A prototype simulation-based optimization approach to model feedstock development for chemical process industry

•Simulation-based optimization approach is used for investment planning problem.•Decisions on capacity and R&D expenditure for biomass to commodity chemicals.•The source of the exogenous uncertainty is the level of the product demand.•Endogenous uncertainty is how the technology cost changes with investments.•Accurate product demand forecast is key to reducing total system cost uncertainty.

Incorporating non-traditional feedstocks, e.g., biomass, to chemical process industry (CPI) will require investments in research & development (R&D) and capacity expansions. The impact of these investments on the evolution of biomass to commodity chemicals (BTCC) system should be studied to ensure a cost-effective transition with acceptable risk levels. The BTCC system includes both exogenous, e.g., product demands (decision-independent) and endogenous, e.g., the change in technology cost with investment levels (decision-dependent) uncertainties. This paper presents a prototype simulation-based optimization (SIMOPT) approach to study the BTCC system evolution under exogenous and endogenous uncertainties, and provides a preliminary analysis of the impact of using three different sampling methods, i.e., Monte Carlo, Latin Hypercube, and Halton sequence, to generate the simulation runs on the computational cost of the SIMOPT approach. The results of a simplified case study suggest that annual demand increases is the dominant factor for the total cost of the BTCC system. The results also suggest that using Halton sequence as the sampling method yields the smallest number of samples, i.e., the least computational cost, to achieve a statistically significant solution.