Yokogawa Electric (Yokogawa)
Software : Operational Technology : General
Yokogawa is a leading provider of Industrial Automation and Test and Measurement solutions. Combining superior technology with engineering services, project management, and maintenance, Yokogawa delivers field proven operational efficiency, safety, quality, and reliability.
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In a World First, Yokogawa’s Autonomous Control AI Is Officially Adopted for Use at an ENEOS Materials Chemical Plant
ENEOS Materials Corporation (formerly the elastomers business unit of JSR Corporation) and Yokogawa Electric Corporation (TOKYO: 6841) announce they have reached an agreement that Factorial Kernel Dynamic Policy Programming (FKDPP), a reinforcement learning-based AI algorithm, will be officially adopted for use at an ENEOS Materials chemical plant. This agreement follows a successful field test in which this autonomous control AI demonstrated a high level of performance while controlling a distillation column at this plant for almost an entire year. This is the first example in the world of reinforcement learning AI being formally adopted for direct control of a plant.
Over a 35 day (840 hour) consecutive period, from January 17 to February 21, 2022, this field test initially confirmed that the AI solution could control distillation operations that were beyond the capabilities of existing control methods (PID control/APC) and had necessitated manual control of valves based on the judgements of experienced plant personnel. Following a scheduled plant shut-down for maintenance and repairs, the field test resumed and has continued to the present date. It has been conclusively shown that this solution is capable of controlling the complex conditions that are needed to maintain product quality and ensure that liquids in the distillation column remain at an appropriate level, while making maximum possible use of waste heat as a heat source. In so doing it has stabilized quality, achieved high yield, and saved energy.
Yokogawa Launches Autonomous Control AI Service for Use with Edge Controllers
Yokogawa Electric Corporation (TOKYO: 6841) announces the launch of a reinforcement learning service for edge controllers. This autonomous control service for OpreX™ Realtime OS-based Machine Controllers (e-RT3 Plus) utilizes the Factorial Kernel Dynamic Policy Programming (FKDPP) reinforcement learning AI algorithm, and consists of packaged software and an optional consulting service and/or a training program, depending on end user requirements. This software is being released globally, while consulting and the training program will be provided first in Japan, then in other markets.
Yokogawa Acquires Fluence Analytics, a Pioneer in Digitalizing the Monitoring of Polymerization Reaction Processes
Yokogawa Electric Corporation (TOKYO: 6841) announces the acquisition of Fluence Analytics, Inc., a US-based startup that provides real-time analytics solutions to polymer and biopharmaceutical companies worldwide. Since signing investment and collaboration agreements in August 2021, the two companies have been exploring potential business opportunities. Through this acquisition, Fluence Analytics will integrate its operations with Yokogawa’s existing business and further enhance its technological capabilities. Starting today, Fluence Analytics will operate as Yokogawa Fluence Analytics.
Yokogawa Enters Investment and Collaboration Agreement with Ideation3X
Yokogawa Electric Corporation (TOKYO: 6841) announces that it has made a US$10 million Series B investment* in Ideation3X Pte. Ltd. (i3X), a Singapore-based venture company that is targeting the rapidly expanding integrated solid waste management (ISWM) sector in India with a process that adopts a circular economy approach. The two companies have also signed a business collaboration agreement. With this investment in the high-growth ISWM field, Yokogawa aims to develop its business in the Indian market.
A High-speed and High-precision Color Sensor for Improving Color Management in the Paper-making Process
Thanks to the expanding retail business, the paperboard market in Asia is growing and thus the demand for paper color control is increasing. To meet this need, online measurement of paper chromaticity in the paper-making process is used to ensure strict quality control of paper color. Yokogawa has enhanced the functions of the LED color sensor for the B/M9000VP paper quality control system. A new high-sensitivity spectroscope enables high-sensitivity and high-speed measurements, and a moisture-proof coating on components has improved moisture resistance. With the enhanced functionality and robustness of the LED color sensor, the B/M9000VP has improved quality control in the paper-making process.
