Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning

Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, the autonomous control of such robots is still a big open challenge due to the high number of actuators and the complex manoeuvres required. To address this problem, we investigate the usage of Deep Reinforcement Learning for achieving autonomous navigation of in-pipe robots in pipeline networks with complex topologies. Moreover, we introduce a hierarchical policy decomposition based on Hierarchical Reinforcement Learning to learn robust high-level navigation skills. We show that the hierarchical structure introduced in the policy is fundamental for solving the navigation task through pipes and necessary for achieving navigation performances superior to human-level control

The influence of the removal of specific NOM compounds by anion exchange on ozone demand, disinfection capacity, and bromate formation

This research on a pilot scale focuses on the reaction of ozone with natural organic matter (NOM) for three water qualities with different dissolved organic carbon (DOC) concentrations and NOM compositions, obtained after several stages of an anion exchange process. It was shown that for the same ozone dosage per DOC, the ozone demand was higher, less bromate was formed and a lower disinfection capacity was reached for water containing mainly humic substances, than for water where the humic substances were partly removed. It can be concluded that NOM composition, specifically the humic substances, influences the ozone demand, disinfection capacity and bromate formation