(733d) An Integrated Chemical Site Planning and Scheduling Framework---Model and Algorithm

Rajagopalan, S., Carnegie Mellon University
Amaran, S., Dow Inc.
Sahinidis, N., Carnegie Mellon University
Bury, S. J., Dow Inc.
A wide range of math programming frameworks are available for planning and scheduling of multiproduct batch and continuous processes chemical plants, notably state-task network (STN) [1,2], resource-task network (RTN) [3], and corresponding variants thereof---[4], [5], and [6], to name a few. There are also superstructure and flowsheet-based frameworks that incorporate appropriate connectivity aspects as well as proposed recently by [7] and [8]. However, such unified frameworks for site-wide modeling of integrated chemical sites are either limited in scope or were developed as a part of an application, and thus, are not necessarily general---[9], [10], [11], and [12], for instance. In this work, we propose a modeling framework for enterprise-wide planning of integrated chemical sites that can suitably be applied for long-term as well as medium-term strategic and tactical planning. We also propose solution strategies to tackle such planning problems via a mixed-integer linear programming (MILP) model.

In particular, the model framework simultaneously captures (1) different discrete and continuous operating modes of production units; (2) storage facilities including tanks, reservoirs, pipelines, rail cars, and barges and associated time delay and rate constraints; (3) handling of redundancy of utilities such as steam through smoothing and symmetry breaking methods; (4) production ramping constraints, recycles, and residence time delays; (5) maintenance and turnaround outages and downtimes; (6) reliability of production units including unplanned events and yield and selectivity degradation; (7) transport, supply and demand constraints; and (8) economics. The model is generalized to flexibly handle time-dependent and uncertainty-dependent data with multiple time scales. We discuss algorithmic aspects such as heuristics and relaxations, preprocessing techniques, and possible reformulations for different model components. We provide computational results on a large industrial-size test case.


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