Yokogawa Invests in Waylay to Support Continued Rapid Expansion of Cloud-based Solutions
Yokogawa Electric Corporation (TOKYO: 6841) and Waylay NV (“Waylay”) announce that Yokogawa has invested in Waylay, a leading Belgium-based enterprise information technology and operational technology (IT-OT) digital unification software company. Yokogawa and Waylay have been successfully collaborating on digital transformation (DX) solutions and services. The equity investment will help accelerate the growth of Yokogawa’s cloud portfolio of applications and services while helping Waylay expand its market reach.
In addition to an existing IA-focused Software as a Service (SaaS) portfolio, Yokogawa customers will also have the option to opt for other enterprise solutions in the areas such as utilities, smart buildings, telecom, and data centers on the Yokogawa Cloud. The strengthened partnership will allow Waylay to generate more real-world use cases for its technologies and serve a wider array of industries to continuously enhance its digital offerings. The customers of both companies can now also experience a broader array of enhanced and differentiated DX services combining OT and IT capabilities.
Yokogawa and Mitsubishi Heavy Industries to Undertake AI-enabled Robot System Project for the Nippon Foundation - DeepStar Joint Research & Development Program
The aim of this project is to develop an automatic inspection system that utilizes robots to identify and predict hazards in offshore facilities. The use of a wide variety of robots to enable unmanned operations and thereby reduce the risk of performing inspections on offshore platforms has long been considered; however, the centralized coordination of individual robots is complex as it requires the management of multiple systems and the data that they acquire. Yokogawa has already been engaged in the research and development of a robot service platform that centralizes the management of multiple robots and seamlessly links them with existing control systems. Leveraging the findings of this R&D, this project will build a communications infrastructure and robot system that is well suited for the environment found on offshore platforms, and utilize an AI application to convert for use in offshore platform operations the image and sound data acquired by robots.
Yokogawa and DOCOMO Successfully Conduct Test of Remote Control Technology Using 5G, Cloud, and AI
Yokogawa Electric Corporation and NTT DOCOMO, INC. announced today that 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.
In a World First, Yokogawa and JSR Use AI to Autonomously Control a Chemical Plant for 35 Consecutive Days
Yokogawa Electric Corporation (TOKYO: 6841) and JSR Corporation (JSR, TOKYO: 4185) announce the successful conclusion of a field test in which AI was used to autonomously run a chemical plant for 35 days, a world first. This test confirmed that reinforcement learning AI can be safely applied in an actual plant, and demonstrated that this technology can control operations that have been beyond the capabilities of existing control methods (PID control/APC) and have up to now necessitated the manual operation of control valves based on the judgements of plant personnel. The initiative described here was selected for the 2020 Projects for the Promotion of Advanced Industrial Safety subsidy program of the Japanese Ministry of Economy, Trade and Industry.
The AI used in this control experiment, the Factorial Kernel Dynamic Policy Programming (FKDPP) protocol, was jointly developed by Yokogawa and the Nara Institute of Science and Technology (NAIST) in 2018, and was recognized at an IEEE International Conference on Automation Science and Engineering as being the first reinforcement learning-based AI in the world that can be utilized in plant management.
Given the numerous complex physical and chemical phenomena that impact operations in actual plants, there are still many situations where veteran operators must step in and exercise control. Even when operations are automated using PID control and APC, highly-experienced operators have to halt automated control and change configuration and output values when, for example, a sudden change occurs in atmospheric temperature due to rainfall or some other weather event. This is a common issue at many companies’ plants. Regarding the transition to industrial autonomy, a very significant challenge has been instituting autonomous control in situations where until now manual intervention has been essential, and doing so with as little effort as possible while also ensuring a high level of safety. The results of this test suggest that this collaboration between Yokogawa and JSR has opened a path forward in resolving this longstanding issue.
