• Journal of Korean Society on Water Environment
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• v.18 no.3
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• pp.303-312
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• 2002

Polly-pigs technique was developed to remove internal rusts and scales from service pipes in the building by using Polly-pigs that were used as various applications in conventional plant pipelines. Results of cleaning experiments on 15mm GSP(Galvanic Steel Pipe) showed that hydraulic cross sectional area was more increased from 3.5% to 15.4% for straight part or elbow parts of several test pieces. Results of optical analysis also showed that rust and scale removed mostly by KDP series(only consisted of Polyurethane) were outside red colored scale ($Fe_2O_3{\cdot}H_2O$), and there was a limitation to the removal of black colored scale($Fe_2O{\cdot}nH_2O$) that was below red colored scale. But it was evaluated that KDPS series coated fine sand with KDP series could remove not only red colored scale but also black colored scale and more increase hydraulic cross sectional areas of 15mm GSP old service pipes from 13.0% to 17.9%. After KDPS series cleaning, hydraulic cross sectional areas of them were recovered from 95.9% to 99.5%. Turning force of Polly-pigs was largely improved by the effect of Helical guide vane(Cleaning v/v) and Rotating wing(Pigs), and the number of pig rotating also more increased sixteen times compared with conventional cleaning system without Helical guide vane and Rotating wing. After KDPC series cleaning of 100mm GSP old service pipes that hydraulic cross sectional areas were 90%, hydraulic cross sectional areas were almost recovered perfectly like new service pipes. Additionally pressure necessary to run Polly-pigs m 100mm GSP was lower and cleaning efficiency also was higher than 15mm GSP cleaning. Therefore it was thought that as the diameter of pipe was more increased, pressure necessary to clean service pipes was more decreased and cleaning efficiency was more increased in Polly-pigs cleaning.

• Journal of the Korean Institute of Gas
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• v.27 no.2
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• pp.49-56
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• 2023

As AI (Artificial Intelligence)-related technologies are highly developed due to the 4th industrial revolution, cases of applying AI in various fields are increasing. The main reason is that there are practical limits to direct processing and analysis of exponentially increasing data as information and communication technology develops, and the risk of human error can be reduced by applying new technologies. In this study, after collecting the data received from the 'remote potential measurement terminal (T/B, Test Box)' and the output of the 'remote rectifier' at that time, AI was trained. AI learning data was obtained through data augmentation through regression analysis of the initially collected data, and the learning model applied the value-based Q-Learning model among deep reinforcement learning (DRL) algorithms. did The AI that has completed data learning is put into the actual city gas supply area, and based on the received remote T/B data, it is verified that the AI responds appropriately, and through this, AI can be used as a suitable means for electricity management in the future. want to verify.