Integration of CasADi and JModelica.org

This paper presents the integration of two open source softwares: CasADi, which is a framework for efficient evaluation of expressions and their derivatives, and the Modelica-based platform JModelica.org. The integration of the tools is based on an XML format for exchange of DAE models. The JModelica.org platform supports export of model in this XML format, whereas CasADi supports import of models expressed in this format. Furthermore, we have carried out comparisons with ACADO, which is a multiple shooting package for solving optimal control problems. CasADi, in turn, has been interfaced with ACADO Toolkit, enabling users to define optimal control problems using Modelica and Optimica specifications, and use solve using direct multiple shooting. In addition, a collocation algorithm targeted at solving large- scale DAE constrained dynamic optimization problems has been implemented. This implementation explores CasADi’s Python and IPOPT interfaces, which offers a convenient, yet highly efficient environment for development of optimization algorithms. The algorithms are evaluated using industrially relevant benchmark problems

A CasADi Based Toolchain For JModelica.org

Computer-aided modeling for simulation, optimization and analysis is increasingly used for product development in industry today, resulting in high demands on the tools used. A tool chain for transferring interpreted code of the modeling languages Modelica and Optimica from the simulation and optimization tool JModelica.org to CasADi has been implemented. CasADi provides several desirable features, most importantly an integrated and ecient automatic dierentiation engine and the ability to interactively work with the systems expressed using it. The biggest problems solved to enable this were the creation of a representation of the mathematical systems described by Modelica and Optimica code that is integrated with CasADi, and the construction of a transfer scheme for moving information from the Java-based JModelica.org compiler to C++ in which CasADi resides. This was successfully achieved for a continuous subset of Modelica and Optimica that may contain functions

Dynamic Parametric Sensitivity Optimization Using Simultaneous Discretization in JModelica.org

Dynamic optimization problems involving parametric sensitivities, such as optimal experimental design, are typically solved using shooting-based methods, while leveraging numerical integrators with sensitivity computation capabilities. In this paper we present how simultaneous discretization can be employed to solve these problems, by augmenting the dynamic optimization problems with forward sensitivity equations. We present an implementation of this approach in the open-source, Modelica-based tool JModelica.org, which addresses the need for solving optimal experimental design problems in Modelica tools. The implementation is demonstrated on a fed-batch reactor and a plate-fin heat exchanger

Collocation Methods for Optimization in a Modelica Environment

The solution of generic dynamic optimization problems described by Modelica, and its extension Optimica, code using direct collocation methods is discussed. We start by providing a description of dynamic optimization problems in general and how to solve them by means of direct collocation. Next, an existing implementation of a collocation algorithm in JModelica.org, using CasADi and IPOPT, is presented. The extensions made to this implementation are reported. The new implementation is compared to an old C-based collocation algorithm in JModelica.org in two benchmarks. The presented benchmarks are based on a continuously stirred tank reactor and a combined cycle power plant. The new algorithm and its surrounding framework is more flexible and shown to be several times more efficient than its predecessor

Integration of modelica-based models with the JModelica Platform

In this thesis, an introduction to Nonlinear Model Predictive Control (NMPC) combining both theory and application is presented. The basis of the NMPC is described to give a general idea how this process control strategy works. For this purpose, two test casestudies are implemented. The first one is a simple two tanks in series to get acquainted with open-source platform the JModelica.org and the Optimica extension.Finally, a more complex example, which consists of solving a start-up problem of a steam boiler using the JModelica framework, is provided. The results demonstrate the effort saved in solving an optimal control problem when using the JModelica framework in comparison to individual, case-specifically arranged solutions.Outgoin

Start-up Optimization of a Combined Cycle Power Plant

In the electricity market of today, with increasing demand for electricity production on short notice, the combined cycle power plant stands high regarding fast start-ups and efficiency. In this paper it has been shown how the dynamic start-up procedure of a combined cycle power plant can be optimized using JModelica. org, proposing a way to minimize the start-up time while maximizing the power production during start-up. The physical models have been developed in Modelica, adapted to suit optimization purposes and extended to optimization problems. Constraints keeping the lifetime consumption of critically stressed components under control are limiting factors. The plant models have been successfully optimized to full load

Modeling and Optimization of Grade Changes for Multistage Polyethylene Reactors

Grade changes in polyethylene reactors, i.e., changes of operating conditions, are performed on a regular basis to adapt to market demands. In this paper, a dynamic optimization procedure is presented built upon the Modelica language extended with Optimica constructs for formulation of optimization problems. A Modelica library for the Borstar R multistage polyethylene reactors at Borealis AB, consisting of two slurry and one gas phase reactor, has been constructed. Using JModelica.org, a framework to translate dynamic optimization problems to NLP problems, optimal grade transitions between grades currently used at Borealis AB, can be calculated. Optimal inflows and grade key variables are shown