A Self-Optimizing Strategy for Optimal Operation of a Preheating Train for a Crude Oil Unit

摘要

We apply a recently developed approach for optimizing heat exchanger networks with stream splits to the case study of a preheating train of the crude oil unit at the Mongstad Refinery in Norway. To maximize heat transfer, we adjust the split such that the "Jaschke Temperatures" assume equal values for each branch. For a branch with one heat exchanger, the Jaeschke Temperature is calculated as T_J=((T-T_0)~2)/(T_h-T_0), where T is the temperature of the split stream at the heat exchanger exit, and T_h, and T_0 are inlet temperatures of the hot and cold stream, respectively. Controlling the Jaschke Temperatures to equal values gives near-optimal operation despite varying flow rates, stream temperatures, and heat transfer coefficients. We fitted a model to plant data obtained from the refinery, and consider two cases with decentralized PI control, and one case where the Jaschke Temperatures are controlled by a model predictive controller. Our paper demonstrates that controlling the Jaeschke Temperatures of each branch to equal values is a simple alternative to online real-time optimization methods. Moreover, it is significantly cheaper to implement as an online optimizer, and it is easier to maintain.