Global parameter optimization for biokinetic modeling of simple batch experiments. (November 2016)

• Record Type: Journal Article
• Authors: Mašić, Alma; Udert, Kai M.; Villez, Kris
• Is Part Of: Environmental modelling & software. Volume 85(2016:Nov.)
• Page Start: 356
• Page End: 373
• Publication Date: 2016-11
• Subjects: Biotechnology -- Deterministic search -- Global optimality -- Kinetic modeling -- Nitrification -- Parameter estimation; AOB ammonia oxidizing bacteria -- DO dissoved oxygen -- NOB nitrite oxidizing bacteria -- ODE ordinary differential equations -- OUR oxygen uptake rate -- QP quadratic program -- TNN total nitrite nitrogen -- WLS weighted least squares -- WRMSR weighted root mean squared residuals; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.06.015
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Environmental process modeling is challenged by the lack of high quality data, stochastic variations, and nonlinear behavior. Conventionally, parameter optimization is based on stochastic sampling techniques to deal with the nonlinear behavior of the proposed models. Despite widespread use, such tools cannot guarantee globally optimal parameter estimates. It can be especially difficult in practice to differentiate between lack of algorithm convergence, convergence to a non-global local optimum, and model structure deficits. For this reason, we use a deterministic global optimization algorithm for kinetic model identification and demonstrate it with a model describing a typical batch experiment. A combination of interval arithmetic, reformulations, and relaxations allows globally optimal identification of all (six) model parameters. In addition, the results suggest that further improvements may be obtained by modification of the optimization problem or by proof of the hypothesized pseudo-convex nature of the problem suggested by our results. Graphical abstract: Highlights: Objective function bounds for global biokinetic parameter optimization are proven. Global deterministic parameter estimation of a six-parameter model is possible. Globally optimal parameters are obtained to fit the model to experimental data.