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Processes 2018, 6(8), 106; https://doi.org/10.3390/pr6080106

GEKKO Optimization Suite

Department of Chemical Engineering, Brigham Young University, Provo, UT 84602, USA
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Received: 1 July 2018 / Revised: 19 July 2018 / Accepted: 23 July 2018 / Published: 31 July 2018
(This article belongs to the Special Issue Process Modelling and Simulation)
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Abstract

This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This paper introduces the GEKKO Optimization Suite, presents GEKKO’s approach and unique place among AMLs and optimal control packages, and cites several examples of problems that are enabled by the GEKKO library. View Full-Text
Keywords: algebraic modeling language; dynamic optimization; model predictive control; moving horizon estimation algebraic modeling language; dynamic optimization; model predictive control; moving horizon estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Beal, L.D.R.; Hill, D.C.; Martin, R.A.; Hedengren, J.D. GEKKO Optimization Suite. Processes 2018, 6, 106.

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