The operations team at a Renewable Natural Gas (RNG) facility was struggling with limited data accessibility and inefficient tools for trend analysis. The existing SCADA system and manual processes were holding back the team’s ability to make timely, data-driven decisions to optimize plant operations.
Rimba’s analytics tool helped overcome these challenges by unlocking data access, simplifying analysis, and enabling insights that impacted decision making to improve operations.
Daily operations was hindered by several critical limitations in the current setup without the team realizing:
The existing SCADA system historian only allowed retrieval of a limited number of data points and lacked the ability to query specific date ranges. This made it difficult and time-consuming to correlate historical operational data with events or anomalies. It is also difficult to set up benchmarks and compare granular performance data across several RNG plants.
Without being equipped with proper access to data and analytics tools, process engineers, director of operations, and plant operators have to manually extract specific tags and create trends for analysis. In this case, data analysts are also needed to assist with using tools like SQL or Power BI. This process was slow and limited as it created a bottleneck instead of empowering each operational function to easily query data and troubleshoot issues that are often time sensitive.
KPI tracking, such as mass balance or uptime, required manual exports and calculations in spreadsheets, introducing delay and potential errors versus getting the data output in real-time.
A core pain point was also the lack of a user-friendly tool. The team needed something fast, simple, and intuitive for anyone in the company to use (such as the CEO, VP of Operations, Plant Manager, EHS Manager), without requiring specialized skills like those needed for Power BI or SQL interfaces.
Rimba’s AI-powered analytics tool addressed these pain points head-on:
Improved Trend Analysis:
Rimba’s capability to process large datasets (time series data 6 months or longer) allow for more robust trend analysis. With more data points, the team can identify subtle patterns and anomalies that might be missed with smaller datasets. This enables earlier detection of potential issues, helping prevent unscheduled downtime and maximizing production efficiency. For example, recognizing a gradual decline in a key parameter might signal the need for preventative maintenance before a major failure occurs.
Predictive Maintenance:
By analyzing historical data, the team can develop predictive models for equipment failures and maintenance needs. This allows for scheduled maintenance during less critical periods, reducing downtime and optimizing production continuity. Getting analytics and trends that show historical patterns can assist data-driven decision making, such as when to proactively replace components helps ensure consistent system performance.
Bespoke Analytics Dashboards and Alerts:
Users can create customized dashboards that visualize and monitor the metrics that matter most, tailored to their specific workflows, assets, and regulatory requirements. Intelligent alerts notify teams of threshold breaches or anomalies, which indicate potential issues based on historical data, enabling faster response times and proactive issue resolution.
Process Optimization:
Larger datasets can uncover hidden inefficiencies or bottlenecks in the production process. By identifying and addressing these areas, the team can make informed adjustments to improve overall process efficiency, resulting in increased output and lower operating costs.