Hitachi Launches AI-Assisted Predictive Maintenance Service for Petrochemical Plants

Oct 04, 2018 12:29 JST

Source: Hitachi, Ltd.

Commercialized after Joint Demonstration Experiment at Showa Denko's Ethylene Plant
to Help Improve Efficiency in the Operation and Maintenance of the Whole Plant

TOKYO, Oct 04, 2018 - (JCN Newswire) - Hitachi, Ltd. (TSE: 6501) has now started offering the predictive maintenance service for petrochemical plants. It makes use of artificial intelligence (AI) to automatically classify and analyze the petrochemical plant's operation status in preparation for the real-time detection of changes in conditions and abnormalities that may be signs of failure. The service collects plant operation data and analyzes them by means of the adaptive resonance theory (ART), one of AI-based data clustering technologies, to swiftly detect abnormalities in devices and equipment that constitute the plant arising from multiple causes that are difficult to be discovered by predictive maintenance systems based on ordinary prediction models or by human judgment. It will help reduce not only the workload of operators monitoring the plant's operation but also the failure occurrence ratio to increase efficiency in operation and maintenance.

Left: Exterior view of the decomposition furnace in the ethylene plant of Showa Denko Right: Screen of the monitoring system

Detection of abnormalities by the predictive maintenance service for petrochemical plants

This predictive maintenance service has been developed and commercialized through a demonstrative trial conducted by Hitachi in collaboration with Showa Denko K.K. at the ethylene plant in Showa Denko's Oita Complex. It came into service for the practical operation of the plant in October 2018.

Oil refineries, petrochemical plants and other plants require the constant maintenance of safe and stable operation. It is common for skilled operators to check the data from the monitoring and control system and carry out on-site patrols to perform visual inspections in order to grasp the operation status of individual devices and equipment for the purpose of monitoring plant operation. However, even when individual devices and equipment in the plant are in normal operation, multiple factors such as the flow rate, pressure and temperature may bring the plant to a halt. The plant operation monitoring is therefore dependent on the judgments of skilled operators based on their experience and expertise. Meanwhile, the shrinkage of the working population means that it is becoming increasingly difficult to secure skilled operators. It is therefore necessary to increase operation and maintenance efficiency with the aim of reducing the staff workload, and to resolve this issue with the use of digital technologies.

Under these circumstances, Hitachi has developed a predictive maintenance technology for plants. With the active use of the AI technology of Lumada, solutions for digital innovation, as well as its abundance of expertise in operational technology (OT) that plant design and process control for energy and chemical field, this technology evaluates signs of changes in conditions and abnormalities in the entire petrochemical plant on a real-time basis. It was confirmed that this technology analyzed conditions for the occurrence of coking(1) as a possible cause of plant failure, which used to be difficult to determine, and successfully detected signs of coking independently from skilled operators when it was applied to the ethylene plant in Showa Denko's Oita Complex.

Accordingly, Hitachi has begun offering this technology that reflected the result of demonstration as predictive maintenance service for petrochemical plants. At Showa Denko, this service will commence full operation for the practical operation of the ethylene plant where the joint demonstrative trial was conducted. To prevent plant failure, Showa Denko will work toward developing operation methods that align with the state of coking and methods of inhibiting coking. In addition, Showa Denko will introduce it to other manufacturing processes and plants.

The introduction of this service allows petrochemical plant manufacturers to transform conventional operation monitoring based on the experience and expertise of skilled operators into more efficient and advanced monitoring based on detection data. Since it detects signs of abnormalities that are overlooked by humans, it helps lower the plant failure ratio as well as maintenance costs.

In the future, Hitachi will offer this service broadly to Japanese and overseas markets as a solution based on Lumada, and support the safe, stable operation of petrochemical plants.

Detection of abnormalities by the predictive maintenance service for petrochemical plants
http://www.acnnewswire.com/topimg/Low_HitachiAIAssistedPMS2.jpg

This service incorporates one of Lumada's AI technologies, an analysis engine by ART. After advance learning of data on the past normal operation of plant devices and equipment, such as temperature, pressure, water level and flow rate, it classifies correlations of data that serve as yardsticks for evaluating possible signs and automatically creates a category of normal data. It then automatically classifies new data obtained from actual plant operation and compares them with the category of normal data to determine whether the operation is normal. In the event of the generation of a new data category, it issues an alert to the operators to urge them to make a decision. The operators judge whether the plant is in a normal state by the plant operation status and the factors involving the difference from the category of normal data (such as temperature and pressure). They can improve the accuracy of this service's future evaluation by having this service learn their decisions. In addition, it visualizes not only abnormalities of individual devices and equipment constituting the plant, but also their spread to other devices and equipment in the plant. It therefore enables them to be understood to help swiftly restore the normal state of operation. The analysis engine by ART has been developed on the basis of technologies and knowledge that have been cultivated for years by Hitachi's Research & Development Group with a view to its application to thermal power generation, water treatment and other large-sized plants and through demonstration trials at plants.

