KHS presents the future of filling: at BrauBeviale the machine and systems manufacturer is to introduce a new, self-learning filling valve. The system optimizes the production process with the help of artificial intelligence and at the same time considerably reduces the time and effort required for operation and maintenance. The feasibility of this flexible valve has been verified by KHS in the DnSPro research project[1].
“To date, depending on the beverage and container around 20 different types of filling valve are used,” says Jochen Ohrem, expert of R&D Management at KHS in Bad Kreuznach, Germany. “The beverage industry is increasingly calling for versatile filling systems. Digitally networked line and machine systems are also in high demand.” KHS would like to significantly push these changes which is why the Dortmund systems provider has taken part in the DnSPro research project alongside six other partners[2]. Their common goal is to develop a self-learning filling valve with which beverage producers can fill all liquids into all existing types of container. This would do away with the need for manual conversions and the effort required for operation and maintenance would be greatly reduced, says Ohrem.
Self-learning and digitally networked
“We developed cyber-physical systems for this purpose, with the help of which the valve can determine how to best fill a certain beverage into a certain container as quickly as possible,” Ohrem continues. The filling process is analyzed with the assistance of a camera. This continuously monitors the inclusion of bubbles and foaming to prevent excessive foaming and thus product loss.
With the help of microcontrollers and the camera’s evaluation electronics, the filling valve is opened to varying degrees by a stepper motor depending on the fill level. “The focus was on ‘learning’ a number of skills: self-configuration, analysis, self-diagnosis and, ultimately, self-optimization,” explains Ohrem. The future objective behind all this is to increase flexibility and energy and resource efficiency in production through the application of an autodidactic system of artificial intelligence. For the first time at BrauBeviale KHS will be presenting the key data on this intelligent filling valve which fully satisfies all of the previously specified project requirements.
Ideas for the future of the beverage industry
“The development is now entering the next phase where we’ll be gathering further experience with this prototype,” Ohrem states.
He already has one idea for later use of the valve in practice. Instead of a filling computer centrally positioned on the machine that regulates the process of all valves, in the future this task is to be managed locally by miniaturized computers installed on each valve group. This would allow a simple sensor such as a pressure sensor to be inserted into each filling valve which documents and analyzes the pressure curve, resulting in a process which optimizes itself of its own accord. “As less effort is involved in installation, this should yield a number of cost and time benefits, for instance during commissioning,” concludes Ohrem.
[1] DnSPro – sensor-based subsystems with decentralized cooperation for Industry 4.0 production systems.
[2] Besides KHS, Infineon, WIBU Systems, EPOS, the Ruhr University in Bochum and Ostwestfalen-Lippe University of Applied Sciences are involved in the research project. The project is coordinated by KROHNE Innovation from Duisburg, a supplier of sensors and measuring equipment and long-term partner of the Dortmund engineering company.
“To date, depending on the beverage and container around 20 different types of filling valve are used,” says Jochen Ohrem, expert of R&D Management at KHS in Bad Kreuznach, Germany. “The beverage industry is increasingly calling for versatile filling systems. Digitally networked line and machine systems are also in high demand.” KHS would like to significantly push these changes which is why the Dortmund systems provider has taken part in the DnSPro research project alongside six other partners[2]. Their common goal is to develop a self-learning filling valve with which beverage producers can fill all liquids into all existing types of container. This would do away with the need for manual conversions and the effort required for operation and maintenance would be greatly reduced, says Ohrem.
Self-learning and digitally networked
“We developed cyber-physical systems for this purpose, with the help of which the valve can determine how to best fill a certain beverage into a certain container as quickly as possible,” Ohrem continues. The filling process is analyzed with the assistance of a camera. This continuously monitors the inclusion of bubbles and foaming to prevent excessive foaming and thus product loss.
With the help of microcontrollers and the camera’s evaluation electronics, the filling valve is opened to varying degrees by a stepper motor depending on the fill level. “The focus was on ‘learning’ a number of skills: self-configuration, analysis, self-diagnosis and, ultimately, self-optimization,” explains Ohrem. The future objective behind all this is to increase flexibility and energy and resource efficiency in production through the application of an autodidactic system of artificial intelligence. For the first time at BrauBeviale KHS will be presenting the key data on this intelligent filling valve which fully satisfies all of the previously specified project requirements.
Ideas for the future of the beverage industry
“The development is now entering the next phase where we’ll be gathering further experience with this prototype,” Ohrem states.
He already has one idea for later use of the valve in practice. Instead of a filling computer centrally positioned on the machine that regulates the process of all valves, in the future this task is to be managed locally by miniaturized computers installed on each valve group. This would allow a simple sensor such as a pressure sensor to be inserted into each filling valve which documents and analyzes the pressure curve, resulting in a process which optimizes itself of its own accord. “As less effort is involved in installation, this should yield a number of cost and time benefits, for instance during commissioning,” concludes Ohrem.
[1] DnSPro – sensor-based subsystems with decentralized cooperation for Industry 4.0 production systems.
[2] Besides KHS, Infineon, WIBU Systems, EPOS, the Ruhr University in Bochum and Ostwestfalen-Lippe University of Applied Sciences are involved in the research project. The project is coordinated by KROHNE Innovation from Duisburg, a supplier of sensors and measuring equipment and long-term partner of the Dortmund engineering company.
