Page 1 of 17

European Journal of Applied Sciences – Vol. 11, No. 5

Publication Date: October 25, 2023

DOI:10.14738/aivp.115.15635

Karimanzira, D., & Rauschenbach, T. (2023). Running a Reverse Osmosis Plant at Maximum Renewable Energy Use. European

Journal of Applied Sciences, Vol - 11(5). 279-295.

Services for Science and Education – United Kingdom

Running a Reverse Osmosis Plant at Maximum Renewable Energy

Use

Divas Karimanzira

Fraunhofer Institute of Optronics, System Technologies and

Image Exploitation (IOSB), Am Vogelherd 90, 98693 Ilmenau, Germany

Thomas Rauschenbach

Fraunhofer Institute of Optronics, System Technologies and

Image Exploitation (IOSB), Am Vogelherd 90, 98693 Ilmenau, Germany

ABSTRACT

Decentralized water and energy systems are increasingly seen as the most effective

solutions for remote areas. This paper presents the development of a solar- powered reverse osmosis (RO) plant to provide water to a remote village. The

objective is to optimize the use of renewable energy and maximize water

production by implementing a control mechanism that can quickly adapt to

changing operating conditions. To achieve this, the RO plant operates in batch mode

and is controlled by a nonlinear model-based predictive controller. It also serves as

an active load to effectively utilize solar energy. Fortunately, in arid and water- scarce regions, the water demand aligns well with solar power availability.

Although a diesel generator is used as a backup, energy is stored as pressure in a

pressure chamber and in batteries for direct electrical energy consumption by the

plant. The energy-intensive tasks, such as driving the high-pressure pump and

membrane cleaning, are scheduled during periods of abundant solar energy.

Additionally, a module for predicting membrane fouling is developed to enable the

controller to appropriately schedule membrane cleaning and pump operation,

while considering the various limitations imposed by the RO plant components. The

system has successfully met the water demand while maximizing solar power

utilization.

Keywords: Reverse Osmosis, Model-based Optimization, Optimal Cleaning Strategies,

Renewable Energy, Water Demand Modelling.

INTRODUCTION

One of the most used forms of desalination worldwide is Reverse osmosis (RO) (Figure 1). In

semi and arid countries, especially in the Middle East, more than half of the fresh drinking water

supplies come from desalination facilities [1]. As already discovered by [2], medium sized

Reverse Osmosis (RO) Plants are quite attractive in terms of realizability, scalability, simple

control and energy use efficiency compared to other desalination plants such as Multi-stage

Flash (MSF)s. This is due to the increased implementation of energy recovery devices, high

efficiency pumps, and improved membranes [1]. But still, to maintain the high level of pressure

required for the process – up to 70 times atmospheric pressure – a RO desalination plant must

employ large numbers of pumps and other equipment. And that uses a lot of energy. It has been

Page 2 of 17

Services for Science and Education – United Kingdom 280

European Journal of Applied Sciences (EJAS) Vol. 11, Issue 5, October-2023

shown in [3] that RO can easily be used as active load for intermittent energy sources such as

solar and wind energy.

Figure 1: The contribution of each desalination process to the world water production

(Statistics taken from source [4]).

In designing a RO plant like in all investment’s projects, it is desirable to reduce the capital

expenditure and operational costs. Most of the cost in RO desalination incur form membranes,

energy consumption, water storage tanks for temporary storage of cleaned water before use

[5]. Temporary storage of water is associated with increased chemical use for disinfection. The

are several ways of tackling these problems. Novel membranes with improved permeability,

selectivity characteristics and higher tolerance to fouling have been developed [6],[7]. There

have been several advancements in renewable energy production technologies which are

currently driving down prices enough to be cost competitive with fossil fuels, which ensure

healthy economics while maintaining a minimal carbon footprint [8]. Tracking systems for

solar photovoltaic (PV) panels is one example of improvements which can bring down the cost

of energy for integrated infrastructure projects [8]. Other breakthroughs such as the

development of a variant of the process of batch reverse osmosis promises improvement in

energy use efficiency, equipment lifetime, low downtime and the ability to process water of

much higher salinity [7]. Another improvement is in the way on how to operate the systems

efficiently. In [9] it was shown how to operate a reverse osmosis plant for energy serving and

optimal injection of wind power. Furthermore, cleaning schedules were optimized to save costs

and energy in [9].

In term of control strategies for reverse osmosis process, several approaches for controlling

nonlinear systems in general such as sliding mode control, linear quadratic regulator (LQR),

and backstepping control are listed in [3]. Nevertheless, all these techniques usually do not take

into account the actual constraints of the process and just consider the control effects.

Furthermore, the parameters of the controllers are chosen aimlessly, hence the optimality of

Page 3 of 17

281

Karimanzira, D., & Rauschenbach, T. (2023). Running a Reverse Osmosis Plant at Maximum Renewable Energy Use. European Journal of Applied

Sciences, Vol - 11(5). 279-295.

URL: http://dx.doi.org/10.14738/aivp.115.15635

the system cannot be guaranteed. Hence, there have been implementations of the Model

predictive control for control RO desalination plants [10], [11]. As discussed in Sassi, 2010, the

performance of a nonlinear model predictive controller largely depends on the quality of the

predictive model used, especially if the system is complex and highly nonlinear. Several

techniques have been used for system identification for the MPC, e.g., Kalman filtering [12],

maximum likelihood estimation [13]. However, it is known that the Kalman filter requires

knowledge of the mathematics behind the system, which we know is very difficult to obtain for

highly complex processes such as the RO desalination system with several unknown

disturbances, and the physical phenomena such as membrane fouling.

This paper focuses on the operational part of the RO and Solar energy system to increase plant

life, reduce costs, satisfy water demand, optimize cleaning strategies and maximize the use of

solar energy to reduce carbon footprint by using a model predictive control. Several models are

utilized in the system including water demand prediction, energy demand prediction and

membrane fouling prediction. Unlike other development batteries are used only for electrical

energy, which is required by the equipment’s, for a reverse Osmosis system it is intuitive to

store the excess energy as pressure in a pressure chamber and run the RO Plant in batch mode.

In continuous mode, the feed pressure depends on (1) the desired conversion yield and (2) the

salt concentration to guarantee a minimum permeate flow at the end of the spiral wound.

Whereas batch RO is, in theory, the only configuration where the required minimum energy is

equal to the thermodynamic theoretical minimal specific energy consumption (SEC), by

matching/adapting the applied pressure to the increasing osmotic pressure [14]. Another

advantage of running the RO plant in batch mode is that the pressure can be modulated and

adapted precisely according to the osmotic pressure evolution. The approach is a further

improvement of the work done in [3], [9]. The feasibility of the method relies on the fact that in

semi and arid regions higher water consumption occurs when more renewable energy is

available, the renewable energy consumption can be optimized and the auxiliary energy

consumption minimized, using a variable production policy, as already found in [15].

The highlights and main contributions of this work are:

• Operational strategy for medium-scale RO desalination plant operated by renewable

energy.

• Energy storage by pressure buffering and batteries

• Variable operation is a promising operation strategy to directly operate RO plants with

wind and solar power.

The rest of the paper is outlined as follows. The system will be described in section 2. The

methods developed for optimization of the RO plant operation will be described and given in

Section 3. The results of the work will be given and discussed in section 4. Finally, some

conclusions will be drawn in the last section.

THE STUDY AREA AND DATA AVAILABILITY

The study area is situated in a semi-arid region with solar production and water demand

profiles as shown in Figure 1 a and b, respectively. As can be seen the maximum solar

production is at midday and the daily averages changes according to the month of the year. The