Fuzzy Control Strategy for an Anaerobic Wastewater Treatment Process

In this paper, a fuzzy control strategy (FCS) for an anaerobic wastewater treatment process is proposed in order to reject large disturbances on input substrate allowing a high methane production. This strategy is composed of: i) a state observer, which is based on a principal components analysis (PCA) and Takagi-Sugeno (TS) algorithm; it is designed to estimate variables hard to measure: biomass and substrate, ii) proportional-integral (PI) controllers based on a combination of the L/A(logarithm/antilogarithm) and fuzzy approaches; these controllers have variable gains and are designed to regulate bicarbonate in the reactor by two control actions: a base supplying (binc) and dilution rate (D) changes, iii) a TS supervisor which detects the process state, selects and applies the most adequate control action, allowing a smooth switching between open loop and closed loop. Applicability of the proposed structure in a completely stirred tank reactor (CSTR) is illustrated via simulations. The obtained results show that the process works in open loop in presence of small disturbances. For large disturbances, the supervisor allows the control actions to be applied avoiding washout; after that, the process returns to open loop operation. In general, the FCS improves the performances of the anaerobic process and is feasible for application in real processes, since the control scheme shows a good compromise between efficiency and complexity

Neural Observer Based Hybrid Intelligent Scheme for Activated Sludge Wastewater Treatment

Activated sludge wastewater treatment plants have received considerable attention due to their efficiency to eliminate biodegradable pollution and their robustness to reject disturbances. Different control strategies have been proposed, but most of these techniques need sensors to measure process main variables. This paper presents a discrete-time recurrent high order neural observer (RHONO) to estimate substrate and biomass concentrations in an activated sludge wastewater treatment plant. The RHONO is trained on-line with an extended Kalman filter (EKF)-based algorithm. Then this observer is associated with a hybrid intelligent system based on fuzzy logic to control the substrate/biomass concentration ratio using the external recycle flow rate and the injected oxygen as control actions. The intelligent system and neural observer performance is illustrated via simulations

Neural Observer Based Hybrid Intelligent Scheme for Activated Sludge Wastewater Treatment

Activated sludge wastewater treatment plants have received considerable attention due to their efficiency to eliminate biodegradable pollution and their robustness to reject disturbances. Different control strategies have been proposed, but most of these techniques need sensors to measure process main variables. This paper presents a discrete-time recurrent high order neural observer (RHONO) to estimate substrate and biomass concentrations in an activated sludge wastewater treatment plant. The RHONO is trained on-line with an extended Kalman filter (EKF)-based algorithm. Then this observer is associated with a hybrid intelligent system based on fuzzy logic to control the substrate/biomass concentration ratio using the external recycle flow rate and the injected oxygen as control actions. The intelligent system and neural observer performance is illustrated via simulations

Benchmark for evaluating control strategies in wastewater treatment plants

The paper describes the development of a benchmark for the evaluation of control strategies in wastewater treatment plants. The benchmark is a platform-independent simulation environment defining a plant layout, a simulation model, influent loads, test procedures and evaluation criteria. Several different research teams have contributed to the development of the benchmark and have obtained results using several simulation platforms (GPS-X™, Simulink™, Simba™, West™, FORTRAN code).</p