Introducing KIPET: A novel open-source software package for kinetic parameter estimation from experimental datasets including spectra

This paper presents KIPET (Kinetic Parameter Estimation Toolkit) an open-source toolbox for the determination of kinetic parameters from a variety of experimental datasets including spectra and concentrations. KIPET seeks to overcome limitations of standard parameter estimation packages by applying a unified optimization framework based on maximum likelihood principles and large-scale nonlinear programming strategies for solving estimation problems that involve systems of nonlinear differential algebraic equations (DAEs). The software is based on recent advances proposed by Chen et al. (2016) and puts their original framework into an accessible framework for practitioners and academics. The software package includes tools for data preprocessing, estimability analysis, and determination of parameter confidence levels for a variety of problem types. In addition KIPET introduces informative wavelength selection to improve the lack of fit. All these features have been implemented in Python with the algebraic modeling package Pyomo. KIPET exploits the flexibility of Pyomo to formulate and discretize the dynamic optimization problems that arise in the parameter estimation algorithms. The solution of the optimization problems is obtained with the nonlinear solver IPOPT and confidence intervals are obtained through the use of either sIPOPT or a newly developed tool, k_aug. The capabilities as well as ease of use of KIPET are demonstrated with a number of examples. (C) 2020 Elsevier Ltd. All rights reserved.