AI-driven Predictive Operations
AI-driven Predictive Operations
How realistic is AI-driven predictive operations in petrochemical plants considering instrumentation reliability and process complexity?
Re: AI-driven Predictive Operations
AI predictive operations in petrochemical plants are realistic — but not in the “fully autonomous plant controlled by AI” way that vendors sometimes market!
The reality is somewhere in the middle:
Very powerful for prediction, monitoring, anomaly detection, optimization, and reliability improvement
Still heavily dependent on instrumentation quality, process understanding, and experienced human operators
The biggest limitation is not usually the AI itself.
It’s the plant data quality. And what I understand a lot of work has to be done here before dependable AI models are applied.
The reality is somewhere in the middle:
Very powerful for prediction, monitoring, anomaly detection, optimization, and reliability improvement
Still heavily dependent on instrumentation quality, process understanding, and experienced human operators
The biggest limitation is not usually the AI itself.
It’s the plant data quality. And what I understand a lot of work has to be done here before dependable AI models are applied.
Re: AI-driven Predictive Operations
I would agree with ivani1 that the data quality which is available with the plant owners is the biggest challenge to resolve and apply AI.
And operators would not still be allowing AI to take the control of the review, and making the decisions.
With AI in place, it is still believed at operators level that complete responsibility of the processes should stand with individuals. Responsible as we speak are still the humans to implement the support taken from AI.
Controlled application of AI with prediction modelling is supported however would not have an autonomous control.
And operators would not still be allowing AI to take the control of the review, and making the decisions.
With AI in place, it is still believed at operators level that complete responsibility of the processes should stand with individuals. Responsible as we speak are still the humans to implement the support taken from AI.
Controlled application of AI with prediction modelling is supported however would not have an autonomous control.
Re: AI-driven Predictive Operations
Instrumentation reliability is a challenge and then the process complexity within petrochemicals contribute a lot as where AI can support without affecting accuracy. AI in support role is already help and coupled with our knowledge, we can benefit a lot from it.
Even in general trend analysis of day to day data, if we do not keep on feeding and adding the meaningful data, no AI solution would provide reliable results.
I am in favor of applying AI on component /system level making sure that it is well coupled with updated input data. Like few examples include:
1. Compressor surge prediction
2. Bearing failure prediction
3. Seal degradation monitoring
4. Pump cavitation detection
Much before an actual alarm telling there is an issue, same issue can be identified at an initial stage.
Even in general trend analysis of day to day data, if we do not keep on feeding and adding the meaningful data, no AI solution would provide reliable results.
I am in favor of applying AI on component /system level making sure that it is well coupled with updated input data. Like few examples include:
1. Compressor surge prediction
2. Bearing failure prediction
3. Seal degradation monitoring
4. Pump cavitation detection
Much before an actual alarm telling there is an issue, same issue can be identified at an initial stage.