Simultaneous optimal control and optimal cleaning scheduling of small heat exchanger networks subject to fouling

Abstract

Refinery operations are responsible for a high fraction of the energy used in the world and have a significant environmental impact on account of CO2 emissions. One of the major causes reducing their energy efficiency is fouling, the deposition of unwanted material over the surface of heat transfer units. The effects of fouling are more evident in the preheat train of the crude distillation unit where the thermohydraulic efficiency can decrease rapidly over time and many cleaning actions or other control-based mitigations alternatives have to be implemented. The optimal cleaning scheduling and optimal control problems are typically addressed separately. The former has been usually addressed using simple models and heuristics or stochastic algorithms, due to the complexity of MINLP formulations with other than unrealistically simple models. This paper presents a novel formulation and mathematical programming approach for fouling mitigation that treats simultaneously the optimal control problem of the network and the optimal cleaning scheduling, with realistic dynamic fouling models. The NLP and MINLP optimization problems are solved via deterministic optimization algorithms. Using two small examples it is shown that the simultaneous strategy has the potential to reduce operation cost by more than 10% over and above the use of individual strategies.