A renewable fuels producer processing tallow and soy feedstocks faced significant challenges in extracting insights from massive amounts of operational data. Their processing units generated thousands of PI tags and millions of data points in short timeframes. Consequently, limited visibility into this critical data made it difficult to detect inefficiencies or prevent downtime before small issues became major problems.
To address this, Persepta implemented a modern solution architecture using Azure and PI Cloud Connect. This transformation enabled seamless data aggregation, real-time analytics, and automated alerts. With these improvements, operators could make faster decisions, detect issues proactively, and significantly enhance efficiency. Ultimately, the new system minimized downtime, improved production processes, and simplified daily operations.
A renewable feedstock and fuels producer faced major challenges managing operational data across multiple refining units. Key personnel relied on manual data extraction from PI tags using complex, error-prone spreadsheets. This process was slow, inconsistent, and frustrating. As a result, analyzing trends and detecting critical issues became difficult.
Due to the lack of real-time access to accurate data, the company struggled to identify inefficiencies, anticipate equipment failures, and improve production. The labor-intensive reporting process delayed responses to operational problems, increased downtime risks, and caused missed opportunities for process optimization. Ultimately, this lack of visibility hurt efficiency, production output, and profitability.
Persepta first developed and implemented a scalable solution architecture using Microsoft Azure, PI Cloud Connect, and Power BI. This system centralized and automated data collection from thousands of PI tags across multiple refining units overall. It eliminated manual data extraction, ensuring seamless real-time intelligence within a cloud-based analytics environment for instant access and analysis.
The system automatically ingests, normalizes, and organizes PI data, giving engineers and operators Power BI-driven insights into vast datasets. Next, custom dashboards and automated alerts provided real-time intelligence on key operational parameters. Teams could now monitor performance, detect anomalies, and adjust processes instantly—without manual data processing burdens.
With real-time intelligence and Power BI insights, the renewable fuels producer gained instant access to accurate, structured data—eliminating the need for manual extraction and error-prone spreadsheets. Engineers and operators could analyze trends, track key performance metrics, and identify inefficiencies across multiple refining units with ease.
Automated data processing and real-time alerts allowed the team to detect and resolve issues before they led to downtime. The new system improved visibility, reduced reporting workload, and enabled faster, data-driven decisions. As a result, the company achieved significantly greater efficiency and a more optimized production process.
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