AI-Supported Workflows for Chemical Batch Plants

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article number
03654_2020_08_04
Optimizing quality, efficiency and safety
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This article presents an AI supported solution for cross-application workflows in batch plants. Based on a digital virtual assistant, industrial digital services are connected in workflows and support users in making the best use of the digital infrastructure. We focus on two digital services which allow for early detection of faults within the production and root cause analysis: a novel approach to online identification of batch failures building on an adjusted form of multiway principal component analysis and a low-cost sensing approach to perform root cause analysis for different failure modes occurring in batch plants. A case study of the implementation in a test plant is reported together with insights into the benefits and limitations of the approach gained via several process executions.
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Authors Martin Hollender, Benedikt Schmidt, Arzam Kotriwala, ABB Corporate Research Center Germany
Publishing Date 12 Aug 2020
Format PDF
Publisher Vulkan-Verlag GmbH
Language German
Pages 9
Title AI-Supported Workflows for Chemical Batch Plants
Subtitle Optimizing quality, efficiency and safety
Description This article presents an AI supported solution for cross-application workflows in batch plants. Based on a digital virtual assistant, industrial digital services are connected in workflows and support users in making the best use of the digital infrastructure. We focus on two digital services which allow for early detection of faults within the production and root cause analysis: a novel approach to online identification of batch failures building on an adjusted form of multiway principal component analysis and a low-cost sensing approach to perform root cause analysis for different failure modes occurring in batch plants. A case study of the implementation in a test plant is reported together with insights into the benefits and limitations of the approach gained via several process executions.
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