Synthesis and optimization of the recovery route for residual products under uncertain product demand

The present work describes an optimization model for managing the recovery of residual products that originate at industrial plants. The framework for the proposed general network superstructure, where all possible process transformations, storage, transports and auxiliary operations are accounted for, is modeled using a maximal state task network representation. This framework is combined with the evaluation of a set of environmental impacts, quantified by metrics (for air, water pollution, etc.) through the minimum environment impact analysis methodology and is associated with waste generation at utility production and transportation levels. The final model is described as a mixed-integer linear programming model, which, once solved, is able to suggest the optimal processing and transport routes, while optimizing a given objective function and meeting design and environmental constraints. For each solution obtained, a stochastic flexibility index is computed, allowing for the drawing of trade-off curves for investment decision support. A motivating example is explored, based on the recovery of the sludge obtained from aluminum surface finishing plants. This example aims to maximize the quantity of recovered sludge and reflects the trade-off between the costs of disposal, processing, transport and storage, while accounting for the limits imposed on the associated environmental pollutants.