A modified controller design based on symbiotic organisms search optimization for desalination system

Desalination is a method by which seawater and brackish water is filtered out in the form of fresh water by using chemical and physical processes so that the unusable water is made available for industrial, irrigation, and drinking purposes (Sobana & Panda 2011). In the last few years, significant developments in the area of desalination technologies have been seen all over the world. At present, many desalination techniques such as multistage-flash distillation (MSF), multiple-effect distillation (MED), evaporation, vapor-compression (VC), and membrane processes are used for water treatment. Among all the desalination methods, the reverse osmosis (RO) water treatment method is accepted worldwide due to its simplicity and high performance with minimal cost requirements (Sobana & Panda 2014).

The RO desalination process, in spite of being simple, is easy to implement and versatile in nature, and thus can be used for treatment of water; but still the technology suffers from the problems of membrane fouling and concentration polarization (Madaeni et al. 2015). However, these problems can be solved with advance technology in terms of process and control optimization. Traditionally, a combination of proportional-integral-derivative (PID) controller is incorporated to fulfill the control objectives in many applications. Alatiqi et al. (1989) first proposed traditional PID controllers in RO desalination plants. Furthermore, Robertson et al. (1996) used a modified Ziegler–Nichols (MZN) method for the tuning of PID controller parameters. Classically, the PID controllers are tuned with conventional techniques such as Ziegler–Nichols, MZN, trial and error method, Cohen and Coon (Bennett 2001), etc. Simple PID controllers with conventional tuning methods do not provide satisfactory results with plant and model uncertainties. A model predictive control (MPC) approach is presented in Abbas (2006) to overcome the limitations of PID controllers in RO desalination units. With the evolution of various optimization techniques, conventional methods of PID controller tuning are being replaced by optimization-based techniques (Åström & Hägglund 2006; Rathore et al. 2015a, 2015b; Gupta et al. 2016).

A literature survey shows that researchers have used many intelligent optimization techniques for the RO desalination problem. Zilouchian & Jafar (2001) and Kim et al. (2008) presented a genetic algorithm (GA)-based controller design for RO desalination plants. In Gambier (2011), a multi-objective optimization-based controller was designed for RO desalination systems. Although the algorithm is found to be superior to conventional methods, it still suffers from proper tuning of algorithm-specific parameters. Fuzzy logic and neural networks have been applied to investigate the RO desalination problem (Madaeni et al. 2015). Phuc et al. (2016) suggested a modified PID controller based on loop shaping method for desalination plants. However, the problem associated with these controllers is that they increase the complexity, mathematical complications, and computational efforts in the controllers. Therefore, in these techniques, the researchers/engineers need expert knowledge of the complex procedure and its applicability for practical use in RO desalination plants. This makes them less popular among researchers/engineers. Hence, the design procedure for the aforementioned controller needs further investigation (Jiang et al. 2016). Moreover, there are no limitations associated with classical PID controllers, which are simple, reliable, and affordable to use (Gambier et al. 2009). Therefore, optimization-based controller techniques have proven themselves in terms of better performance, simplicity, and less computation efforts required for the optimal tuning of controller parameters.

Rathore & Singh (2018) proposed a PID controller using teacher-learner-based optimization (TLBO) for RO desalination. The proposed method shows its superiority over other state-of-the-art techniques available in the literature. Recently, Sahib (2015) presented a new controller scheme, which is a combination of PID controller with second order derivative controller (DD). This combination is newly adopted and found to be efficient in a few research problems such as automatic voltage regulator (AVR) (Sahib 2015), automatic generation control (AGC) (Raju et al. 2016), etc. However, this control technique is not implemented in RO desalination systems.

In the current work, a new optimization algorithm, i.e., symbiotic organism search (SOS), proposed by Cheng & Prayogo (2014), is applied to design a PID-DD controller for RO desalination plants. The tuning of parameters for PID-DD controllers is achieved by minimizing the integral of square error (ISE) criterion. The RO model proposed by Riverol & Pilipovik (2005) is considered for experimental testing. In this procedure, a simplified decoupling method is first used to transform the multivariable RO model to a single-input single-output (SISO) model. Then, the proposed SOS-based controllers are examined for permeate flux and conductivity variables, respectively. Comparative studies of the proposed controllers with TLBO, DE, PSO, and ABC algorithm-based PID-DD controllers are presented in order to prove its supremacy over others.

The remainder of the paper is organized as follows. The section below introduces the RO seawater desalination process and its model structure. This is followed by a section that gives the modified controller design approach used in this work. Then the basics of the SOS algorithm applied to a RO desalination plant are discussed. Simulation results and comparison follow and the final section concludes the paper and discusses future avenues of research.

