(40a) Optimal Cleaning Scheduling and Control of Heat Exchanger Networks Under Fouling: Problem Formulation and Solution Strategy

Petroleum refining processes supply a large number of the world chemical commodities and energy demand. There is a strong push towards more efficient operation due to tight market requirements, competition with alternative energies and environmental regulations. There are still many sources of inefficiencies that can be addressed to improve refining operations performance, both from an economic and an environmental perspective. Among these sources, fouling of process units has a significant impact on energy recovery because the deposition of unwanted material over process surfaces significantly compromises the heat transfer of many process units. This particularly impacts the front-end heat exchanger network and energy recovery in the crude distillation unit (CDU) section, which processes essentially the entire world crude oil.

Fouling in heat exchangers, a slow dynamic process, reduces their thermal and hydraulic performance significantly, therefore mitigation alternatives are needed to recover the performance of the network and to maintain a profitable and safe operation. Two important and common mitigation alternatives are: the control of flow distribution in the network, and the periodic cleaning of the affected units. Both mitigation alternatives can be determined by the formulation and solution of a dynamic optimization problem over a long operating horizon (from months to a few years).

This work addresses two key aspects: i) a realistic representation of the problem, and ii) the efficient solution of the optimal cleaning scheduling and control problems. The starting point is a compact, radially distributed, but axially lumped, nonlinear dynamic heat exchanger model that considers the deposit growth rate, its composition and the radial heat transfer. An efficient formulation is obtained via a time horizon discretization into periods, and a continue time approach is used to model the transitions of states (e.g. “operating”, “being cleaned”) in the units. This approach allows solving simultaneously the scheduling and control problems. We also describe how scheduling decisions are modelled using this discretization approach and how this general formulation can handle simultaneous cleanings and different types of cleanings with various efficiencies. The second key aspect deals with the complicating variables and constraint in the formulation, and the complexity arising from the combinatorial nature of the problem due to the large number of possible cleaning schedules over long operating horizons. We propose a reformulation and relaxation of the binary variables and scheduling constraints using complementarity constraints. This allows solving the optimal cleaning scheduling problem for a fixed number of periods in a reasonable computational time.

An industrially significant case study is presented as a demonstration of how this formulation and this solution strategy can be used to solve important problems. The example also illustrates the potential economic savings resulting from an optimal cleaning schedule, and simultaneous optimisation of cleaning schedule and flow rate control.