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.

本文介绍了KIPET（动力学参数估算工具包），这是一个开放源代码工具箱，用于从各种实验数据集中（包括光谱和浓度）确定动力学参数。基佩通过应用基于最大似然原理的统一优化框架和大规模非线性规划策略来解决涉及非线性微分代数方程组（DAE）的估算问题，力图克服标准参数估算程序包的局限性。该软件基于Chen等人提出的最新进展。（2016），并将其原始框架纳入从业人员和学者的可访问框架。该软件包包括用于数据预处理，可估计性分析以及确定各种问题类型的参数置信度的工具。另外KIPET引入了有益的波长选择，以改善拟合度不足。所有这些功能都已通过Python的代数建模软件包Pyomo实现。KIPET利用的灵活性Pyomo制定和离散的参数估计算法中出现的动态优化问题。使用非线性求解器IPOPT获得优化问题的解决方案，并通过使用sIPOPT或新开发的工具k_aug获得置信区间。大量示例演示了KIPET的功能和易用性。