Ignacio Santín

TL;DR: In this paper, a two-level hierarchical control structure for biological wastewater treatment plants, with the goal of improving effluent quality and reducing operational costs, is presented, which allows to adjust the dissolved oxygen in the fifth tank (S O,5 ) according with the working conditions, instead of keeping it in a fixed value.

TL;DR: In this paper, the authors present the application of control strategies for wastewater treatment plants with the goal of effluent limits violations removal as well as achieving a simultaneous improvement in effluent quality and reduction of operational costs.

TL;DR: This work proposes the deployment of an artificial neural network (ANN)-based soft sensor in which a Long-Short Term Memory (LSTM) network is used to generate predictions of nitrogen-derived components, specifically ammonium (SNH) and total nitrogen (SNtot).

TL;DR: In this article, a hierarchical control structure is implemented to regulate the dissolved oxygen (DO) on the three aerated tanks, with a fuzzy controller that adapts the DO set points of the low level based on the NH concentration in the fifth tank (NH5), and low level is composed of three MPC controllers with feedforward control (MPC + FF).

TL;DR: In this paper, the effects of the main control actions used in wastewater treatment plants on the three main indicators used for performance evaluation: water quality, operational cost and, especially, greenhouse gas emissions are presented.