In urban water distribution systems, the scientific design of pump group scheduling strategies directly affects both water supply safety and operational cost-effectiveness. However, the operation of pump groups involves coupling conflicts among multiple objectives and complex operational constraints, making it difficult for traditional methods to effectively solve the scheduling optimization problem. Meanwhile, the continuous rise in urban water demand and energy prices introduces new challenges to achieving efficient and energy-saving pump operation. To address this issue, a multi-objective collaborative pump group scheduling model is proposed, fully considering the regulation function of the clear water pool. Based on this, a global–local co-evolutionary hyper-heuristic algorithm is developed. In the intake-supply collaborative optimization model, an adaptive time-division strategy is designed for intake flow planning, which reduces the load on intake pumps by smoothing flow distribution. In the hyper-heuristic optimization algorithm, a hybrid adaptive triggering mechanism is designed to dynamically coordinate the cooperative co-evolution of global optimization and local search. Meanwhile, a multi-armed bandit strategy is employed to adaptively select local search operators online, thereby efficiently identifying the best individuals within promising search regions. Validated on a real-world pump scheduling case, the proposed algorithm achieves superior optimization performance, reducing total energy consumption by 20.02% and decreasing the number of pump switching operations by 44% compared with the on-site scheduling strategy, and it is capable of producing accurate pump scheduling plans within a limited number of iterations. Furthermore, it exhibits superior performance on standard benchmark functions, demonstrating strong applicability.

In industrial soft-sensor applications, labeled samples are often scarce and unable to fully represent the dynamic changes in industrial processes. Although semi-supervised methods offer a potential solution to this issue, existing feature-construction-based methods cannot ensure the effectiveness of the feature, and pseudo-label-based methods lack an established confidence evaluation standard. To address these challenges, this article first proposes a novel performance-driven distillation strategy, which designs an innovative siameseLSTM structure for training multiple teacher models. By assigning higher weights to high-performance teacher models and simultaneously leveraging the guidance of the soft sensing task, the student model is guided to learn more effective feature representations. Additionally, a new pseudo label confidence evaluation strategy is introduced, which aims to enhance the generalization of the base soft-sensor model by selecting samples with high-confidence pseudo labels. Finally, By combining the above two strategies, a semi-supervised soft-sensor framework is proposed for the soft sensing of industrial quality variables. The effectiveness of the proposed framework is validated through two real-world datasets from different stages of the alumina production process. Compared with some existing advanced soft sensor frameworks, the prediction results on different datasets show that the root-mean-square error (RMSE) and mean absolute error (MAE) are reduced by an average of 10.76% and 11.18%, respectively, while the correlation coefficient (R2) is averagely increased by 0.1203.

Traditional industrial production processes, such as nonferrous metallurgy, are mostly based on complex, cascading, large-scale equipment. Many soft sensing approaches are rendered inapplicable due to the particularity of this physical structure, which involves uncertain time delay and extreme imbalance between the input and output dimensions. To alleviate this problem, this article first proposes a time-delay analysis strategy to preliminarily reduce the input dimensions of the process variables. Then, a new orthogonal self-attention (OSA) mechanism is proposed to capture nonlinear features related to quality variables along both spatial and temporal dimensions, thus solving the problem of uncertainty of the time delays of process variables affecting quality variables. In addition, a new long short-term memory (LSTM) structure called differential-cross LSTM is proposed, which is incorporated in a cascading manner differential-cross cascade LSTM (DCCLSTM) to emulate the physical structure of the industrial process. Therefore, the soft-sensor framework called OSA-DCCLSTM is constructed, where data from each major equipment undergo the time-delay analysis strategy and the OSA computation and is subsequently input into the corresponding differential-cross LSTM module. Extensive experiments on a real-world alumina evaporation process datasets show the effectiveness of the proposed framework. Compared with some existing state-of-the-art methods, the root-mean-squared error and mean absolute error are on average decreased by 0.3742 and 0.2234, while the correlation coefficient is on average increased by 0.1389.

The growth of data collection in industrial processes has led to a renewed emphasis on the development of data-driven soft sensors. A key step in building an accurate, reliable soft sensor is feature representation. Deep networks have shown great ability to learn hierarchical data features using unsupervised pretraining and supervised fine-tuning. For typical deep networks like stacked auto-encoder (SAE), the pretraining stage is unsupervised, in which some important information related to quality variables may be discarded. In this article, a new quality-driven regularization (QR) is proposed for deep networks to learn quality-related features from industrial process data. Specifically, a QR-based SAE (QR-SAE) is developed, which changes the loss function to control the weights of the different input variables. By choosing an appropriate inductive bias for the weight matrix, the model provides quality-relevant information for predictive modeling. Finally, the proposed QR-SAE is used to predict the quality of a real industrial hydrocracking process. Comparative experiments show that QR-SAE can extract quality-related features and achieve accurate prediction performance.

In copper removal process control, the commonly used technique is the so-called rule-based control, which is largely dependent upon the operators' experience, likely leading to unstable process production due to each individual's characters and favors. In this paper, to enhance the effectiveness of process control, a controllable-domain-based fuzzy rule extraction strategy is proposed. New definitions of representative controlled samples are introduced, by which the input variable space is divided into several controllable domains by applying positive and unlabeled learning algorithm. Also, the unreasonable removed and the controllable domains are accordingly determined. Then, support vector machine method is employed to extract fuzzy control rules for different domains. Finally, an industrial experiment is presented to demonstrate the effectiveness and advantages of the developed new design scheme.