Anticipating Energy-driven Crises in Process Industry by AI-based Scenario Planning

Power outages and fluctuations represent serious crisis situations in energy-intensive process industry like glass and paper production, where substances such as oil, gas, wood fibers or chemicals are processed. Power disruptions can interrupt chemical reactions and produce tons of waste as well as damage of machine parts. But, despite of the obvious criticality, handling of outages in manufacturing focuses on commissioning of expensive proprietary power plants to protect against power outages and implicit gut feeling in anticipating potential disruptions. With AISOP, we introduce a model for AI-based scenario planning for predicting crisis situations. AISOP uses conceptual, well-defined scenario patterns to capture entities of crisis situations. Data streams are mapped onto these patterns for determining historic crisis scenarios and predicting future crisis scenarios by using inductive knowledge and machine learning. The model was exemplified within a proof of concept for energy-driven disruption prediction. We were able to evaluate the proposed approach by means of a set of data streams on weather and outages in Germany in terms of performance in predicting potential outages for manufacturers of paper industry with promising results

Cascading Scenario Technique Enabling Automated And Situation-based Crisis Management

Crises are becoming more and more frequent. Whether natural disasters, economic crises, political events, or a pandemic - the right action mitigates the impact. The PAIRS project plans to minimize the surprise effect of these and to recommend appropriate actions based on data using artificial intelligence (AI). This paper conceptualizes a cascading model based on scenario technique, which acts as the basic approach in the project. The long-term discipline of scenario technique is integrated into the discipline of crisis management to enable short-term and continuous crises management in an automated manner. For this purpose, a practical crisis definition is given and interpreted as a process. Then, a cascading model is derived in which crises are continuously thought through using the scenario technique and three types of observations are classified: Incidents, disturbances, and crises. The presented model is exemplified within a non-technical application of a use case in the context of humanitarian logistics and the COVID-19 pandemic. Furthermore, first technical insights from the field of AI are given in the form of a semantic description composing a knowledge graph. In summary, a conceptual model is presented to enable situation-based crisis management with automated scenario generation by combining the two disciplines of crisis management with scenario technique