## 1 Introduction
The impact of this unavoidable process has undesirable consequences on the performance of the RO desalination plants: a decrease in permeate flowrate production for constant operating pressures, or increase in the feed flowrate, which involves expensive pre-treatments, higher operating pressures, and prevalent chemical cleanings that can damage the membranes, decrease the permeate quality, and reduce the replacement time of membranes [37]. Moreover, the dynamic process parameters obtained after cleaning are different from the dynamic parameters existing before cleaning [35]. Consequently, the costs of water and energy consumption grow [17]. As seawater RO desalination plants turn more complex, the control theory allows the estimation of uncertainties, the determination of risks and safety measures, and the design of robust and reliable control systems [38; 39; 40]. For the critical variables' control of the seawater RO desalination plants, diverse controllers have been developed. These controllers are mainly: conventional [41; 42; 43], feedforward [37], optimal [28; 44], adaptive [35], model predictive [45; 46; 47; 48], fractional order [49], or robust [32; 50; 51; 52]. In these plants, the most implemented controller is the proportional integral derivative (PID), owing to the fact that many process engineers are much more familiar with this class of controller than with sophisticated advanced controllers, due their functional simplicity, which allows them to operate in a straightforward mode [53; 54]. Moreover, it is well-known that in diverse industrial processes, the PID controllers show good performance, robustness, and simple implementation [55; 56]. However, when the seawater RO desalination plants are characterized by time-varying dynamic behaviors, the PID controllers are not sufficient and fail to provide satisfactory performance [32; 35; 45]. To overcome the shortcomings of PID controllers, a number of advanced controllers have been developed over the last decades, for example the model predictive controller (MPC) [57]. The MPC is an industry-accepted controller for advanced control of processes with complex dynamical behaviors,which, in comparison to the PID controller, is considered a better tool based on many studies reported in the literature, see e.g., Ref. [58]. However, the MPC has not been intensively used in the automation of RO desalination plants, and only few contributions regarding this class of controllers can be found in the literature [46]. The MPC has some practical problems; for example, it requires a precise model of the plant to be controlled, which in many cases is difficult to obtain [57; 58]. Usually, the MPC performs well initially, but as the plant dynamics change with process operating conditions, its performance usually deteriorates, see e.g., Refs. [35; 49; 45]. Linear time-invariant multivariable models are used in the MPC design of the RO desalination plants, although it is well-known that these plants are time-varying multivariable systems; therefore, the required control performance is very difficult to achieve over a reasonable range of operating conditions, see e.g., Refs. [33; 45; 46; 48]. Artificial intelligence is one of the newest fields in science and engineering, which has provided successful solutions to many problems that are extremely difficult to be solved by traditional mathematical approaches, and it constitutes a powerful tool for control of complex dynamical processes [59; 60; 61]. In particular, the research field of knowledge-based systems has received a lot of attention; see e.g., Refs. [61; 62]. The knowledge-based systems are defined as systems in which some symbolic representation of human knowledge is applied, usually in a way very similar to human reasoning. In this class of systems, the expert systems have been the most successful and those that have grown most rapidly nowadays [63; 64].