Transfer Learning in wastewater treatment plants control: Measuring the transfer suitability. (April 2023) - Pisa, Ivan; Morell, Antoni; Vicario, Jose Lopez; Vilanova, Ramon
Abstract: The industrial sector is nowadays experiencing a digital transformation motivated by the Industry 4.0 paradigm. Concepts such as data-driven models, Artificial Neural Networks (ANNs), and Transfer Learning (TL) are part of the current vocabulary in the industrial management and control topics. For that reason, in this paper the application of TL techniques is proposed to derive new ANN-based control structures from pre-existing ones. Notice that if an ANN-based controller is transferred into a new industrial environment, its appropriate behaviour must be ensured, and what it is more important, this must be known a priori . Nevertheless, TL techniques do not always ensure this. That is why the Transfer Suitability Metric (TSM) is proposed here. Determining the similarity among environments, this metric tells if the controller can be transferred, transferred with certain limitations, or if it cannot be transferred at all. Here, the metric is applied over a Wastewater Treatment Plant (WWTP). The objective is to derive the control structure of one control loop, let us say the Dissolved Oxygen (DO), and then transfer it into another basic control loop in a WWTP, the Nitrate–nitrogen (NO), and vice-versa. Results show that with the help of the TSM, an improvement around a 68.54% and 80.53% in the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE) is obtained in the NO management, respectively. Moreover, a simplification and speed-up of the controller design process is achieved. Highlights: Reduce the complexity and speed-up the design process of ANN-based controllers. Transfer ANN-based structures between industrial environments. Measure the transfer suitability of an ANN-based controller. Measure the transfer suitability by means of the Transfer Suitability Metric (TSM).