Translating MFM into FOL: towards plant operation planning

no code implementations • 19 Jun 2018 • Shota Motoura, Kazeto Yamamoto, Shumpei Kubosawa, Takashi Onishi

This paper proposes a method to translate multilevel flow modeling (MFM) into a first-order language (FOL), which enables the utilisation of logical techniques, such as inference engines and abductive reasoners.

Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning

no code implementations • 6 Mar 2019 • Shumpei Kubosawa, Takashi Onishi, Yoshimasa Tsuruoka

Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, disturbance, and operation procedures.

reinforcement-learning Reinforcement Learning (RL) +1

Railway Operation Rescheduling System via Dynamic Simulation and Reinforcement Learning

no code implementations • 17 Jan 2022 • Shumpei Kubosawa, Takashi Onishi, Makoto Sakahara, Yoshimasa Tsuruoka

The system leverages reinforcement learning and a dynamic simulator that can simulate the railway traffic and passenger flow of a whole line.

reinforcement-learning Reinforcement Learning (RL) +1

Soft Sensors and Process Control using AI and Dynamic Simulation

no code implementations • 8 Aug 2022 • Shumpei Kubosawa, Takashi Onishi, Yoshimasa Tsuruoka

During the operation of a chemical plant, product quality must be consistently maintained, and the production of off-specification products should be minimized.