# Challenge-Based Learning and Scrum as Enablers of 4.0 Technologies in Engineering Education
## 3 Results
### Examples of Challenges Addressed by Students
#### 3.1.3 Example 3--Accurate Positioning of North Magnetic for Solar Panel Installation  
A third group of Industrial Engineering students, although from the same subject, \"Business Workshop\", addressed the challenge of identifying the magnetic north for positioning solar panels in a company dedicated to installing these devices. The company's usual process, which depended on a traditional technique with certain limitations in terms of repeatability, presented the challenge of improving efficiency during the installation of the panels through a more precise determination of the magnetic north. In this case, the students proposed a new methodology based on a prediction model of the aforementioned magnetic north. They then demonstrated how, using a collaborative robot, a solar panel could be positioned with high precision oriented to the magnetic north, thereby achieving an optimal location to maximize solar capture. The company, recognizing the potential of the new methodology developed by the students, decided to implement it and then proceed with the on-site verification of the accuracy of the prediction model. Figure 4 shows the simulated positioning towards magnetic north for a solar panel.  
Figure 4: Simulated positioning towards magnetic north for a solar panel.  
Figure 3: Identification and solving of congestion problems in vehicular accesses: (a) 3D rendering of parking lot plan; (b) restructuring of layout and paths for vehicular flow.  
1.4 Example 4--Development of a Predictive Model to Determine the Viscosity of Complex Brines for the Lithium Industry Mining Using AI  
A final example concerns Mining Engineering students who attended the \"Business Workshop\" course. In this case, the students addressed the challenge of developing a predictive model to determine the viscosity of complex brines, specifically in lithium nitrate (LiNO\({}_{3}\)) solutions, as part of a challenge proposed by a company specialized in the lithium industry. The main objective was to improve the accuracy in predicting the rheological properties of brines using mathematical models and technological tools based on AI. The data used for the development of the model were provided by the mining company, which carried out the corresponding measurements. For their part, the students focused on analyzing these data and developing the predictive model requested by the company. In particular, they implemented exploratory analysis techniques and neural networks, using the programming language Python and specialized deep learning libraries to build a model that would allow them to predict the viscosity of solutions under different temperature conditions more accurately. Currently, the results of the trained model have shown a high correlation with the experimental data, which allowed for validation of its applicability in processes associated with the lithium industry. Subsequently, the mining company continued working with the students who participated in this challenge to strengthen the predictive capacity of the developed model and thus contribute to the optimization used in the lithium industry. Figure 5 shows a mining student developing part of the computer code for the predictive model raised by the mining company as a challenge.