Introduction
Reverse Osmosis systems are vital for producing high-purity water, but small-scale plants often struggle with high operational costs, membrane scaling, inefficient water recovery, and the expense of complex Programmable Logic Controllers. This case study demonstrates how an industrial water-bottling plant implemented Terra M to gain a self-sufficient and economical solution, leading to improved efficiency and significant cost savings.
The Challenge
The client was operating a small-scale plant that faced several critical issues:
- High Operating Expenses (OPEX): Inefficient system performance led to excessive water and energy wastage. They were also expensive for automation.
- Lack of Predictive Maintenance: Without the AI-enabled remote monitoring, the plant experienced unplanned downtime, which increased maintenance costs and operational risk.
- Inconsistent Dosing: Inaccurate control over antiscalant dosing, which resulted in frequent membrane fouling, requiring costly chemical cleaning and premature membrane replacement.
The Solution
Terraconnect introduced the Terra M IoT Controller as a cost-effective alternative to the client. The smart controller provided a perfect formula of technology and sustainability, transforming the plant’s operations.
Predictive Maintenance
Utilized AI-enabled, pro-active maintenance to anticipate equipment failure and prevent downtime.
Optimized Dosing
The system was equipped with a dual pump controller, providing comprehensive monitoring and control functionalities to optimize industrial processes, including precise chemical dosing.
Remote Management
Enabled remote control and monitoring via an advanced dashboard and mobile app, allowing for timely intervention and remote adjustments for best performance.
Improvement in Water Recovery Rate
By allowing remote adjustments and continuous monitoring of parameters, Terra M helped the operator fine-tune the system, minimizing water wastage and improving the overall water recovery rate (the ratio of pure water output to feed water input).
Reduction in Unplanned Maintenance Costs
The replacement of expensive components and the implementation of AI-enabled predictive maintenance reduced the total cost associated with emergency repairs and unexpected component failure.