Lummus Digital

CGC Fouling Mitigator

Real-time insights for peak efficiency.

Physics Meets AI: Hybrid Models for Optimization.

Introducing the CGC Efficiency Monitoring solution, designed to optimize the operational performance of Charge Gas Compressors (CGC) by predicting and mitigating  fouling issues in real-time. Our advanced hybrid model empowers process  engineers to maintain optimal efficiency, reduce downtime, and drive cost savings  across your operations.

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Hybrid Model

A hybrid model integrates a first-principles model (based on physical laws) with a machine learning model (data-driven predictions) to enhance accuracy and flexibility. The first-principles model provides a structured, mechanistic understanding of the system, while machine learning fills in the gaps, manages uncertainties, and adapts to complex, real-world scenarios. By combining these, the fundamental engineering laws in the first-principles model work together with data-driven algorithms in the prediction model to optimize compressor performance.

Powering Intelligent Plant Operations:

AI-Powered Precision for EBIT Growth

Transform your Challenges into Opportunities

CGC Efficiency Monitoring is designed to address the critical challenges faced by industrial operations, offering advanced AI-driven solutions that not only predict and prevent potential issues but also optimize performance, reduce costs, and enhance overall efficiency.
 
Experience increased conversion, real-time predictions of downstream feed and product quality parameters, and optimized hydrogen consumption—transforming your operations for greater efficiency and profitability.

Pain Point

Benefit

Real-time monitoring and predictive analytics identify potential fouling  issues before they cause operational disruptions.

Pain Point

Benefit

Monitor and manage up to six compressor stages, along with aftercoolers, through a comprehensive and flexible dashboard designed to meet your specific needs.

Pain Point

Benefit

Optimize energy usage and enhance efficiency with actionable recommendations based on AI-driven insights and hybrid model approaches.

Pain Point

Benefit

Minimize unplanned downtime with predictive maintenance capabilities,  reducing both costs and operational risks.

Performance Deviation Prediction

Predict the fouling rate at each compression stage along with the corresponding fouling rates for related intercoolers and heat exchangers, based on real-time operational data and conditions.

Stage-wise monitoring of compressor parameters, including pressure rise, temperature rise, after-cooler differential pressure (ΔP), and after-cooler differential temperature (ΔT)

Trend plots for :
1.Wash Oil Injection
2.Attemporation Water
3.Antifoulant Injection
4.Polytropic Efficiency %
5.Theoretical Compressor
6.Efficiency vs. Actual Compressor

A comparison plot between “Current Operation” and “Recommended Operation” is displayed, allowing for a clear visual assessment of the two operational scenarios.

Actionable recommendation (comparing with current values) on controllable parameters (each stage- wise) like Wash Water Injection, BFW Injection, Antifoulant Injection, and Fouling Rate.

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