## 1 Introduction  
The freshwater supply is one of the most important requirements in marine vessels, since it is a vital necessity for the operation of the ship itself, but also for use by the crew [1]. Freshwater can be obtained from water supplies in ports and from the desalination of seawater. This last option makes it necessary to identify certain key parameters to operate a desalination plant efficiently. In this sense, the quality and amount of water are imperative requirements, but the energy consumed is also important and essential. In this study, the Guidelines for Drinking-water Quality (GDWQ) [2] of the World Health Organization (WHO) have been consulted to determine the water quality requirements for marine vessels. Specifically, in this work we have chosen to control only the water conductivity from the desalination process, since it is the parameter interrelated with total dissolved solids (TDS). The WHO proposes as a reference value for the palatability of water a TDS level of less than approximately 600 mg/L (938 \(\upmu\)S/cm). It is important to note that there are no international or official standard values, but rather reference values [3]. The parameters used in this work allow this desalination plant to be used on land and on board, since it meets the criteria for on-board use. At this point, some considerations must be taken into account, since ships are isolated structures, so energy input and storage capacity are key. According to various researchers, the total water used in cruises varies widely, being highly dependent on the number of passengers, the size of the ship, and so on. For example, [PERSON] et al. [4] point out that a cruise ship in a Spanish base port refills an average volume of 628 m\({}^{3}\) per mooring, while [PERSON] [1] indicates that the average drinking water consumed on cruise ships exceeds 984 m\({}^{3}\) per day. Therefore, having a control system to improve the efficiency of desalination plants would save energy and water. In this work, a small desalination plant inside a 20-foot shipping container is used, meaning it could easily be installed in a ship. The water-energy relationship has been studied in recent decades [5; 6], but in the case of ships there are important restrictions in terms of energy consumption, which is why scientists are introducing renewable energy into maritime transport. This remains a work in progress for this sector [7] since these energy resources have been widely applied in other fields [8]. Industry 4.0 and Artificial Intelligence (AI) have been used together for a long time. In particular, Machine Learning (ML), Genetic Neuro-Fuzzy (GNF) and Deep Learning (DL) techniques have improved different processes in seawater treatment plants, yielding higher quality control, increasing their productivity, reducing human error, improving the overall efficiency of the system, and optimizing operating and production costs [9; 10; 11; 12; 13; 14; 15; 16]. In fact, the increased interest in this topic has been pointed out by [PERSON] [5], who indicates that the number of publications related to desalination involving artificial intelligence has increased remarkably in recent years. In some studies, several control methods and different operational strategies for the variable operation for desalination plants have been proposed [17]. Other researchers have also applied control strategies to connect different renewable energy sources such as wind or PV energy to desalination plants [18; 19]. The benefits of AI are not only in desalination, but in other water treatment technologies which have been widely studied [20]. In our work, we have opted for a control strategy based on the change in the operating pressure of the high-pressure pump. Furthermore, in this study, there are different requirements for the daily water obtained, the permeate conductivity and the total energy consumption. Therefore, the main objective is to control the desalination process under stationary management in accordance with these requirements.