(1) Coking is adhesion in piping and elsewhere of coke, which is a solid substance generated from the decomposition of naphtha, a type of poor quality gasoline.

Contact:

Hitachi Ltd
Corporate Communications
Tel: +81-3-3258-1111

Source: Hitachi, Ltd.
Sectors: Cloud & Enterprise

Related Press Release

Hitachi Energy and SP Energy Networks to boost renewable energy flow

April 05 2024 16:15 JST

Hitachi Selected as "Excellent TCFD Disclosure" by GPIF's External Asset Managers for the Third Consecutive Year

April 05 2024 16:00 JST

JFE Steel and Hitachi Jointly Started Providing Solutions for the Steel Industry

March 26 2024 18:04 JST

Hitachi Selected as CDP Supplier Engagement Leader for the Third Consecutive Year

March 22 2024 15:04 JST

Hitachi Collaborates with NVIDIA to Accelerate Digital Transformation with Generative AI

March 19 2024 18:11 JST

Hitachi Vantara Announces Collaboration with NVIDIA to Create New Portfolio of Industrial AI Solutions

March 19 2024 11:35 JST

Hitachi High-Tech Launches High-sensitivity and High-throughput Wafer Surface Inspection System LS9300AD for Wafer Manufacturers

March 15 2024 10:21 JST

Hitachi Forges a Three Year Strategic Alliance with AWS to Advance Hybrid Cloud Solutions

March 11 2024 15:44 JST

Hitachi Energy and TransnetBW make German grid fit for future

February 15 2024 10:22 JST

Contributing to Efficient Development of Metal Thin Film Materials by Using Chemicals Informatics and Materials Informatics

February 09 2024 12:41 JST

More Press release >>

Latest Press Release

First-ever Mazda CX-80 Crossover SUV Unveiled in Europe
Apr 19, 2024 13:50 JST

Fujitsu develops technology to convert corporate digital identity credentials, enabling participation of non-European companies in European data spaces
Apr 19, 2024 10:17 JST

Mitsubishi Heavy Industries and NGK to Jointly Develop Hydrogen Purification System from Ammonia Cracking Gas
Apr 18, 2024 17:01 JST

Toyota Launches All-New Land Cruiser "250" Series in Japan
Apr 18, 2024 13:39 JST

Fujitsu and Oracle collaborate to deliver sovereign cloud and AI capabilities in Japan
Apr 18, 2024 11:14 JST

Eisai: Research on Treatments for Alzheimer's Disease Based on Its Pathological Mechanisms Recieves Award for Science and Technology (Research Category)
Apr 18, 2024 10:53 JST

All-New Triton Confirmed as First Double-Cab Pickup Truck to Achieve 2024 Five-Star ANCAP Safety Rating
Apr 18, 2024 09:22 JST

Eisai's Antiepileptic Drug Fycompa Injection Formulation Launched In Japan
Apr 17, 2024 16:17 JST

Honda Unveils Next-generation EV Series for China
Apr 17, 2024 12:15 JST

Lexus presents Time at the 2024 Milan Design Week
Apr 16, 2024 18:49 JST

Mitsubishi Corporation Announces Participation in a DAC Project in Louisiana, USA
Apr 16, 2024 14:36 JST

New circuit challenge for TOYOTA GAZOO Racing
Apr 15, 2024 17:21 JST

TOYOTA GAZOO Racing back on asphalt for Croatian challenge
Apr 12, 2024 19:36 JST

Heidelberg Materials North America Announces Latest Milestone in Edmonton CCUS Project
Apr 12, 2024 14:39 JST

MHIAEL Completes Expansion of the its Nagasaki Plant for Manufacture of Aero Engine Combustors
Apr 11, 2024 18:08 JST

Mitsubishi Shipbuilding Acquires Approval in Principle (AiP) from Classification Society ClassNK for Ammonia Fuel Supply System (AFSS)
Apr 11, 2024 17:50 JST

DOCOMO, NTT, NEC and Fujitsu Develop Top-level Sub-terahertz 6G Device Capable of Ultra-high-speed 100 Gbps Transmission
Apr 11, 2024 15:10 JST

Mitsubishi Corporation Announces Completion of Capital Raise by Nexamp
Apr 11, 2024 13:07 JST

Mitsubishi Shipbuilding Receives Order for Ammonia Fuel Supply System for Ammonia-Powered Marine Engine
Apr 10, 2024 16:55 JST

Transgene and NEC Present First Clinical Benefits of Neoantigen Cancer Vaccine, TG4050, in Head & Neck Cancer at AACR 2024
Apr 10, 2024 13:36 JST

More Latest Release >>