This paper presents the optimization of the power required to activate a jaw-type blowout preventer (BOP) in the oil industry using an axial piston pump. Experimental and numerical methods were combined to analyze the effects of pressure, flow rate, volumetric efficiency, and clearance leakage on energy consumption. Taguchi methodology with an orthogonal array and the “smaller-is-better” criterion was used in the experiments, while regression analysis provided a predictive model. Optimization was performed using the Grey Wolf Optimizer (GWO) in Python 3.13. The results show that pressure and flow rate significantly affect power consumption, while higher volumetric efficiency leads to notable energy savings. The optimal configuration reduced the power demand to 5.0001 kW. Based on this, reliability models were created to assess deviations from optimal conditions. The study demonstrates the effectiveness of combining statistical and optimization techniques for improving safety systems in the oil industry. The key contribution of this study lies in the integration of experimental Taguchi-based modeling with Grey Wolf Optimizer (GWO) metaheuristic techniques to optimize the energy-efficient activation of jaw-type blowout preventers, representing a novel methodological approach in the field of hydraulic safety systems in the oil industry.

Correlation, functioning as a symmetric relation, is very powerful indicator of the mutual association between two attributes. The problem of weak correlation indicates a lack of linearity in the observed range. This paper presents the precise piecewise correlation method, which overcomes the problem by determining the segments where the linear association will be present. The determination was achieved using the histogram segmentation method. The conditions of the application and analysis of the method are presented, as well as the application of the method to the representative datasets. The obtained results confirm the existence of stronger linear associations on the segments. Detected correlations reveal the strength and nature of the symmetric association between two attributes on each of the separated segments.

This paper presents a dashboard-oriented visual analytics workflow for classification-related exploratory analysis based on Information Gain (IG), histogram segmentation, and complementary localized interpretation through the Precise Piecewise Correlation (PPC) method. The workflow is designed to support the construction of a primary dashboard view by prioritizing attributes with stronger relevance to the decision variable and inspecting their class-related behavior within segmented histogram intervals. Rather than introducing a new standalone feature-selection metric, this study formalizes how established analytical components can be integrated into a coherent dashboard framework for structured visual inspection. The proposed workflow was examined on three datasets from different application domains: the Iris dataset, an educational performance dataset, and an Oil and Gas dataset. Across these cases, IG-based prioritization identified attributes that provided clearer class-related structure in the primary dashboard view, while histogram segmentation supported interval-level interpretation of class concentration and overlap. A compact quantitative evaluation further showed that top-ranked IG subsets retained strong discriminative information under standard classification models, whereas lower-ranked subsets generally performed less favorably. Entropy-based segment analysis additionally indicated lower local class uncertainty for higher-ranked attributes. A small user study provided preliminary user-centered support for the interpretability and practical usefulness of the proposed dashboard structure. The results suggest that the proposed workflow can support dashboard-based inspection of class-related patterns across different contexts.