Automated Development of Manual Startup and Shutdown Procedures by a Non-linear Non-derivative Optimization Algorithm

Abstract

The most critical phases of plant operativity are the start-up and shutdown, which are usually implemented by following an experience-based sequential manual procedure. This work aims to develop an optimizing route for the unsteady states of a chemical plant through non-derivative local minimization algorithms. The proposed library for such development is NLopt, an open-source collection of optimization algorithms that can be implemented in C++ and Python languages. The definition of the problem followed a Monte Carlo initialization approach and optimization with a successive algorithm validation to test the optimizer potentiality. The case studies implemented describe common units in chemical plants and show the prospects of the route for the automation of such phases, in order to transform obsolete manual sequences into non-time-consuming and energy-saving routes to be implemented in plant activity.