Switched constrained linear adaptive identifier for the trichloroethylene elimination in sequential upflow anaerobic sludge blanket

• Wastewater treatment using microbiological activity is a particular case of sequential methods.
• Sulfate reduction followed by decomposition of sulfated compounds was considered as the problem.
• An adaptive algorithm to obtain a suitable model of this process using neural networks is proposed.
• The adaptive model preserves the sequential nature of the process and the bounded nature of states.
• The neural network is proposed as a system identifier in terms of the hybrid systems theory.

Sequential processes appear naturally in all types of industries. Biotechnology is a good example of such schemes. Wastewater treatment using microbiological activity is a particular case having all the characteristics of sequential methods. Sulfate reduction as pre-treatment followed by the decomposition of sulfated compounds using adapted microorganisms is the sequential nonlinear process with state constrains analyzed in this paper. Modeling this procedure is still a difficult task because the number of elements involved in the reaction. This paper presents an adaptive algorithm to obtain a suitable model of this process using continuous neural networks. The adaptive model preserves the sequential nature of the process as well as the bounded nature of all states. The neural network is proposed as a system identifier in terms of the hybrid systems theory. The Lyapunov stability method is used to demonstrate the convergence of the identifier states to the real concentrations of the microbiological system. Experimental results and their corresponding simulation using the adaptive model based on neural networks confirm the theoretical results described in this paper.