Search

Developing a fault detection model for the wastewater treatment process that combines satisfactory accuracy with comparatively low time overhead remains an exceedingly formidable endeavor. Fortunately, the broad slow feature neural network (BSFNN) perfectly embodies the dual advantages mentioned above. The BSFNN utilizes both slow feature windows and enhancement windows to extract significant and slowly varying information characterized by different velocities, which facilitates the learning of nonlinear and dynamic features related to superior monitoring accuracy. Another benefit of the BSFNN model is that it continues to retain the efficiency of the broad learning system with regard to time overhead, which is considerably decreased through employing the pseudoinverse strategy to determine network parameters. The operational environment often undergoes nonstationary dynamic changes in actual wastewater treatment processes. Especially in scenarios where higher monitoring accuracy is demanded or the network structure needs online adjustments to real-time update, and yet network adjustments can be time-consuming, the number of node parameters within incremental windows can be flexibly determined by dynamically adding enhancement nodes, which better obviates the necessity of retraining the entire BSFNN system from scratch, thereby allowing for online real-time adjustments to the structure and fault detection accuracy to achieve the desired performance. A case study using benchmark wastewater treatment platforms demonstrates that the suggested method outperforms advanced fault detection methods.

In industrial processes, quality variables are typically sampled at a considerably lower frequency than system inputs due to technical or cost constraints. Dynamic soft sensors utilize temporal prediction to bridge these sampling gaps, thus enabling real-time closed-loop control. However, existing approaches primarily focus on one-step prediction accuracy, potentially leading to significant deviations in long-term predictions. In addition, these methods are incapable of evaluating the reliability of prediction results, subsequently increasing the potential risk of closed-loop systems. To tackle these challenges, this study presents a novel regression modeling approach based on the conditional variational autoencoder (CVAE) framework. In contrast to traditional regression approaches, this method focuses on modeling the transition probability distribution of the system, allowing the model to produce a range of credible quality variable predictions via Monte Carlo (MC) sampling. Based on the CVAEs, the sequential MC method is further employed to simulate diverse potential system state trajectories, thereby achieving multistep soft measurement prediction. Compared with traditional soft measurement techniques, the proposed method demonstrates lower prediction biases and the capacity to assess the credibility of prediction results from a probabilistic standpoint. When online quality variables are assessed by the laboratory, this method can update predictions utilizing the resampling scheme. Two case studies are offered to validate the effectiveness of the proposed scheme.