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

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

A Framework for Nonlinear Model-Predictive Control Using Object-Oriented Modeling with a Case Study in Power Plant Start-Up

In this paper, nonlinear model predictive control (NMPC) is applied to the start-up of a combined-cycle power plant. An object-oriented first-principle model library expressed in the high-level language Modelica has been written for the plant and used to set up the simulation and optimization models. The NMPC optimization problems are both encoded, using a high-level notation, and solved in the open-source framework JModelica.org. The results demonstrate the effectiveness of the framework and its high-level description. It bridges the gap between an intuitive physical modeling format and state of the art numerical optimization algorithms. Promising closed-loop control results are shown for plant start-up when the NMPC model contains parametric errors and the simulation model, corresponding to the real plant, is subject to disturbances

Nonlinear Model Predictive Control in JModelica.org

In this thesis, a stronger support for Model Predictive Control (MPC) in JModelica.org has been implemented. JModelica.org is an open-source software for simulation and optimization of systems described by Modelica models. MPC is an optimization-based control strategy where one formulates an Optimal Control Problem (OCP) to describe the aim of the controller. At discrete time points the state of the system is estimated and the OCP is solved to find the optimal input to apply to the system. The main goal of this thesis has been to make the time it takes to obtain the optimal input as short as possible and also streamlining the setup of MPC in JModelica.org. This has been done by implementing an MPC class, which utilizes the fact that the structure of the OCP is the same in each consecutive sample for efficiency. Two different benchmarks, one on a smaller problem and one on a larger problem, shows that by using the new MPC framework we obtain similar results as before, but considerably faster. The total average computation time for one sample is decreased by almost 60% for the large problem and by almost 90% for the smaller problem