TrendMiner

A 7-Recipe Success Story 📊  Created from your own data: We recently partnered with the process engineering team at a leading manufacturing plant. They were grappling with inconsistent results across their seven different batch recipes. Fluctuations in temperature, pressure, and other parameters led to quality issues and wasted resources.    Leveraging Data Contextualization, they were able to quickly gain a comprehensive understanding of the complex interactions within each recipe. By analyzing historical data, they identified 15 "golden batches" with optimal operating conditions, establishing a reliable baseline for comparison.    From there we added the following calculations to the search results:  - Total energy consumption (Integral)  - Maximum concentration  - Average Pressure  - Average Temperature  - End value of End-product quality    The continuous monitoring capabilities automatically alerted operators whenever a batch deviated from the golden fingerprint. This enabled proactive adjustments, minimizing deviations and ensuring consistent quality across all seven recipes.    The result: A significant reduction in waste and a notable improvement in overall product quality.      TrendMiner #ProcessOptimization #DataDriven #QualityControl #BatchProcessing #DataEngineer

Resolving a crystallization challenge in chemical processing     How we did it ⬇️ Recently, we partnered with a chemical processing plant experiencing recurrent crystallization issues in their mixing tank. This was causing sensor malfunctions and threatening product quality – a significant concern for their operations.    Here’s what we did:    After visualizing the sample composition, the decreasing solvent concentration stood out – a clear culprit behind the crystallization. But why was the solvent concentration dropping in the first place?   We didn't have any immediate theories, so we turned to the cross-correlation feature. By analyzing the slow decrease in solvent concentration, we found a surprising correlation with a level drop in a completely different tank upstream. Next, we zoomed out on the timeline and saw that this level drop always followed a sharp increase, perfectly matching the addition of an external intermediate product. Turns out, this external addition triggered a chain reaction: 1. Higher recycle flow: To handle the different product composition, the team had increased the recycle flow upstream.  2. Altered properties: The increased recycle flow changed other properties within the process.  3. Different solvent split: These property changes ultimately shifted the ratio of solvent to other components, leading to less solvent reaching the mixing tank and causing crystallization issues. We cross-checked historical data and, sure enough, the same pattern emerged every time the external product was introduced.    The result: Identified the clear root cause and mechanism behind the crystallization issues. This level of insight wouldn't have been possible with other tools. With this knowledge, our client can now take corrective action whenever the external intermediate product is added, ensuring consistent product quality and reliable measurements.    TrendMiner #ChemicalEngineering #ProcessOptimization #RootCauseAnalysis #DataScience 

Beyond Pre-Built Solutions 📊 How platform flexibility empowers engineers: I'm always inspired by how our users leverage our platform to tackle complex challenges in the chemical industry.    Recently, we collaborated with a process engineer to streamline the calculation of hourly consumption rates for critical resources like energy, steam, and electricity. These precise calculations are crucial for optimizing resource utilization and ensuring smooth process control.   By leveraging the tag builder and formula functionalities, they were able to create two new tags representing both rolling and fixed hourly averages.   This allowed them to monitor consumption trends in real-time, detect anomalies, and identify opportunities for optimization.   The result: Enhanced resource management, reduced costs, and a more efficient overall process.    TrendMiner #DataAnalytics #ProcessOptimization #ChemicalEngineering #ResourceUtilization