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This paper presents an approach for solving minimal time problems for biological batch models with switching controls. Multi-reaction processes operated in a sequence of operating modes are considered, where the control variable is the switch between different operating modes. In order to determine the optimal switching instants that minimize the final time to reach the target, a reachability analysis and a study of dynamical properties are first investigated. This leads to a reduced model and the characterization of reachable switching sequences. Optimization tools are then used to determine the optimal switching instants. Copyright © 2009 John Wiley & Sons, Ltd.

This paper discusses several theoretical as well as practical challenging issues related to a recently developed time-optimal control strategy for the optimization of oxic/anoxic Sequencing Batch Reactors (SBR) for the removal of organic carbon and nitrogen from urban and industrial wastewaters and to its practical implementation.

This paper presents (i) a synthesis of the EOLI supervision system for optimizing both the biological reaction phases and the settling phase of Sequencing Batch Reactors (SBR) performing carbon and nitrogen removal from urban or industrial wastewaters; (ii) a summary of the integration capabilities of the integrated system developed by an industrial partner of the project to propose both the hardware and the software of the EOLI solution under a unified framework.

This paper presents two optimal control strategies developed for the control of two biological sequencing batch reactors (SBR), the first one performing both organic carbon and nitrogen removal from dairy wastewaters, while the second one treats toxic wastewaters. The proposed control laws are based on models that have been validated with real data acquired on two real biological SBR pilot plants. These models are presented in D. Mazouni et al., 16th IFAC World Congress, Prague, 5–8 July 2005. The validation results of these control strategies are presented and discussed.

This paper presents a new software developed in MATLAB for analyzing on-line data of an aerobic SBR, detecting faults and, in this case, proposing the most probable causes of fault. Process diagnosis is achieved using a statistical method divided in two main phases: off-line model building and on-line data diagnosis. The off-line model identifies the correct working conditions of the system (standard operative conditions). It includes the characterization of the deviation of the system from these standard conditions in the case of changing in the biomass properties or carbon and nitrogen load characteristics. The on-line diagnosis aims at collecting and analyzing all the available data available through industrial sensors, and at classifying the behavior of each treatment cycle. The diagnosis performance of the proposed method is tested using a data set of an aerobic SBR pilot plant.