing/investing strategies. Three methodologies are being proposed as part of the approach to value chemical processes. The first is the construction of a one-period replicating portfolio using the binomial asset pricing model as a basis to describe chemical price dynamics. Additionally, it will be shown that when constructing a portfolio where the number of linearly independent asset prices exceeds the number of future states that these prices can attain, the value for the process will not be unique. The second procedure introduces the second order stochastic dominance (SSD) criterion which provides a less conservative bound on the process cost. The criterion incorporates information about the investor's attitude towards risk. SSD also generalizes a widely used measure of risk, Value at Risk (VaR). Finally, this analysis will be extended to multiple time periods, which will provide the investor with the ability to make decisions at different stages of the investment horizon. This will be achieved primarily with the development of a real-time optimization framework known as Model Predictive Control (MPC). The formulation will be stochastic and subject to Conditional Value at Risk (CVaR) as a risk management technique.