Monitoring von Bodenmaterialien zur Einschätzung potenzieller Verwertungswege

Ziel eines laufenden UFOPLAN-Vorhabens ("Monitoringprogramm zur Verwertung von Bodenmaterial gemäß §§ 6-8 Novelle BBodSchV" FKZ 3716 74 203 0) ist es, Datendefizite für Bodenmaterialien, die unter die neuen Kriterien der Verwertungsmöglichkeiten außerhalb der durchwurzelbaren Bodenschicht nach den §§ 6 bis 8 der Novelle der BBodSchV [1,2] fallen, zu schließen und potenzielle Verwertungsmöglichkeiten aufzuzeigen. Neben den Feststoffwerten wird dabei das Elutionsverhalten zur Bewertung herangezogen. Durch die Umstellung des Elu-tionsverfahrens von einem W/F von 10 l/kg (bisherige Regelung in der LAGA M 20) auf ein W/F von 2 l/kg bestehen erhebliche Unsicherheiten im Hinblick auf die Einstufung von Bodenmaterialien zur Verwertung. Neben der Untersuchung relevanter Bodenmaterialien werden auch vorhandene Daten ausgewertet. Der Schwerpunkt der Untersuchungen wurde auf die Schadstoffgruppe der PAK gelegt

Human in the AI Loop: Teaching Shop Floor Workers Artificial Intelligence in Production

In recent years artificial intelligence (AI) and machine learning are finding their way into production processes. The speed of introduction of these promising technologies is hampered by inadequate competencies of associates on all hierarchical levels. On the shop floor level, the challenge is to identify and teach the necessary competencies to operate and maintain complex AI-based systems. The project ``Human in the AI loop'' funded by the EIT Manufacturing focuses on that question and develops online learning tutorials that fit to the required competences a shop floor employee needs to understand and operate an AI-equipped production line. The approach adopted to reach this goal comprises three steps: First, interviews with experts coming from the industrial AI domain have been executed to identify the most common application areas and use cases occurring in modern production facilities, i.e., optical quality inspections and time series data analytics. Based on this input, a map of competences has been assembled, employees should have to successfully deal with AI opportunities and challenges that occur in his/her company. And finally, online tutorials have been developed to cover the needs of the different roles within the shop floor. The tutorials themselves are divided into small so-called ``learning nuggets'' that can be executed by the participants even ``on the job'' and at their own pace. Theoretical input and practical tasks alternate to not only provide theoretical knowledge as other courses already do, but to allow for experiments with industrial data, too. The paper highlights the expert interviews, presents the competence map, and - by means of an example — discusses two of the online tutorials - one with a focus on technical aspects of data and another one dealing with social aspects to build more trustworthy AI