Yokogawa Electric Corporation and NTT DOCOMO (DOCOMO) said they have conducted a proof-of-concept test (PoC) of a remote control technology for industrial processing.
The PoC test involved the use in a cloud environment of an autonomous control AI, the Factorial Kernel Dynamic Policy Programming (FKDPP) algorithm developed by Yokogawa and the Nara Institute of Science and Technology, and a fifth-generation (5G) mobile communications network provided by DOCOMO.
The test, which successfully controlled a simulated plant processing operation, demonstrated that 5G is suitable for the remote control of actual plant processes.
The trend to locate production facilities in remote and/or hazardous areas in recent years is fueling a growing demand for remote industrial operations and transforming how people work.
Meanwhile, equipment used in plants to purify and refine resources and materials for essential products can deteriorate after many years of use, so remote, autonomous regulation and control would be hugely beneficial.
One possible solution is to install edge devices equipped for high-speed wireless communications at plants and employ cloud-based autonomous control AI to control the equipment dynamically.
Yokogawa has already proven that its FKDPP algorithm is a feasible autonomous control AI solution.
In a field test at a chemical plant in February, for 35 days FKDPP successfully controlled processes known to be difficult to automate using existing PID and APC control technologies, and which therefore had been performed manually.
The combination of FKDPP and the cloud with 5G, which offers low latency and the capability to connect a large number of devices, promises to be a core technology for achieving industrial autonomy.
Following an agreement between Yokogawa and DOCOMO on April 14, 2021, the demonstration test was conducted to verify whether a three tank level control system could be controlled using FKDPP in the cloud via a 5G network.
A target water level was set, tests with low- to high-speed control cycles were conducted, and the effects of mobile-communications latency on FKDPP control were confirmed.
Compared to 4G, the test demonstrated that especially with high-speed control 5G delivers: lower latency, less overshoot relative to the target water level, and is capable of handling a control cycle as short as 0.2 seconds, thereby achieving better control for more stable quality and higher energy efficiency.
Yokogawa, which has been advocating the concept of industrial automation to industrial autonomy (IA2IA) since 2019, aims to apply 5G technology for remote plant control.
In collaboration with DOCOMO and customers, it will continue carrying out advanced initiatives aimed at facilitating a shift toward industrial autonomy.
DOCOMO is continuing to enhance and evolve its network technologies, create advanced networks tailored to the needs of specific customers, and develop 5G solutions for diverse public and private purposes.
Both Yokogawa and DOCOMO, as members of the 5G Alliance for Connected Industries and Automation (5G-ACIA), which is pursuing industrial applications for 5G, will continue to evaluate the use of 5G for remote, autonomous plant operations.
By carrying out demonstrations in a wide range of customers’ plants and examining communications reliability and latency-related issues during long-term use, the companies will strive to achieve 5G and AI-enabled autonomous control.
The 5G-ACIA will present the results of this demonstration test at Hannover Messe 2022, between May 30 and June 2.
About the autonomous control AI, Kenji Hasegawa, Vice President and Head of the Yokogawa Products Headquarters at Yokogawa Electric Corporation, said: "It can be used in areas where existing control technologies cannot be applied, can achieve shorter settling times compared to existing technologies, and prevent overshoot."
Hisakazu Tsuboya, Senior Vice-President and General Manager of the 5G & IoT Business Department at NTT DOCOMO, said: "The demonstration has shown that low-latency 5G communications helps to improve the accuracy of remote-control operations in plants, which is expected to contribute significantly to sustainable productivity for processing and other types of manufacturing."