No abstract

No abstract

Shell and tube heat exchangers (STHE) are the most common type of heat exchanger in preheat trains (PHT) of oil refineries and in chemical process plants. Most commercial design software tools for STHE assume uniform distribution over all tubes of a tube bundle. This leads to various challenges in the operation of the affected devices. Flow maldistribution reduces heat duty of STHE in many applications and supports fouling build-up in fluids that tend to particle, bio and crystallization fouling. In this paper, a fluid mechanics study about tube side flow distribution of crude oil and related hydrocarbons in two-pass PHT heat exchangers is described. It is shown that the amount of flow maldistribution varies significantly between the different STHE designs. Therefore, a parameter study was conducted to investigate reasons for maldistribution. For instance, the nozzles diameter, type and orientation were identified as crucial parameters. In consequence, simple design suggestions for reducing tube side flow maldistribution are proposed.

Trajectory optimization and model predictive control are essential techniques underpinning advanced robotic applications, ranging from autonomous driving to full-body humanoid control. State-of-the-art algorithms have focused on data-driven approaches that infer the system dynamics online and incorporate posterior uncertainty during planning and control. Despite their success, such approaches are still susceptible to catastrophic errors that may arise due to statistical learning biases, unmodeled disturbances or even directed adversarial attacks. In this paper, we tackle the problem of dynamics mismatch and propose a distributionally robust optimal control formulation that alternates between two relative-entropy trust region optimization problems. Our method finds the worstcase maximum-entropy Gaussian posterior over the dynamics parameters and the corresponding robust optimal policy. We show that our approach admits a closed-form backward-pass for a certain class of systems and demonstrate the resulting robustness on linear and nonlinear numerical examples.{ * } Equal Contribution.