Optimal Operation of an Evaporation Process

Real-time optimization (RTO) aims to drive a process to its best efficient steady-state operation point. However, these optimizations often disregard long-term effects such as fouling, catalyst deactivation, etc., which leads to degraded operation or even equipment damage. This work deals with these issues in a multiple-effect evaporation process, with the goal of reducing the specific steam consumption. The proposed approach considers: a grey-box model of the process, data reconciliation to update the model, and RTO to search for the best operating point. The fouling effects arising in the heat exchangers are modeled as a function of the operation time and control decisions, and such model is integrated in the RTO. Moreover, fouling forces periodic stops for cleaning, with their corresponding associated costs. So, an economic optimization is proposed to predict the right day to perform maintenance tasks. Modern nonlinear-programming environments which include automatic differentiation have been used for implementation.