Feed flux management in g asification p rocess is critical to enable consistency in operation and performance. Variation in individual components of feed lead s to a huge impact o n s lag viscosity and a sh fusion temperature. Crude unit processes different crude , which results in a huge variation in p etcoke feed components. C ritical to monitor metal from crude to track/predict the variation in p etcoke metal and advance corrective actions.
MTF
Crude
Coal
RTF
Output layer
Input layer
Hidden layers(3)
Yard
CDU
VDU
Limestone
SRFO
DCU
Input metal Input flow rate Predicted metal
Figure 2 Analytics predict slurry metals from crude assay and CDU/VDU
and based on these predictions, feed/flux corrections are made to avoid disturbances in the process (see Figure 2 ). Panel operator action automations Addresses human error during non- routine activities such as start-up, shutdown, and transient operations . This approach focuses on reducing the likelihood of mistakes that commonly occur when operators perform tasks outside normal steady state operations. Non-routine phases, such as equipment start-up, system shutdown, maintenance transitions, and other transient operating conditions, often involve complex sequences, manual interventions, and time- critical decisions. By implementing structured procedures, advanced automation, and enhanced monitoring during these periods, the system helps mitigate risks associated with human fatigue, stress, limited visibility, or process variability. Automated operator actions implemented across more than 31 use cases . A broad set of automated workflows has been deployed to support operators in scenarios where manual execution could introduce variability or risk. These automations cover more than 31 distinct use cases, each designed to standardise responses, ensure consistent execution, and maintain operational integrity. Whether it involves routine adjustments, safety-critical responses, or optimisation
during abnormal conditions, these automated actions provide reliability, repeatability, and faster reaction times compared to manual operations. Example : Automated line-up and isolation of a process column. One practical illustration of this automation strategy is the automated line-up and isolation procedure for a process column. Instead of relying on operators to manually configure multiple valves, verify system readiness, and follow complex isolation sequences, the automation system handles these tasks with precision. It confirms equipment states, executes the required steps in the correct order, and ensures compliance with safety and operational standards. This reduces operator workload, minimises the chance of procedural errors, and enhances overall process safety and efficiency (see Figure 3 ). Early event detection (EED) Analytics have been developed to provide EEDs to avoid any event in the plant. The deployed analytic algorithm continuously monitors all the parameters and compares them against the threshold limits. Alerts are raised against any detected deviation. The EED system benefits the plant by: Providing early alerts to operators for corrective action and failure prevention . Advanced monitoring and diagnostic systems continuously analyse equipment and process conditions to identify early signs of degradation,
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