JModelica---an Open Source Platform for Optimization of Modelica Models

Optimization is becoming a standard methodology in many engineering disciplines to improve products and processes. The need for optimization is driven by factors such as increased costs for raw materials and stricter environmental regulations as well as a general need to meet increased competition. As model-based design processes are being used increasingly in industry, the prerequisites for optimization are often fulfilled. However, current tools and languages used to model dynamic systems are not always well suited for integration with state of the art numerical optimization algorithms. As a result, optimization is not used as frequently as it could, or less efficient, but easier to use, algorithms are employed. This paper reports a new Modelica-based open source project entitled JModelica, targeted towards dynamic optimization. The objective of the project is to bridge the gap between the need for high-level description languages and the details of numerical optimization algorithms. JModelica is also intended as an extensible platform where algorithm developers, particularly in the academic community, may integrate new and innovative methods. In doing so, researchers gain access to a wealth of industrially relevant optimization problems based on existing Modelica models, while at the same time facilitating industrial use of state of the art algorithms. The JModelica project rests upon three pillars, namely a language extension of Modelica for optimization entitled Optimica, software tools, and applications. In this paper, these three topics will be highlighted.

[1]  Torbjörn Ekman,et al.  Implementation of a Modelica compiler using JastAdd attribute grammars , 2010, Sci. Comput. Program..

[2]  Johannes P. Schlöder,et al.  An efficient multiple shooting based reduced SQP strategy for large-scale dynamic process optimization: Part II: Software aspects and applications , 2003, Comput. Chem. Eng..

[3]  M. L. Chambers The Mathematical Theory of Optimal Processes , 1965 .

[4]  Johannes P. Schlöder,et al.  An efficient multiple shooting based reduced SQP strategy for large-scale dynamic process optimization. Part 1: theoretical aspects , 2003, Comput. Chem. Eng..

[5]  Karl Johan Åström,et al.  Evolution of Continuous-Time Modeling and Simulation , 1998, ESM.

[6]  L. Biegler,et al.  Advances in simultaneous strategies for dynamic process optimization , 2002 .

[7]  Anil V. Rao,et al.  Practical Methods for Optimal Control Using Nonlinear Programming , 1987 .

[8]  Johan Åkesson,et al.  Dynamic start-up optimization of a plate reactor with uncertainties , 2009 .

[9]  Vassilios Vassiliadis,et al.  Computational solution of dynamic optimization problems with general differential-algebraic constraints , 1993 .

[10]  Carol S. Woodward,et al.  Enabling New Flexibility in the SUNDIALS Suite of Nonlinear and Differential/Algebraic Equation Solvers , 2020, ACM Trans. Math. Softw..

[11]  Görel Hedin,et al.  JastAdd--an aspect-oriented compiler construction system , 2003, Sci. Comput. Program..

[12]  Mats Andersson,et al.  Object-Oriented Modeling and Simulation of Hybrid Systems , 1994 .

[13]  Peter A. Fritzson,et al.  Principles of object-oriented modeling and simulation with Modelica 2.1 , 2004 .

[14]  Johan Andreasson Enhancing active safety by extending controllability - How much can be gained? , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[15]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[16]  L. S. Pontryagin,et al.  Mathematical Theory of Optimal Processes , 1962 .

[17]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..