A generalized layout for a RO desalination system is shown in Figure 1. In this model, the RO system is characterized by four main segments, i.e., pre-treatment, high-pressure pump, membrane module, and post-treatment (Gambier et al. 2009). In the first segment, i.e., pre-treatment, saline water obtained from the sea or any other source is filtered through a chemical and physical process to remove major impurities present in the water. This process is very important in RO desalination plants in order to enhance the life cycle of the RO membrane. Also, it reduces scaling and fouling. In the next segment, a high-pressure pump is provided to raise the pressure of incoming water from the pre-treatment process. In the subsequent segment, the pressurized water is fed to a membrane assembly module. The membrane assembly module should be strong enough to withstand pressurized feed water. Therefore, the RO membrane assembly module is made up of a composite polyamide-type membrane, which is widely used in many RO desalination plants. After passing through membrane assembly, fresh water accumulates in a storage tank and brine water is discarded by the membrane accumulates at the discharge channel. The fresh water obtained from this process is post-treated in the last segment by adding some inhibitors and adjusting the pH value to the appropriate level (Rathore & Singh 2018).

Figure 1 contains all four segments, manipulated variables, and control variables. Manipulated variables are (pressure) and (pH value) at feed stream, respectively, and control variables are (flux) and (conductivity) at permeate stream, respectively.

The operating range for the linear model parameters of RO system is as listed in Table 1.

The proposed modified controller consists of a PID controller with a DD controller. The second order derivative or double derivative controller provides a better response than PID control structures (Sahib 2015). The control structure of PID-DD is illustrated in Figure 3.

SOS optimization is a newly developed metaheuristics algorithm based on the natural phenomenon of organisms (Cheng & Prayogo 2014). The SOS algorithm shows the symbiotic relationship within paired organisms present in the ecosystem. Similar to other organisms like honeybee swarms, particle swarm optimization, and genetic algorithms, the SOS optimization algorithm uses the bunch of population and it searches the global best solution for the given problem within the search space. In this algorithm, there are three phases based on the relationships of real-world organisms. The three phases are mutualism, commensalism, and parasitism phases.

Further details of these three phases are given as follows.

In parasitism, only one organism benefits, but the other organism is harmed by the first organism. In this way, plasmodium parasites pass into the human body with the help of Anopheles mosquitoes, where they get food and increase their population and, in return, humans suffer from malaria and may die.

In the SOS algorithm, a parasite vector is created by duplicating organisms in the search space. Further, organisms are modified randomly using a random number to obtain the parasite vector. Organisms are treated as host in the ecosystem for the parasite vector. This parasite vector tries to replace ⁠. If the fitness of the parasite vector is better than that of ⁠, then the parasite vector will replace ⁠.

The steps for the SOS algorithm are given as follows:

Step 1: Generate initial population in the ecosystem randomly.

Step 2: Determine the fitness value and find their value for each organism.

Step 3: Select the randomly such that (Mutualism phase)

Step 4: Calculate mutual vector and benefit factor (⁠⁠) either 1 or 2.

Step 5: Modify the solution according to Equations (14) and (15).

(Commensalism phase)

Step 6: Repeat step 3 and modify the solution according to Equation (16).

(Parasitism phase)

Step 7: Repeat step 3 and create parasite vector from ⁠.

Calculate the fitness value

end if

Step 7: Repeat step 2 and proceed for next iteration.

Step 8: Stop if termination criterion is met.

Two case studies are considered in this work to examine the performance of the modified PID controller with different optimizing techniques presented in the literature. The objective function designed in Equation (13) is tested for permeate flux and conductivity parameters, respectively.

In the first case, the performance of the PID-DD controller with SOS, TLBO, DE, PSO, and ABC optimization algorithms is tested. The PID-DD controller parameters obtained from different algorithms are presented in Table 2 and performance indices are given in Table 3. It is found that the minimum performance index is found with the SOS-based PID-DD (SOS-PID-DD) controller. Time response analysis for flux is plotted in Figure 4 and their values are tabulated in Table 3.

In the second case, to establish the capability of the proposed SOS-PID-DD controller, other controllers based on TLBO, DE, PSO, and ABC optimization algorithms are also designed for loop 2. The PID-DD controller parameter values are presented in Table 4 and performance indices are tabulated in Table 5. It is obvious from simulation results that performance index is minimum for the SOS-PID-DD controller. Time response analysis for conductivity with the PID-DD controller using different algorithms is shown in Figure 5 and their time response specifications are also tabulated in Table 5 for comparison.

Figures 4 and 5 and Tables 3 and 5 clearly establish that the RO system performance is appreciably enhanced with the SOS tuned PID-DD controller.

In this work, a new optimization approach, i.e., SOS is tested for PID with a second order derivative (DD) controller to examine the performance of permeate flux and conductivity parameters of an RO system. The effect of the SOS algorithm with the PID-DD controller is found to be superior to other methods of optimization in the literature. Hence, it can be concluded that the proposed method is quite satisfactory and can be successfully implemented for RO desalination plants. Future work includes the design and tuning of a modified PID-DD controller for other models proposed for RO desalination plants. Additionally, this work can be extended to design a robust modified PID-DD controller for RO desalination plants.