(612g) Scheduling of Continuous Chemical Production Considering Transient Operations

Scheduling of continuous chemical production has been extensively studied over the last three decades, and numerous mathematical models based on, for example, state-task network (STN) or resource-task network (RTN) representations, have been proposed [1-3]. However, despite extensive research in the area, modeling continuous chemical production with transient operations (e.g., startups, shutdowns, and transitions) remains an open challenge. On the one hand, integrated scheduling–dynamic optimization approaches are usually computationally expensive [4,5], which limits their applicability to small systems. On the other hand, standalone scheduling approaches usually rely on coarse approximations [6,7], which might lead to solutions that might not be implantable in practice.

To balance computational efficiency and accuracy, we propose an optimization framework for the scheduling of continuous processes considering transient operations. The proposed framework enables the modeling of transient operations without directly incorporating process dynamics. We first generalize the concept of processing tasks to represent transient operations. Model parameters for the tasks corresponding to transient operations are systematically generated. We further introduce a new mixed-integer linear programming model based on the STN representation, thus enabling wide applicability to various continuous processes. Moreover, this optimization model is extended to account for the operational flexibility of linear dynamic systems, whose dynamic functions are affine with respect to both input and state variables. First, for steady-state operations, the proposed extension allows both batch sizes and the conversion coefficients to change over time without introducing any bilinear terms. Second, for transient operations, the extension allows a unit to operate along dynamic trajectories obtained by interpolating among predefined dynamic trajectories. Finally, we propose a range of solution methods to improve the computational efficiency of the proposed models. Through several representative case studies, we show the applicability and performance of the proposed optimization framework.

References

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