This paper introduces an anaerobic digestion identification and control scheme, based on
adaptive, on-line trained neural networks. Anaerobic digestion is a complex, nonlinear …

Three adaptive neural network control structures to regulate a biological wastewater
treatment process are introduced: indirect, inverse model, and direct adaptive neural control …

Identification and control of nonlinear processes can be achieved using neural networks.
However, previous work extending the identification to the on-line adaptive case has …

In this work several feed-forward back propagation neural networks (FFBP) were trained in
order to model, and subsequently control, methane production in anaerobic digesters. To …

In this paper, we develop an indirect adaptive control structure based on recurrent neural
networks. An adaptive emulator inspired from the Real Time recurrent Learning algorithm is …

This paper deals with the application of a neural controller for temperature control of a batch
reactor. The term “neural controller” is used to refer to a multilayer neural network which …

The paper studies the design and analysis of a neural adaptive control strategy for a class of
square nonlinear bioprocesses with incompletely known and time-varying dynamics. In fact …

This study aims to investigate the use of machine learning in the field of anaerobic digestion.
This process involves breaking down organic matter without oxygen to produce biogas. In …

Adaptive non‐linear controller designs for various controlled variables are evaluated for the
operation of the anaerobic digestion process. The selected controlled variables are the …

This work deals with the design and analysis of some nonlinear and neural adaptive control
strategy for a lactic acid production that is carried out in continuous stirred tank bioreactors …