Interpretable data-driven modeling in biomass preprocessing

Data-driven models provide a powerful and flexible modeling framework for decision making and controls in industry. However, extracting knowledge from these models requires development of easily interpretable visualizations. In this paper, we present a data-driven methodology for modeling and visualization of relative equipment workload in a biomass feedstock preprocessing plant. The methodology is designed to serve in two main fronts: (1) knowledge discovery and data-mining from instrumentation data, (2) improving situational awareness during monitoring and control of the plant. We used Gaussian Processes to create a model of the expected current overload rate of for each of the electric motors involved in the plant. The expected number of overloads on each equipment was used to quantify and visualize the relative workload of the different components of the system. The visualization is presented in the form of an intuitive directed graph, whose properties (node size, position, colors) are driven by overload rates estimations.