A Scenario-Based Stochastic Programming Model for the Optimization of Process Integration Opportunities in a Pulp Mill

Preprint, 2008

Several studies show that substantial industrial energy savings can be achieved through process integration. The returns on such investments are, however, uncertain because of uncertainties in future energy prices and policies. This article presents a stochastic mixed-integer programming approach which enables the identification of robust process integration investments under uncertainty. The proposed approach is applied to the case of a pulp mill for which the complete optimization model is presented. The model is a scenario-based multistage stochastic programming model with the objective of maximizing the net present value of the investments. The model also enables the optimization of investment timing. We show as one important result that the probability distribution can be varied rather much without a change in the optimal solution. This implies that the stochastic programming approach is a valuable tool although the true probabilities for the future scenarios are not known.