# Self-Learning Salp Swarm Optimization Based PID Design of Doha RO Plant
## 1 Introduction  
The existence of civilization depends upon the availability of potable water. The studies are repeatedly indicating that the potable water is acutely depleted in the last few decades. The main reason for depletion of fresh water is overuse of ground water due to growing population and industrialization [1]. In order to meet the requirements due to increased population and industrialization, it is required to explore for sources of fresh water. The desalination of sea water is one of the possible solutions since the expense of sea water is abundant [2]. The process of removing salts from saline water is called desalination [3]. Desalination is achieved by (a) thermal distillation processes such as multiple effect evaporation, multi-stage flash (MSF), vapour compression, etc., and (b) membrane processes such as nano-filtration, electro-dialysis reversal, forward osmosis, reverse osmosis (RO), etc. Among these, RO has been proved to be economical [4]. RO process also produces high quality water with minimal energy consumption. Additionally, there is low initial investment in case of RO process. RO is a filtration process which segregates fresh water from higher concentrated saline water. In RO process, a semi-permeable membrane is used which passes the fresh water through it and rejects the concentrated solution. The various plants which use the RO process are Doha RO system [5],Chaabene RO system [6], Riverol and Pilipovik [7], etc. The various pilot-scale models [8] for RO are also presented in literature. The RO models given in literature are interacting having flux and conductivity as controlled variables generally. The most common controller used for interacting RO models is a proportional-integral-derivative (PID) controller. The PID controller is used due to its simple structure and being able to produce satisfactory performance. The performance of PID controller depends on the quality of tuning. The classical methods [9] used for tuning are Ziegler-Nichols, gain-phase margin method, Cohen-Coon, etc. However, the main drawback with classical tuning methods is moderate performance of the controller [10]. The controller performance can be enhanced by improving the quality of tuning. Various optimization based methods can guarantee the improved quality of tuning [11]. Recently, the intelligent optimization algorithms like fireworks algorithm [12], bacterial foraging optimization [13], elephant herding optimization [10], whale optimization algorithm [2; 14], monarch butterfly optimization [15], brain storm optimization [16], etc. are proposed in the existing literature. Some techniques are proposed in the literature for tuning the PID controller for RO systems. In [11], PID controller is designed using Luus-Jaakola (LJ) optimization algorithm for the RO system. Particle swarm optimization (PSO) is utilized for tuning the PID controller parameters in [17]. Grey wolf optimization (GWO) assisted PID tuning for RO system is presented in [4]. Similarly, whale optimization algorithm and elephant herding optimization based controllers are suggested in [2; 10], respectively. Other relevant controller designs can be found in [18; 19]. The methods presented in [2; 4; 10; 19] also provide better response when compared to classical methods of tuning of PID controller. However, the methods proposed in [2; 4; 10; 19] are providing the tuning with basic algorithms only. It is well known that the basic algorithms have the problem of local minima trapping [20]. So sometimes, it becomes necessary to enhance the performance of basic algorithm by some modifications [21; 22]. In this investigation, an improved algorithm, i.e., self learning salp swarm optimization (SLSSO), is utilized for tuning of PID controllers [23]. The controller tuning is presented for Doha reverse osmosis plant (DROP). DROP model is having two controlled variables namely, flux and conductivity and two manipulated variables namely, pressure and pH. These four variables are interacting in nature.