Gekko (optimization software)
| File:Gekko logo.png | |
| Developer(s) | Logan Beal |
|---|---|
| Stable release | 0.0.4
/ February 4, 2018 |
| Engine | |
| Operating system | Cross-Platform |
| Type | Technical computing |
| License | Apache |
| Website | gekko |
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The GEKKO Python package solves mixed-integer and differential algebraic equations with nonlinear programming solvers (IPOPT, APOPT, BPOPT, SNOPT, MINOS). Modes of operation include data reconciliation, real-time optimization, dynamic simulation, and nonlinear model predictive control. In addition, the package solves Linear programming (LP), Quadratic programming (QP), Quadratically constrained quadratic program (QCQP), Nonlinear programming (NLP), Mixed integer programming (MIP), and Mixed integer linear programming (MILP). GEKKO is available in Python.
GEKKO works on all platforms (Windows, MacOS, Linux, ARM processors) and with Python 2.7 and 3+. By default, the problem is sent to a public server where the solution is computed and returned to Python. There is Windows option to solve without an Internet connection. GEKKO is an extension of the APMonitor Optimization Suite but has integrated the modeling and solution visualization directly within Python. Applications include cogeneration (power and heat)[1], drilling automation[2], severe slugging control[3], solar thermal energy production[4], solid oxide fuel cells[5][6], flow assurance [7], Enhanced oil recovery [8], Essential oil extraction[9], and Unmanned Aerial Vehicles (UAVs)[10]. There are many other references to APMonitor and GEKKO as a sample of the types of applications that can be solved. GEKKO is developed from the National Science Foundation (NSF) research grant #1547110 [11][12][13][14] and is detailed in a Special Issue collection on combined scheduling and control[15]. Other notable mentions of GEKKO are the listing in the Decision Tree for Optimization Software[16], added support for APOPT and BPOPT solvers[17], projects reports of the online Dynamic Optimization course from international participants[18]. GEKKO is a topic in online forums where users are solving optimization and optimal control problems[19][20]. GEKKO is used for advanced control in the Temperature Control Lab (TCLab)[21] for process control education at 20 universities[22][23][24][25].
See also[edit]
References[edit]
- ↑ Mojica, J. (2017). "Optimal combined long-term facility design and short-term operational strategy for CHP capacity investments". Energy. 118: 97–115. doi:10.1016/j.energy.2016.12.009.
- ↑ Eaton, A. (2017). "Real time model identification using multi-fidelity models in managed pressure drilling". Computers & Chemical Engineering. 97: 76–84. doi:10.1016/j.compchemeng.2016.11.008.
- ↑ Eaton, A. (2015). "Post-installed fiber optic pressure sensors on subsea production risers for severe slugging control" (PDF). OMAE 2015 Proceedings, St. John's, Canada.
- ↑ Powell, K. (2014). "Dynamic Optimization of a Hybrid Solar Thermal and Fossil Fuel System". Solar Energy. 108: 210–218. doi:10.1016/j.solener.2014.07.004.
- ↑ Spivey, B. (2010). "Dynamic Modeling of Reliability Constraints in Solid Oxide Fuel Cells and Implications for Advanced Control" (PDF). AIChE Annual Meeting Proceedings, Salt Lake City, Utah.
- ↑ Spivey, B. (2012). "Dynamic modeling, simulation, and MIMO predictive control of a tubular solid oxide fuel cell". Journal of Process Control. 22: 1502–1520. doi:10.1016/j.jprocont.2012.01.015.
- ↑ Hedengren, J. (2018). "New flow assurance system with high speed subsea fiber optic monitoring of pressure and temperature". ASME 37th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2018/78079, Madrid, Spain.
- ↑ Udy, J. (2017). "Reduced order modeling for reservoir injection optimization and forecasting" (PDF). FOCAPO / CPC 2017, Tuscon, AZ.
- ↑ Valderrama, F. (2018). "An optimal control approach to steam distillation of essential oils from aromatic plants". Computers & Chemical Engineering.
- ↑ Sun, L. (2013). "Optimal Trajectory Generation using Model Predictive Control for Aerially Towed Cable Systems" (PDF). Journal of Guidance, Control, and Dynamics.
- ↑ Beal, L. (2018). "Integrated scheduling and control in discrete-time with dynamic parameters and constraints". Computers & Chemical Engineering. doi:10.1016/j.compchemeng.2018.04.010.
- ↑ Beal, L. (2017). "Combined model predictive control and scheduling with dominant time constant compensation". Computers & Chemical Engineering. doi:10.1016/j.compchemeng.2017.04.024.
- ↑ Beal, L. (2017). "Economic benefit from progressive integration of scheduling and control for continuous chemical processes". Processes. 5. doi:10.3390/pr5040084.
- ↑ Petersen, D. (2017). "Combined noncyclic scheduling and advanced control for continuous chemical processes". Processes. 5. doi:10.3390/pr5040083.
- ↑ Hedengren, J. (2018). "Special issue: combined scheduling and control". Processes. doi:10.3390/pr6030024.
- ↑ Mittleman, Hans (1 May 2018). "Decision Tree for Optimization Software". Plato. Arizona State University. Retrieved 1 May 2018.
Object-oriented python library for mixed-integer and differential-algebraic equations
- ↑ "Solver Solutions". Advanced Process Solutions, LLC. Retrieved 1 May 2018.
GEKKO Python with APOPT or BPOPT Solvers
- ↑ Everton, Colling. "Dynamic Optimization Projects". Petrobras. Petrobras, Statoil, Facebook. Retrieved 1 May 2018.
Example Presentation: Everton Colling of Petrobras shares his experience with GEKKO for modeling and nonlinear control of distillation
- ↑ "APMonitor Google Group: GEKKO". Google. Retrieved 1 May 2018.
- ↑ "Computational Science: Is there a high quality nonlinear programming solver for Python?". SciComp. Retrieved 1 May 2018.
- ↑ Kantor, Jeff (2 May 2018). "TCLab Documentation" (PDF). ReadTheDocs. University of Notre Dame. Retrieved 2 May 2018.
pip install tclab
- ↑ Kantor, Jeff (2 May 2018). "Chemical Process Control". GitHub. University of Notre Dame. Retrieved 2 May 2018.
Using the Temperature Control Lab (TCLab)
- ↑ Hedengren, John (2 May 2018). "Advanced Temperature Control Lab". Dynamic Optimization Course. Brigham Young University. Retrieved 2 May 2018.
Hands-on applications of advanced temperature control
- ↑ Sandrock, Carl (2 May 2018). "Jupyter notebooks for Dynamics and Control". GitHub. University of Pretoria, South Africa. Retrieved 2 May 2018.
CPN321 (Process Dynamics), and CPB421 (Process Control) at the Chemical Engineering department of the University of Pretoria
- ↑ "CACHE News (Winter 2018): Incorporating Dynamic Simulation into Chemical Engineering Curricula" (PDF). CACHE: Computer Aids for Chemical Engineering. University of Texas at Austin. 2 May 2018. Retrieved 2 May 2018.
Short Course at the ASEE 2017 Summer School hosted at SCSU by Hedengren (BYU), Grover (Georgia Tech), and Badgwell (ExxonMobil)
External links[edit]
- GEKKO Documentation
- GEKKO Source Code
- GEKKO on PyPI for Python pip install
- GEKKO is open-source product of National Science Foundation (NSF) research grant 1547110
- References to APMonitor and GEKKO in the literature
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