A Framework for Ensuring Safe Plant Design and Operation in the Process Industries
The safety of industrial plants is a prerequisite for reassuring local communities and achieving a sustainable society. The process industries operate large, complex man-machine systems and even a single accident in a plant could cause immense damage to facilities, local communities, and the environment, and, in an extreme case, could destabilize the whole of society. To prevent such serious accidents, laws and regulations concerning process safety were discussed globally and the concept of risk reduction with multiple protection layers and a management system through the design and operation of safety instrumented systems was established as a framework for the safety of the process industries. This paper reviews this framework with reference to the trend of related standardization activities and introduces how AI is used to support safety in the process industries.
Ethernet-APL for Evolving Field Devices and the Future of Industrial Ethernet
Communication technologies used in process automation (PA) plants must satisfy several requirements such as long-distance communication and stable operation in hazardous areas. Although 4–20 mA devices and fieldbus devices satisfy these requirements and thus have been used for many years, general-purpose Ethernet technology in PA plants is expected to achieve DX and IT/OT convergence at the field device level. From the viewpoint of standardization, this paper explains how Ethernet Advanced Physical Layer (Ethernet-APL) can meet the requirements of PA plants, shows what benefits Ethernet-APL will bring to PA plants and what challenges are expected to emerge, and describes the prospects and expectations of Yokogawa’s contribution to this field.
5G Wireless Communication for Driving Digitalization in the Process Industry
The fifth-generation mobile communication system (5G) has been developed not only for consumer use but also as a fundamental communication infrastructure for various industries. The importance of wireless technology is becoming increasingly recognized in the process industry; this technology enables devices in the plant field to be connected wherever they are, which is also essential to accelerate digitalization and improve productivity. To drive digitalization using 5G technology, Yokogawa has been actively involved in the standardization of the technology while working on proof-of-concept tests to clarify use cases in the process industry. From the viewpoint of how the process industry utilizes 5G for digitalization, this paper overviews 5G technology, its potential use cases, and challenges in practical use and describes Yokogawa’s commitments.
The Digital Factory framework: An International Standard for Semantic Interoperability
“Smart Manufacturing” is an internationally agreed concept of an ideal state of the manufacturing industry. To achieve this, systems with different architectures must exchange information without compromising its meaning. In other words, systems must not only connect to, but also understand, each other. This crucial requirement is called semantic interoperability. The Digital Factory framework is an international standard that Yokogawa has contributed to its development. Its purpose is to achieve semantic interoperability and thus establish a foundation for Smart Manufacturing. This standard defines the structure of common model elements and their usage rules based on common concept dictionaries and integrates various information of a “system of systems” related to production. When related implementation technologies worldwide comply with this standard, digital information representing production systems (Digital Factories) will be available to all parties throughout the lifecycle of production systems while keeping up-to-date. This paper outlines the Digital Factory framework, the significance of international standardization for Smart Manufacturing, and Yokogawa’s commitment to this effort.
The IEC 62832 Digital Factory framework was developed by IEC TC 65/WG 16 and published in October 2020. It provides the basic structures of model elements needed to digitally represent an entire production system and their usage rules. It consists of the following three parts. ● IEC 62832-1 General principles (Part 1)(9) ● IEC 62832-2 Model elements (Part 2)(10) ● IEC 62832-3 Application of Digital Factory for lifecycle management of production systems (Part 3)(11)
To establish Semantic Interoperability and allow different systems to understand each other, dictionaries that define concepts in an identifiable and understandable way are needed (e.g., IEC 61360-4 - Common Data Dictionary(8)). A method that shares structures for combining the shared concepts and using them as complex information is also needed.
⭐ A Framework for Enhancing the Interoperability of Information across a Plant
Since it is becoming increasingly difficult for a single vendor to meet diversifying user requirements by itself, interoperability among multi-vendor components and control systems such as distributed control systems (DCS) and programmable logic controllers (PLC), has been improved by adopting open industrial communication protocols. However, these protocols, and the information generated, stored, and transferred, are not fully compatible with each other. Accordingly, the open platform communications unified architecture (OPC UA) and related international standards are attracting attention from many vendors and users as a key to high interoperability. This paper introduces how OPC UA improves interoperability among plant components and systems and describes Yokogawa’s prospect.
