A Scenario-Based Stochastic Programming Model for the Optimization of Process Integration Opportunities in a Pulp Mill
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.
decision support analysis
multistage stochastic programming