## 5 Adoption Case in the Steel Industry
### Solution and Results  
To address the improvements in the process, the steel company chose the solution proposed by Canvass AI with the following objectives:  
1. Identify the parameters that influence the maximization of production performance using process data generated by the OSI PI platform. 2. Predict product quality and performance at different intervals, and use those predictions to adjust control parameters in real time, thus maximizing product quality and performance. 3. Apply a proactive policy for the resolution of problems and anomalies of the process based on having advance information about what happens in the casting process. 4. Institutionalize the best practices of the operators and provide those responsible for the information processes that facilitate decision making. The solution was adopted after identifying the project objectives mentioned above. A forecasting template model from the CA platform was chosen to predict process behavior in advance. This prebuilt configuration was chosen to forecast parameters on currently known data to understand the impact of the current process variables. This way, it materialized the Low-Code/No-Code strategy of the original business model proposed by CA. Then, the historical data was revised according to quantity and quality for the model to learn from. Then, an exploration and preparation phase was performed to obtain insights from the data and prepare for modeling. The last step with the historical data was to train the model and validate the results. Then, the in-house historian database from OSI PI was integrated through an application programming interface (API). In this way, the data from the processes was ingested by the Canvass AI platform that was hosted in the Azure cloud, through real-time streaming. The model was productionized, and then real-time predictions began to appear. The results were monitored, and adjustments were performed. Finally, the model results returned predictions to facilitate the decisions on changes in the process to allow for its improvement. Figure 1 shows the seven-step process followed to achieve the results that allowed the optimization of the processes. The adoption process took two months until project kick-off in order to have the model on line ingested with real-time data, and generate predictions to suggest better operation conditions to the furnace operators. In this period, the people in the process were taught how to use and understand the information given by the solution, while people from the platform provider adjusted the model. The validation was done together by the software engineers and plant operators. The CA platform made it easy to define the relationships between variables that significantly affected the production result, such as quantity of silicon added and fuel consumption. The AI models collaborated to determine which of the control variables had a positive or negative impact on production quality. By applying ML-reinforced learning through the forecasting template, the result of the production was predicted in certain duly specified intervals. Historical data were taken from a period of one year, and parameters such as energy consumption, temperature, and silicon content, among others, were analyzed to generate a model that allowed the prediction of when the alloy with the highest silicon content should be conditioned, and when to increase or decrease the furnace temperature. In this way, it worked proactively, and the furnace operator received information in advance for decision making. As a result of using data generated by the MES platform through ML methodology, the steel firm obtained a competitive advantage by improving processes and achieving consistency in quality, with silicon content always at a normal range. In addition, by defining set points in real time for fuel consumption, energy savings were facilitated. The firm also reduced the percentage of scrap, thus reducing costs and producing higher-quality steel.