Digital Transformation of Packaged Reverse Osmosis Plants for Industrial and Sewer Mining Applications

Abstract

Purpose of Review

Packaged reverse osmosis (RO) systems are often synonymous with industrial water supply and high quality water reuse. These RO systems can satisfy specific industries with stringent water quality specifications. They are also compact for deployment in basement of commercial buildings for sewer mining. Increasing applications of packaged RO systems opens the door for digital transformation of their design, operation, and maintenance for a quantum leap in system performance (energy consumption, treatment efficiency, and cost). This review summarises opportunities and challenges associated with the digitalisation of packaged RO systems and guide the industry to take advantage of these opportunities.

Recent Findings

Digital connectivity and machine learning offer a game changing capability to packaged RO systems. With digital capability, it is more cost-effective to design, operate, and manage these RO systems. Performance can be optimised via a range of approaches that are not possible with traditional human intervention. For example, hybrid systems that require sophistication control and prediction can benefit from big data analytics. On the other hand, other system that needs less intervention can work autonomously with little human intervention.

Summary

Automatic high-quality water treatment systems have attracted significant attention in recent years. This review identified a gap in understanding variable possibilities that machine learning and prediction can be successfully utilized by RO systems. This review confirms that artificial intelligence and machine learning can improve the way these systems work. Future research should strive to achieve a better way to apply these applications in packaged RO systems.