Processing, June 2020

18 Processing JUNE 2020 instance enables the extraction of features aids in the understanding of the relationship between extracted features and e ciently identi es abnormal factors Even if features can be extracted it is not possible to identify the true cause of an abnormality by just analyzing the relationship between the quality values and the extracted feature To do this it is necessary to analyze the data as the reaction progresses e project team divided the reaction phase into early middle and latter stages identi ed the extracted features for each stage and analyzed the relationship between product quality and the individual extracted features Changes in the extracted features at each stage of the reaction impact product quality Machine learning makes it possible to identify the dominant extracted feature and to verify the certainty of this conclusion Machine learning is also able to reduce the risks of implementing an action plan for product quality stabilization The project team verified the certainty of the extracted features using stepwise regression a statistical analysis method ree explanatory variables that contributed to the reaction including the heating value generated right after the start of reaction were selected from the extracted features According to the estimated result the quality value could be reliably estimated by using these explanatory variables e project team concluded that the quality value could be correctly manipulated by controlling the three extracted features After the factors that had caused fluctuations in product quality were identified the project team developed new operating procedures such as adjusting the additive amount depending on the progress of the reaction as indicated by the extracted features ey also designed a display comparing the current extracted features with past lots e system enables site operators to carry out their duties while monitoring the progress of a reaction It is expected that the new operating procedures for controlling the uctuations in quality with the quality value will reduce costs by several million yen per year Conclusion If the available data set is small the expected benefits of AI are often not obtained This issue can be addressed by using accurate process simulations to generate the data required for learning With these simulations various combinations of input conditions are used to generate thousands or tens of thousands of cases and the machine learning model then learns from the generated data After this step the machine learning model can rapidly carry out tasks such as prediction case study generation and optimization In addition when the machine learning model detects a process abnormality it is possible to use the process simulation to come up with a countermeasure for the abnormality At present extensive human intervention is required to interface with advanced applications often referred to as human driven AI Figure 6 In the future data driven AI will be able to examine data and discover and solve problems Eventually knowledge driven AI will create knowledge by examining one unit or process and then applying these learnings in other situations References 1 Otani Tetsuya Examples of AI utilization in the manufacturing industry the form of collaboration between people and machines Separation Technology Vol 50 No 2 2020 2 Tetsuya Ohtani Digital data improvement Hydrocarbon Engineering Vol 25 No 3 2020 pp 69 74 3 Adaption of an article from Yokogawa Technical Report Vol 63 No 1 2020 Dr Tetsuya Ohtani works in the Advanced Solutions Center at Yokogawa Electric Corporation He holds a bachelor and a masters degree from Osaka University and a doctor of engineering degree from Hosei University Yokogawa www yokogawa com Figure 6 AI roadmap Visit processingmagazine com for more on process control and automation MORE ONLINE