# Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
## 4 An Application of the Framework: Use Case of Cold Rolling
### Knowledge Acquisition Methods  
Domain expert knowledge is often considered to be of implicit and tactic nature which contradicts ontologies explicit modelling behaviour [35]. In the context of manufacturing, tacit knowledge refers to the concept of informal learning by simply performing actions and experiences, often where the knowledge is unconsciously retained in individual memory rather than being formally recorded or shared [35]. To overcome this phenomenon, many different ways of extracting tacit knowledge have been widely studied over the years. This includes techniques such as interviewing, questionnaires, protocol analysis, inferential flow analysis, and many more [36]. For our study, we conducted interviews and questionnaires with domain experts and plant operators to construct our domain expert knowledge rule set. Presently, we obtained a small sample of domain expert rules which we aim to increase in quantity and quality over time. Meanwhile, the small sample of domain expert rules demonstrate the capabilities of the framework. Table 1 displays some expert rules in a categorised format before they are translated into the SWRL format. Example 3 displays a domain expert knowledge obtainedduring the knowledge acquisition sessions which we translate as rules. As these rules come directly from knowledge acquisition sessions with domain experts, the validity and accuracy of these rules are treated as absolute. In an event that a rule creates an incorrect status, it is reviewed with experts and updated accordingly. **Example 3**. : _For any trips considered to have the 'Bad' condition, if that trip reaches a high level of tonnage and the work rolls recently had severe grindings in their last five trips, then the stopping of operation should be considered. '_  
Here, we have to explicitly define 'high tonnage' and'severe grindings' with the help of domain experts. In this case, high tonnage is a dynamic value that changes thresholds depending on the condition of the roll. Meanwhile, the condition of the work roll can be calculated by looking at the previous grindings and whether it has recently had any severe grindings. A roll grinding is considered severe if any of the last five grindings of the work roll had a stock removal value greater than 0.5 mm and therefore require a lower tonnage threshold. Additionally, the historical data of roll change reasons are also considered as part of the domain expert rule set. A severe grinding could be caused by a mill incident where the impact on the health of the roll is much greater. This knowledge is not captured in the RF but is included as expert rules. Listing 2 displays the rule in Example 3 in its SWRL format. These rules are manually translated into the SWRL format. Afterwards, they were passed into the rule engine, which combined the RF prediction with the expert knowledge, producing new knowledge stored in the data property hasStatus. The status produced is displayed using SPARQL, a well-known semantic-based querying language that enables querying based on RDF triples and graph data [31]. Listing 3 is a SPARQL query, which when executed, prints the operational trips and their corresponding classification and status.