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|Title:||A novel continuous-time formulation for short-term scheduling of continuous processes|
|Authors:||Li, J. |
|Citation:||Li, J.,Susarla, N.,Karimi, I.A. (2008). A novel continuous-time formulation for short-term scheduling of continuous processes. AIChE 100 - 2008 AIChE Annual Meeting, Conference Proceedings : -. ScholarBank@NUS Repository.|
|Abstract:||Scheduling of continuous plants has received less attention compared to batch plants in the literature although it is substantially important in chemical industries such as petroleum industry. Because continuous plants have some key differences from batch plants, for instance, the processing times in continuous plants depend on unit-dependent processing rates, final product demand, and storage capacities, separate models are required for scheduling continuous processes. So far, several works (Karimi and Mcdonald, 1997; Mendez and Cerda, 2002; Reddy et al. 2004; Castro et al. 2004; Shaik and Floudas, 2007) in the literature have addressed this problem and developed various formulations based on different time representation such as unit slot-based (McDonald & Karimi, 1997), process slot-based (Reddy et al., 2004), global event-based (Castro et al., 2004), unit-specific event-based (Shaik and Floudas, 2007) and sequence-based (Mendez and Cerda, 2002). All these models incorporated different storage configurations such as unlimited, finite, dedicated, flexible, and no intermediate storage. In this paper, we develop a novel continuous-time formulation for short-term scheduling of continuous processes based on unit-slots (Pinto & Grossmann, 1995; Lim & Karimi, 2003). We incorporate various storage configurations such as unlimited, finite, dedicate, flexible and no intermediate storage policies. We solve several examples including the benchmark example of a fast moving consumer goods manufacturing plant, which is extensively studied in the literature, to evaluate the performance of our proposed model. The results show that our model requires fewer discrete variables and constraints and gets the same or better solutions with less computation times compared to existing models. We also use our model to investigate the effect of different modeling and computing considerations such as computer configurations, MIP solvers and software versions, etc.|
|Source Title:||AIChE 100 - 2008 AIChE Annual Meeting, Conference Proceedings|
|Appears in Collections:||Staff Publications|
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