Events
Debutanizer Column (Distillation Column) monitoring AI
Effective monitoring of a debutanizer column is essential for maximizing the LPG content in the top product and optimizing overall distillation performance. Continuous monitoring of the C4 fraction in the bottom products ensures process efficiency and quality control. Implementing a predictive model to estimate the butane fraction in the bottoms enhances performance by allowing for real-time adjus...
Prediction of power output in natural gas-fired power generation plant AI
Predicting power output in natural gas-fired peaker power plants is critical due to their intermittent operation and high economic significance. These plants, which supply power sporadically and at a premium price, pose a unique challenge for grid operators. We aim to forecast power output based on environmental conditions, enabling more informed decisions about plant activation and power purchasi...
%O2 Prediction in the Flue Gas in Furnace Operations AI
In the domain of fired heater operations, accurate monitoring and prediction of oxygen levels in flue gases play a critical role in ensuring operational efficiency and safety. This project focuses on conducting correlation and trend analysis to identify key process parameters (KPIs) that influence oxygen concentrations in flue gases. By examining factors such as fuel composition, combustion temper...
Order fill projection AI
Predicting the end-of-cycle (EOC) for a heat exchanger due to fouling is a persistent challenge for refineries. By proactively forecasting when a heat exchanger requires cleaning, refineries can implement risk-based maintenance planning, optimizing processing rates, and reducing operating and maintenance costs. Historically, engineers had to manually combine data entries in spreadsheets, spending ...
Induced-Draft Fan Predictive Maintenance AI
In industrial settings, ensuring the uninterrupted operation of induced-draft fans is essential for maintaining profitability. However, relying solely on routine maintenance schedules can overlook potential failures and incur unnecessary expenses. Additionally, visualizing maintenance trends, particularly concerning fan vibration, becomes challenging due to dust scale build-up. This initiative foc...
Free Lime Modeling AI
Balancing lime addition to minimize both NOx emissions and free lime in clinker poses a significant challenge. With lab samples typically providing feedback with delays of 1-2 hours, operators face difficulties in timely adjustments. Excessive lime addition results in fuel wastage, while inadequate addition leads to poor-quality clinker. This initiative focuses on developing a predictive modeling ...
Burning Zone Temperature Prediction AI
In the quest for quality production, controlling temperature variations within the kiln's burning zone is paramount. The challenge lies in the diverse temperature ranges across the kiln, complicating monitoring and regulation. Low temperatures risk excess free lime, while excessive heat can compromise product quality and refractory lining integrity. This initiative focuses on predictive temperatur...
Batch Tracking and Cycle Time Analysis AI
Reducing cycle time in batch manufacturing is challenging due to the complexity of defining and analyzing process phases to identify variations and idle times between batches. Additionally, pinpointing areas for process and capital improvements requires detailed understanding and analysis. Effective batch tracking and cycle time analysis are essential to uncover inefficiencies, minimize idle times...
Filter Membrane Predictive Maintenance AI
Ineffective Clean-In-Place (CIP) procedures or fouling of filter membranes can lead to various operational challenges, including increased cycle times, lost yield, or poor product quality. Additionally, detecting and modeling long-term deterioration in filter membrane performance poses significant challengess. This use case focuses on developing predictive maintenance strategies for filter membran...
Asset Utilization (OEE) Monitoring AI
Asset Utilization (OEE) Monitoring aims to analyze the performance of batch processes by identifying time spent in various phases. This analysis helps reduce unproductive process time, such as during cleaning and maintenance, and highlights differences in manual re-cleaning events between shifts. By quantifying opportunities to reduce waiting times, OEE monitoring supports improved efficiency and ...