In this paper the environmental impacts of 10 control strategies implemented in the Benchmark Simulation Model No. 1 (BSM1) are evaluated by using Life Cycle Analysis (LCA). The aim is to analyze their environmental profiles in order to identify the main flows contributors to those impacts and to find the existing correlations between the control strategies and the selected impact categories. The knowledge of these correlations allows to assess where are located the main environmental impacts product of the plants operation under the control strategies evaluated and to take actions in order to reduce them.

 In this paper, a neural network PCA method that integrates neural networks (NN) and principal component analysis (PCA) is used to detect faults in a wastewater treatment plant. The neural networks are used to calculate a non-linear and dynamic model of the process in normal operating conditions. PCA is used to generate monitoring charts based on the residuals calculated as the difference between the process measurements and the output of the networks. It can evaluate the current performance of the process and detects the faults. This technique has been applied to the simulation of a benchmark of a biological wastewater treatment process, a highly non-linear process. The simulation results clearly show the advantages of using this NNPCA monitoring in comparison with classical PCA monitoring.

 This paper deals with five existing nonlinear estimators (filters), which include Extended Kalman Filter (EKF), Extended H-infinity Filter (EHF), State Dependent Riccati Equation Filter (SDREF), State Dependent H-Infinity Filter (SDHF) and Unscented Kalman Filter (UKF) that are formulated and implemented to estimate unmeasured states of a typical wastewater system. The performance of these five estimators of different complexities, behaviour and advantages are demonstrated and compared via nonlinear simulations. This study shows promising application of UKF for monitoring and control of the process variables, which are not directly measurable.

 This paper presents the main characteristics of the activated-sludge treatment plants focusing on dissolved oxygen control as a key condition for successful water treatment. Particularities of current strategies used for dissolved oxygen control are mentioned and compared with a somewhat improved control scheme. Some simulation results are presented in order to point out certain advantages of the new approach to dissolved oxygen control.

 In this paper a analysis of state controllability for the dynamical behavior of a wastewater treatment plant is presented. This plant is a large non-linear system subject to large disturbances. Based on a state controllability analysis a redesign for the plant is proposed. State controllability analysis is done by using set theory. This allows checking the controllability limits for the system. Indeed it allows including disturbance limits and constraints on control inputs. In this way, nominal design parameters determine the system controllability while other parameters would improve this system property. Finally, improvements in controllability issues for the system are presented.

 In this work the integrated design of the activated sludge process is addressed. The objective is to determine the best plant parameters and working point that minimize the operation costs related to the Effluent Quality and the Aeration Energy, while imposing constraints on the plant condition number (γ) and the perturbation condition number (γp), ensuring open-loop controllability. A linear multivariable predictive control (MPC) is used for the closed loop design, and it is used also a very practical non-linear version of the MPC, based on the instantaneous linearization of non linear models of the plant (the phenomenological model as well as a neural network model obtained by identification). The results are analyzed based on two aspects: the improved performance of the controlled system when using the integrated design instead of a classical economic design, and the convenience of the instantaneous linearization to realize a non-linear MPC.