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In our recent post, Top Three Opportunities for Machine Learning to Improve Reverse Osmosis Plants, we introduced the case for automated plant-wide risk mitigation in reverse osmosis facilities.

System upsets can result in costly unplanned downtime for an organization. Poor maintenance strategies can reduce an industrial facility’s overall productive capacity by 5-20%. Disruption to treatment systems connected to industrial applications may also halt production at the primary facility (e.g., bottling plant) resulting in additional losses. Downtime in a food and beverage facility can reach 500 hours annually, leading to overall costs ranging from $20,000 to $30,000 per hour.

Let’s breakdown the limitations of current risk mitigation strategies and discuss how predictive O&M tools can offset downtime in reverse osmosis facilities.


Operations and Maintenance (O&M) is defined as the decisions and actions to control and upkeep property and equipment. In the context of industrial RO applications, operations and maintenance describe the following:

  • Operations - the actions performed to meet the necessary product water quality and quantity

  • Maintenance - the activities to ensure equipment, membranes, and instrumentation work efficiently to achieve operational objectives (e.g. reagent water for food and beverage creation)

Figure 1 - Schematic diagram of an industrial Reverse Osmosis system from Pure Aqua Inc.

Treatment systems are becoming increasingly complex and instrumented, and operations professionals are tasked with a wide range of duties:

  • Monitor system operations and respond to varying conditions and production objectives

  • Perform inspection, diagnostics, and maintenance activities on mechanical assets

  • Monitor instrumentation and piping for faults and leaks

  • Determine chemical dosing in response to varying conditions

  • Tracking and procurement of chemical stock

  • Perform, document, and interpret test results by computer, meter, gauge, and control panels

  • Generate compliance reports

  • Monitor and manage membrane degradation (cleanings, replacements, CIP activities)

Traditional preventative strategies such as plant operating philosophies (reactive and scheduled maintenance), shared tribal knowledge, and digital/automation tools (e.g., SCADA systems) have mitigated operational headaches, but user error is still the leading cause of facility downtime at 24%. Operators face daily challenges in managing complexity in operations and maintenance activities to prevent costly unplanned downtime.


O&M teams face daily challenges in reliably meeting operational objectives at industrial-based RO facilities. Pani’s AI Coach™, a web-based platform designed for reverse osmosis applications seamlessly integrates with existing systems (SCADA and PLC systems) to centralize data and provide plant-wide monitoring and timely operational guidance. Insights, a feature of the Pani platform, acts as a risk mitigation system as it runs plant-wide simulations using incoming data to predict operational risks and prevent system upsets at an RO facility:

Figure 2 - Diagram of O&M strategies for RO applications

The holistic approach runs simulations based on incoming plant data to predict and detect potential system upsets and inefficiencies. Plant personnel are then notified of required action to prevent potential asset, instrumentation, or system faults. Pani’s Insights mitigate guesswork, extend asset life, and can reduce plant downtime up to 20%.


Here are a few examples of Insights from the Pani Digital platform and the AI Coach™ for O&M teams working with RO systems.

1.0 Sensor Integrity – operators and engineering departments depend on sensor data for sound decision-making and accurate reporting.

  • Reactive - Sensors are recalibrated as drift or faults are detected

  • Preventative - Sensors are recalibrated on a scheduled basis

  • Predictive (Pani 🌊) - Simulations detect discrepancies for fault prediction. Operators are informed of potential sensor error in advance

2.0 Asset wear and tear – critical assets such as pumps and energy recovery devices ensure operational goals are met efficiently. O&M teams must ensure equipment is operating optimally and safely.

  • Reactive - Equipment is inspected and maintained when failure occurs

  • Preventative - Equipment is maintained at pre-determined intervals

  • Predictive (Pani 🌊) - Simulations predict potential issues and diagnose root-cause for O&M teams

3.0 Predictive performance degradation – membranes scale and foul over time resulting in performance loss. Some of this performance loss is reversible (can be restored through cleanings) and some are irreversible (require replacement to restore performance). O&M teams must manage membrane health to minimize the energy required to produce water and ensure quality.

  • Reactive - Membranes are cleaned when product water quality is not achieved

  • Preventative - Membranes are cleaned when manual analysis of membrane performance drops below a threshold (e.g., Normalized Pressure Drop increases by 15%)

  • Predictive (Pani 🌊) - Simulations predict in advance when performance criteria will fall below accepted threshold. Operators are notified of required action to offset performance






Post category

Plant-wide Risk Mitigation for Reverse Osmosis Facilities: Digital Twins for the Win

In our recent post, Top Three Opportunities for Machine Learning to Improve Reverse Osmosis Plans, we introduce the case for automated plant-wide risk mitigation in reverse osmosis facilities.

Feb 22, 2021


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