Industrial 6G-IoT and Machine-Learning-Supported Intelligent Sensing Framework for Indicator Control Strategy in Sewage Treatment Process
Zhiwei Guo, Yu Shen, Chinmay Chakraborty, Fahad Alblehai, Keping Yu
Abstract
In context of 6G mobile computing, the combination of Industrial Internet of Things (IoT) and machine learning extends intelligent sensing ability to improve industrial operation efficiency. In conventional operation of sewage treatment process (STP), manipulators often made excessive aeration amount in treatment process, in order to reach environmental standard. However, such rough operation mode will bring redundant energy consumption. To deal with this issue, this work employs industrial 6G-IoT environment provide basic data conditions for intelligent sensing scheme. On this basis, an industrial 6G-IoT sensing and machine-learning-supported intelligent sensing framework is established for indicator control strategy in STP. In particular, the amount of dissolved oxygen (DO) is selected as the main control object. Then, given inlet conditions and expected outlet conditions, the support vector regression model is formulated to predict the appropriate DO amount values. The proposed approach is evaluated on data collected from a real-world industrial 6G-IoT-based STP. And it is compared with several typical machine-learning-based prediction methods. Numerical results show that the method proposed is 5% better than the typical methods with a deviation of less than 0.6 and can achieved prediction precision about 80%.