This paper introduced the trend of FITS and OPC UA FX as standard technologies related to OPC UA. Conventionally, a plant operation system is built by stacking various specialized elements. The system is expected to be integrated vertically and horizontally by industrial-level interoperability standards including OPC UA. As a result, the functional hierarchy will become flat and diverse components and systems will cooperate with each other regardless of the kind of vendors and applications. Yokogawa focuses on the interoperability in the cooperative domain, which was discussed in this paper, and is actively participating in standardization of FITS, OPC UA FX, and IEC/IEEE 60802.
Advancing from industrial automation to industrial autonomous operations
Layered in along the way to autonomous operations, robotics provide an increased amount of functionality for inspection and measurement, system integration and fleet management, and physical operations with arm manipulation capabilities, said Penny Chen, Ph.D, senior principal technology strategist, Yokogawa. Robotic application examples include visual inspection, thermal inspection, auditory inspection, gas detection, object detection and 3D mapping. Yokogawa software aggregates information and platforms to manage many systems, she explained. See Figure 3. The software helps resolve three basic needs: 1) Robot management, 2) data orchestration and 3) integrating with existing control and asset management systems. Security and safety also are very important, she said.
Best practices in IIoT-based predictive maintenance
A key component of the FDT 3.0 standard is the FDT Server built around a core server, which provides a center point for a wide range of client and server interactions. It includes an OPC UA server providing access to device type manager (DTM) data with authenticated OPC UA clients and a web server enabling the use of web user interfaces on remotely connected, browser-based clients and other mobile devices such as smart phones, tablets and PCs. The solution also supports the use of apps that improve workforce productivity and plant availability.
“The latest industry trends center around advanced data analytics, digital twins and cloud computing. The FDT 3.0 standard supports these solutions by delivering network and device information to enable improved diagnostics and predictive analytics. The technology provides a tool to not only monitor and predict asset health, but also remotely configure and manage assets for the highest level of reliability.”
AI in the Process Industry
When applying AI to difficult problems in plants, approaches differ depending on whether AI researchers can access useful information derived from similar problems. This article first discusses how to search and identify useful research and literature. If well established AI research is available, the next step is simply to choose an appropriate AI platform. If not, the most serious bottleneck for the problem-solving task arises: how to integrate plant domain knowledge and AI technology. This article presents a solution to the latter case. This solution enables plant engineers to make full use of AI geared for themselves, not for data scientists. AI-based control, which is one of the promising AI applications for plants and is expected to solve difficult problems in plants, is also discussed.
A Digital Factory Approach to Data-driven Management in Factories
Yokogawa’s solutions and know-how play an important role in accelerating digital transformation (DX) of operational technology (OT) in the manufacturing industry. When proposing these solutions and know-how to customers, it is persuasive to be able to show that Yokogawa has actually improved productivity in its own factories using its OT operations data. This specific example will help customers to understand the effectiveness of the proposal. To achieve data-driven management with OT operation data, three requirements must be satisfied: (1) OT Data Lake, which is a framework for gathering operational data from Yokogawa’s factories worldwide into a single database and improving productivity on a global scale, (2) AI optimization and automation that use operational data and images, and (3) remote operation that ensures the continuity of business even when people’s access is restricted, for example, due to the COVID-19 pandemic. Yokogawa defines a factory that satisfies these three items as a Digital Factory and is working hard to make its own factories as such. Although this approach is one of Yokogawa’s Internal DX measures, the results can be used to develop know-how for External DX, which will increase value for customers, expedite DX in existing businesses, create new DX businesses, and strengthen Yokogawa’s presence in DX. This paper introduces Yokogawa’s approach to Internal DX, its roadmap, and progress toward external DX.
A Platform Based on the Semantic Data Model That Makes Full Use of Design Data throughout the Plant Lifecycle
Design data are created in multiple systems because their purpose and specialty are different. Yokogawa has been developing a plant data transformation platform that checks the consistency among data distributed across various systems and enables the interoperability of the data by applying ontology technology to database operation and management. This platform will make it possible to quickly and reliably resolve data gaps and inconsistencies between the plant design and instrumentation systems, ensure their reliability, and provide high-quality engineering services. This paper describes through the value architecture analysis how this platform technology will also help solve social issues related to the SDGs and explains its core technologies and application examples.
