Dynamic economic and environmental load dispatch (DEED) aims to determine the amount of electricity generated from power plants during the planning period to meet load demand while minimizing energy consumption costs and environmental pollution emission indicators subject to the operation requirements. This planning problem is usually expressed using a nonsmooth cost function, taking into account various equality and inequality constraints such as valve-point effects, operational limits, power balance, and ramp rate limits. This paper presents DEED models developed for a system consisting of thermal units, wind power generators, photovoltaic (PV) generators, and energy storage (ES). A selection hyper-heuristic algorithm is proposed to solve the problems. Three heuristic mutation operators formed a low-level operator pool to direct search the solution space of DEED. The high level of SHHA evaluates the performances of the low-level operators and dynamically adjusts the chosen probability of each operator. Simulation experiments were carried out on four systems of different types or sizes. The results verified the effectiveness of the proposed method.

In the context of Pharma 4.0, i.e., the pharmaceutical version of Industry 4.0, pharmaceutical quality control (PQC) for a pharmaceutical cyber-physical system (PCPS) plays a critical role in ensuring the quality of drug products during pharmaceutical development. However, drug customization through Pharma 4.0 also introduces uncertainty embodied in ever-changing critical material attributes, which presents new challenges related to development costs and efficiency in PQC compared to traditional control modes. Although we have proposed a data-driven methodology to tackle these challenges, it ignores some visible or potential process knowledge that also contains much additional information reflecting the laws and trends governing pharmaceutical process operations. This not only sacrifices the opportunity to use this knowledge to make up for insufficiencies in the information provided by the data but also goes against the core ideology of future intelligent manufacturing. In this article, we introduce the idea of a data- and knowledge-driven approach into PQC for the first time by proposing a general data- and knowledge-driven adaptive PQC framework for a PCPS-based two phases—PQC by direct data- and knowledge-driven adaptive iterative learning control and PQC by learning from primitive data and knowledge. Next, a case study is presented to preliminarily investigate the application of the proposed framework in a simulated pharmaceutical spray fluidized bed granulation process. Finally, a series of simulation experiments are designed to verify the feasibility and effectiveness of the proposed framework.

The dynamic economic dispatch (DED) that considers valve-point effects is a complex nonconvex and non-smooth optimization problem in power systems. Over the past few decades, multiple approaches have been developed to solve this problem. However, many of these approaches have high complexity which is not easily implemented. In contrast to most research efforts, which focus on enhancing the performance of a specific optimization algorithm, this paper presents an efficient overlapped decomposition optimization (ODO) method to solve DED with valve-point effects, by converting DED into a few easily solvable sub-problems. We first exploit the characteristics of the problem itself and present a decomposition strategy. The analysis shows that this strategy can be adopted as an effective local search operator to further improve the optimal solutions. Furthermore, an overlapped decomposition optimization method is proposed in which the study period of sub-problems has overlap. The proposed method efficiently reduces the difficulty of solving DED, so that high-quality solutions can be obtained by combining ODO with some ordinary heuristic-based algorithms. The effectiveness of the proposed method is demonstrated on multiple DED problems. The simulation results indicate that the proposed method can be used as a powerful optimizer for coupled spatial-temporal scheduling problems in power systems.

Dynamic economic dispatch (DED) that considers valve-point effects is a complex nonconvex and nonsmooth optimization problem in power systems. Over the past few decades, multiple approaches have been developed to solve this problem. In this paper, an adaptive differential evolution based on simulated annealing algorithm is proposed to solve the DED problem with valve-point effects. Simulated annealing (SA) algorithm is employed to carry out an adaptive selection mechanism in which the mutation operators of differential evolution (DE) are selected adaptively based on their historical performance. A mutation operator pool consisting of five operators is built to make each operator show its strength at different stages of the evolutionary process. Moreover, a heuristic strategy is introduced to transform infeasible solutions towards feasible ones to enhance the convergence rate of the proposed algorithm. The effectiveness of the proposed methods is demonstrated first on 10 popular benchmark functions with 100 dimensions, in comparison with the classic DE and five variants. Then, it is used to solve four DED problems with 10, 15, 30, and 54 units, which consider the valve-point effects, transmission loss, and prohibited operating zones. The simulation results are compared with those of state-of-the-art algorithms to clarify the significance of the proposed method and verify its performance. Three systems with 100-500 generators are also tested to confirm the advantages of the proposed method on large-scale DED problem.

Vacuum packaging effectively prevents food contamination, facilitates portability and storage. Visual detection technology can rapidly identify packaging issues, ensure food quality, and is a crucial method to address food safety concerns. However, due to the extensive semantic interference and imbalanced distribution of defects in packaging images, defect detection faces significant challenges. In this article, a semantic-adaptive multi-task defect detection approach for vacuum packaging is proposed to handle these problems. First, a vacuum packaging dataset is created by capturing packaging samples on the production line using an industrial camera. Next, addressing the issue of semantic interference, a novel adaptive region partitioning method, integrating U-net and image processing techniques, is proposed to enhance the identification of defects across different regions. Finally, considering the imbalance in defect distribution, the Disentangled Representations of Abnormalities (DRA) model with diverse modes is employed for defect detection tasks across multiple regions in this paper. The introduction of a Convolutional Block Attention Module (CBAM) in the DRA network enhances the capability to detect authentic defects. Experimental results shows that the method achieves an AUC of 99.47% and an accuracy of 97.82% on the constructed dataset, showcasing significant improvement over alternative methods. In conclusion, this study not only enhances the defect detection capabilities of food vacuum packaging but also provides solutions to common issues such as semantic interference and imbalanced distribution of defects.