A blast furnace heats raw iron ore to produce molten pig iron, and this furnace heating is the first major step in the steel-making process that is illustrated in Fig. 1 below.
The blast furnace at one particular steel-making Oracle cloud customer was also prone to problematic `bleeder’ events where the furnace gas pressure can surge without warning by 20-100% (see Fig. 2 below), which triggers a bleeder valve to vent excess furnace gas. Though rare, these bleeder events are undesired because they interrupt production and can have environmental impact.
To date, the furnace’s subject-matter-experts (SMEs) had not determined the root-cause of these events, so a traditional machine learning (ML) model was then trained on the furnace telemetry, to forecast whether a bleeder event would occur 30 minutes in advance. But that ML model was only a partial success. Although that ML model did alert on most events in sufficient time, testing revealed that this model also emitted three times as many false positive alerts, with those false positives causing furnace operators to ignore the alerting system. This Oracle customer then asked for assistance with this use-case, and this blog describes the recommended solution that was developed using the OCI-DataScience service on the furnace’s historical telemetry that this cloud customer had archived in its datalake within Oracle Cloud Infrastructure (OCI).
First note that this steel maker had achieved only partial success with its ML forecasting system, which tells us that this manufacturing mishap is a probabilistic event and is not deterministic. In other words, the furnace data does provide useful hints about whether a mishap is more or less likely to happen, but that data is not sufficiently diagnostic to allow an ML model to forecast when that mishap would happen again with the desired certainty.
In this circumstance, the better remedy is to instead use ML to optimize furnace operations, namely, to train an ML model to recommend to the furnace operator those furnace settings that would preserve furnace production while steering clear of those conditions that are more likely to lead to a mishap. The recommended solution trains an ML model to instead forecast a derived quantity called furnace-health where
ML for optimization: This new ML model is trained on a short list of the model’s most impactful features X, namely, furnace pressure
Note that the mishap factor
Next step: testing the solution. Lastly note that an ML optimizer is a recommendation engine whose efficacy can only be assessed while the solution is used in production. That testing strategy is known as A/B testing and involves temporarily exposing the ML model’s recommended furnace settings to the furnace operator and then monitoring whether production is sustained while bleeder frequency is reduced. Which is this Oracle customer’s next step, the results of which will be reported via a follow up blog.
Joe is an Oracle data scientist, and he specializes in delivering machine learning, analytics, and data visualization on Oracle Cloud Infrastructure (OCI). Joe received a PhD in physics from the University of Notre Dame, and also has many years experience performing astronomy research and scientific computing.