The control of wastewater treatment plants (WWTPs), with the ultimate goal of reducing as much as possible the contamination of aquatic ecosystems, constitutes an important multidisciplinary environmental objective. One of the best-known wastewater treatment procedures consists of the use of the so-called Activated Sludge Processes (ASP), which are biological processes that reduce organic contamination thanks to the vital activity of certain bacteria. The control of this type of processes is not easy, precisely due to its biological nature. Consequently, the control of wastewater treatment plants based on ASP processes constitutes an important challenge in the field of Automatic Control, with numerous strategies proposed to date. With the aim of testing and evaluating the different existing strategies, in an objective and orderly manner, the so-called Benchmark Simulation Models (BSM) emerged, which are standard models of wastewater treatment plants based on ASP processes. The main objective of this article is precisely to test the feasibility and evaluate a specific Fuzzy Model Based Predictive Control (FMBPC) strategy, applied to the wastewater treatment plants represented in the BSM1 benchmark (a particular case of the BSM benchmarks). The FMBPC strategy is potentially appropriate for the control of complex, changing or unknown systems and this article demonstrates that this strategy, used in the BSM1 benchmark control configuration (as an alternative to the default control configuration), is viable and performs satisfactorily, to the point that it can even be considered a competitive strategy compared to more traditional control strategies. In the experiments carried out, in a simulation environment, a specific FMBPC control modality has been used, called FMBPC/CLP, which incorporates mechanisms for imposing restrictions on the control action. The base model of the plant to be controlled, necessary for the implementation of the FMBPC strategy, is obtained by prior fuzzy identification of the plant, which is, in our case, the WWTP plant integrated into the BSM1 benchmark itself. The identification procedure developed is based on the information contained in input output data series of the plant in open loop, previously obtained by simulation. Identification is achieved with the help of a software tool that uses mathematical clustering methods, based on the Gustafson Kessel algorithm, through which it is possible to extract Takagi Sugeno type fuzzy models, from numerical input output data of a given plant.