Plant planning optimization under time-varying uncertainty: Case study on a Poly (vinyl chloride) plant

Planning optimization considering various uncertainties has attracted increasing attentions in the process industry. In the existing studies, the uncertainty is often described with a time-invariant distribution function during the entire planning horizon, which is a questionable assumption. Particularly, for long-term planning problems, the uncertainty tends to vary with time and it usually increases when a model is used to predict the parameter (e.g. price) far into the future. In this paper, time-varying uncertainties are considered in robust planning problems with a focus on a polyvinyl chloride (PVC) production planning problem. Using the stochastic programming techniques, a stochastic model is formulated, and then transformed into a multi-period mixed-integer linear programming (MILP) model by chance constrained programming and piecewise linear approximation. The proposed approach is demonstrated on industrial-scale cases originated from a real-world PVC plant. The comparisons show that the model considering varying-uncertainty is superior in terms of robustness under uncertainties