Research and System Optimization Design of Reverse Osmosis Engineering Technology

1. Challenges Faced

Compared to the sweeping wave of RO engineering projects, domestic research on RO engineering technology is relatively weak and lagging, with many pressing issues. One crucial issue is the need for domestically developed RO system design software, including pretreatment systems, to transform scattered research findings on RO applications into products that directly serve system design. Another important concern for industry professionals is the design and operational optimization of RO systems. In-depth analysis and discussion of this fundamental issue, applied to engineering design and system operation practice, can improve the design and operation levels of reverse osmosis systems, thereby generating significant economic benefits.

Currently, RO system design largely relies on design software provided by foreign membrane manufacturers. While these software programs greatly assist in system design, they are essentially merely simulation environments for membrane system design schemes. They cannot automatically provide feasible design solutions, lack optimization capabilities, or address the crucial pretreatment system. For designers of small and medium-sized RO systems, systematically mastering the concepts and techniques of optimization design is extremely difficult. A more practical approach is to develop a comprehensive family of RO system design optimization schemes, tailored to different raw water quality conditions, permeate flow rates, and permeate quality requirements. Designers of small and medium-sized systems only need to find the corresponding optimization scheme within this family based on their design requirements, and then, based on specific circumstances, make limited modifications and verifications using existing design software to quickly obtain a practical optimized design scheme.

2. System Optimization Design

While developing this family of optimization design schemes is a significant undertaking, initial attempts by the author and collaborators have proven its feasibility. Here, only the basic framework of this concept is presented for reference by those interested in this direction.

A complete RO pure water preparation system can be divided into two main parts: a pretreatment system and a membrane system. Each part can be further divided into three main components: design, operation, and cleaning. Therefore, comprehensive system optimization includes:

Pretreatment process structure optimization.

Pretreatment operating parameter optimization.

Pretreatment cleaning process optimization.

Membrane system process structure optimization.

Membrane system operating parameter optimization.

Membrane system cleaning process optimization.

The relationship between the various optimization modules is shown in Figure 1. Each item here represents an optimization problem targeting a specific aspect of a local system, with the goal of minimizing a specific cost.

The optimization of membrane system operating parameters and pretreatment operating parameters aims to simulate the operating conditions of various optional pretreatment and membrane system process structures to obtain the minimum operating cost corresponding to each condition. The results should be a family of minimum operating cost curves corresponding to different process structures. The optimization of pretreatment cleaning processes and membrane system cleaning processes aims to simulate cleaning processes for membranes and filter media with different levels of fouling or scaling in the pretreatment and membrane systems to obtain the minimum cost of replacing and cleaning the membranes and filter media for each process. The results should be a family of minimum cost curves for replacement and cleaning corresponding to different levels of fouling or scaling.

The pretreatment process structure optimization model can be qualitatively expressed as:

Optimization Objective: Minimize the total cost of pretreatment system investment and operating costs, including material replacement costs, cleaning costs, and water costs.

Optimization Constraints: Water quality indicators such as membrane system feedwater flow rate, membrane system feedwater fouling index, organic matter content, and microbial content.

Optimization Solution: Pretreatment system process structure.

Water Quality Conditions: Pollution index, inorganic salts, organic matter, microorganisms, and other water quality indicators of the raw water for the reverse osmosis system.

Set Values: Pretreatment system operating cost, material replacement and cleaning cost, raw water price.

The pretreatment system operating cost and material replacement and cleaning cost in the model are given by two models: pretreatment operating parameter optimization and pretreatment cleaning process optimization, respectively. The optimization result is the minimum cost curve of the pretreatment system with the membrane system feedwater flow rate as the independent variable, plus a correction coefficient curve with the membrane system feedwater quality level as the independent variable.

The membrane system process structure optimization model can be qualitatively expressed as:

Optimization Objective: Minimize the total cost of the membrane system investment, operating cost, membrane replacement cost, cleaning cost, and feedwater cost.

Optimization Constraints: Membrane system permeate flow rate and permeate water quality indicators required by the system design.

Optimization Solution: Membrane system process structure. Membrane system recovery rate.

Water Quality Conditions: Pollution index, organic matter, microbial content, and other water quality indicators of the membrane system feedwater.

Specified values: membrane system operating cost, membrane replacement and cleaning cost, and system feedwater price.

The membrane system operating cost and membrane replacement and cleaning cost in the model are given by two models: one for membrane system operating parameter optimization and the other for membrane system cleaning process optimization. Indicators such as the pollution index, organic matter, and microbial content in the feedwater are given by the pretreatment process structure optimization model. If deionization equipment such as water softeners and manganese sand filters are configured in the pretreatment process, the distribution of inorganic salts in the membrane system feedwater will also be affected by the pretreatment process configuration. The optimization result is the minimum cost curve of the membrane system with the RO system permeate flow rate as the independent variable, plus a correction coefficient curve with the membrane system permeate water quality level as the independent variable.

If the membrane system feedwater pollution index (SDI) represents the non-inorganic saline water quality index of the membrane system feedwater, then for a specific RO system permeate flow rate requirement and a specific RO system raw water inorganic saline water quality condition, the total cost of the membrane system is a monotonically increasing function of the SDI index, while the total cost of the pretreatment system is a monotonically decreasing function of the SDI index. Synthesizing these two monotonically increasing function curves forms the total cost curve of the RO system for a specific project. The lowest point of the synthesis curve corresponds to the lowest total cost of investment, operation, consumables, and cleaning for the system. This point also corresponds to the optimal process structure, optimal operating parameters, optimal consumable replacement period, and optimal cleaning process for the pretreatment and membrane systems. This is the essence of the overall optimization section of the reverse osmosis system in Figure 1.

3. Conclusion

The preceding sections of this article reflect the author's observations and experiences over several years in the reverse osmosis pure water equipment manufacturing industry, aiming to facilitate exchange with colleagues in the field. The viewpoints presented regarding reverse osmosis technology research are both professional insights and the results of teaching and research in recent years. However, the mathematical models presented are still qualitative descriptions, and specific research work is gradually unfolding. This article aims to discuss this direction with industry professionals.