Cooperation between Control Technology and AI Technology to Improve Plant Operation
As the manufacturing industry is shifting its production model from mass production to the production of multiple products in small or variable quantities, more sophisticated operation of production equipment is required. Yokogawa has a unique approach to this problem, which was adopted by the New Energy and Industrial Technology Development Organization (NEDO). This paper describes details of this NEDO project and its achievements, as well as a study on the effective use of AI technology, which is another theme of this project.
In the NEDO project, to create this time-series model, we used effective nonlinear methods: multilayer perceptron (MLP), BiLSTM, and QRNN. As a result, we obtained correlation coefficients greater than 0.7 in the model. To verify whether this time-series model can reproduce the behavior of the target process, we evaluated its accuracy index. In addition, we used the model to solve the optimization problem and automatically calculate the optimal control parameters (PID values).
Application of AI to Oil Refineries and Petrochemical Plants
Artificial intelligent (AI), machine learning, data science, and other advanced technologies have been progressing remarkably, enabling computers to handle labor- and time-consuming tasks that used to be done manually. As big data have become available, it is expected that AI will automatically identify and solve problems in the manufacturing industry. This paper describes how AI can be used in oil refineries and petrochemical plants to solve issues regarding assets and quality.
A Case of Applying AI to an Ethylene Plant
Unexpected equipment failures or maintenance may result in unscheduled plant shutdowns in continuously operating petrochemical plants such as ethylene plants. To avoid this, the operation status needs to be continuously monitored. However, since troubles in plants have various causes, it is difficult for human workers to precisely grasp the plant status and notice the signs of unexpected failures and need for maintenance. To solve this problem, we worked with a customer in an ethylene plant and developed a solution based on AI analysis. Using AI analysis based on customer feedback, we identified several factors from numerous sensor parameters and created an AI model that can grasp the plant status and detect any signs of abnormalities. This paper introduces a case study of AI analysis carried out in an ethylene plant and the new value that AI technology can offer to customers, and then describes how to extend the solution business with AI analysis.
Scalable reinforcement learning for plant-wide control of vinyl acetate monomer process
This paper explores a reinforcement learning (RL) approach that designs automatic control strategies in a large-scale chemical process control scenario as the first step for leveraging an RL method to intelligently control real-world chemical plants. The huge number of units for chemical reactions as well as feeding and recycling the materials of a typical chemical process induces a vast amount of samples and subsequent prohibitive computation complexity in RL for deriving a suitable control policy due to high-dimensional state and action spaces. To tackle this problem, a novel RL algorithm: Factorial Fast-food Dynamic Policy Programming (FFDPP) is proposed. By introducing a factorial framework that efficiently factorizes the action space, Fast-food kernel approximation that alleviates the curse of dimensionality caused by the high dimensionality of state space, into Dynamic Policy Programming (DPP) that achieves stable learning even with insufficient samples. FFDPP is evaluated in a commercial chemical plant simulator for a Vinyl Acetate Monomer (VAM) process. Experimental results demonstrate that without any knowledge of the model, the proposed method successfully learned a stable policy with reasonable computation resources to produce a larger amount of VAM product with comparative performance to a state-of-the-art model-based control.
Enerize E3 Factory Energy Management System
The Enerize E3 factory energy management system is the first system in the industry to succeed in visualizing the energy key performance indicator (KPI). This makes it possible to standardize energy management and encourage all members in a factory to participate in energy-saving activities. The system is built by modeling energy supplying utility equipment, and energy-consuming and production equipment. This paper reports its development and features.
In order to continually practice energy-saving activities that encourage full participation, indicators that serve as criteria are necessary. Yokogawa defines these indicators as energy key performance indicators (KPIs), and proposes using the KPIs for identifying points where energy can be saved and utilizing them as subsequent management criteria.