Sequential Bayesian Monte Carlo Model Discrimination (SBMCMD) framework has been previously proposed by the authors for the purpose of determining the underlying mechanisms of a system such as a chemical reaction (Masoumi et al., 2013). SBMCMD relies on sampling from the model parameters distribution using Markov Chain Monte Carlo, MCMC, methods. Effective tuning of MCMC methods, when applying to some nonlinear models, can be tedious and challenging. This limits using SBMCMD in many practical applications. The aim of this paper is to address this limitation and facilitate exploiting of the proposed framework with regards to nonlinear structured models. This is achieved by using adaptive random-walk Metropolis-Hasting method for sampling from the models parameter. This method is an adaptive MCMC algorithm that takes care of adjusting its parameters automatically. Two implementations of the adaptive SBMCMD framework have been presented and applied to case studies. Results of two implementations have been compared, and the effect of preliminary data has been discussed. (C) 2015 Elsevier Ltd. All rights reserved.