The variation of substrate concentration in anode chamber directly affects the power generation efficiency and decontamination performance of microbial fuel cell (MFC). In this study, three concentrations of swine wastewater with 800 mg/L, 1600 mg/L and 2500 mg/L were selected as substrates, and the performance of MFC and response characteristics of anode microbial community were investigated. The results show that the concentration of a selected substrate is positively correlated with the output voltage of MFC and chemical oxygen demand (COD) removal rate. The microbial community diversity in the anode chamber and the performance of battery can be significantly affected when concentration changes in different ways, which helps to selectively cultivate the adaptable dominant bacteria to enhance the stability and decontamination performance of MFC. The community structure of anodic biofilm is mainly composed of Proteobacteria, Bacteroidetes, Firmicutes, Chloroflexi and Spirochaetae. These findings are meaningful to improve the treatment effects of swine wastewater and can help to find out the mechanism of varying concentration that influences the production of microorganisms in MFC.

Microbial fuel cells (MFCs) have emerged as a sustainable technology for wastewater treatment that has potential to recycle bioelectricity from livestock wastewater. The performance of MFCs is influenced by the synergistic effect of anode material with nearby microorganisms. In this study, three identical double-chambered MFCs with different anode carbon clothes using swine wastewater are established. The optimization mechanism of MFC performance is analyzed by anode characteristics, cell performance, and microbial community, respectively. The results show that the surface structure and properties of the anode carbon cloth can be obviously improved by the acid–heat-modified treatment. The community structure of anodic biofilm, which varied with different modification methods, was mainly dominated by Proteobacteria, Firmicutes, and Bacteroidetes. These findings demonstrate efficient and simple methods for improving the performance of MFCs based on swine wastewater and may help to explore the influence mechanism of different modified anodes on the exoelectrogens.

For complex mechanical transmission equipment, shaft bearings are usually enclosed together with the shaft in the internal space of the housing to maintain good sealing and reliability. However, it is difficult to monitor the status of the shaft bearing through external sensors on the housing, while internal sensors face challenges in energy supply and data transmission. Therefore, a piezoelectric transducer ring-based energy harvesting microelectromechanical system (PTR-EH-MEMS) is proposed for the condition monitoring of shaft bearings. Specifically, the piezoelectric transducer ring is designed to convert mechanical vibrations into electrical energy, which simultaneously acts as a self-powered monitoring sensor through energy harvesting. In addition, the MEMS is embedded for piezoelectric data processing and condition monitoring of the shaft bearings. To verify the proposed PTR-EH-MEMS, an experimental investigation is implemented under different conditions. The experimental results demonstrate that the system can achieve the maximum DC output of 0.8 V and the root mean square power of 43.979 μW within 128 s, which can effectively identify early-stage bearing faults frequency through a self-powered mode. By combining energy harvesting with condition monitoring capability, the PTR-EH-MEMS offers a compact and sustainable approach for predictive maintenance in rotating machinery, reducing the reliance on external power sources and enhancing the reliability of industrial systems.

Pressure pipelines are widely used in the energy and transportation fields for conveying natural gas, water, etc. Under complex and harsh conditions with long-term operation, this easily leads to leakage, threatening the safe and stable operation of transportation systems. Although acoustic sensors support non-destructive leakage detection, their accuracy is restricted by noise interference and minor leakage uncertainties, and existing systems lack a targeted integration design for pipeline scenarios. To address this, the micro-electromechanical system (MEMS) is specifically designed as an MEMS microphone array integrated system (MEMS-MAIS), which is applied for pipeline leakage detection through data fusion at different levels. First, a dedicated MEMS microphone array system is designed to realize high-sensitivity collection of leakage acoustic data. In addition, the integrated feature extraction and feature-level fusion modules are proposed to retain effective information, and a decision-level fusion module is incorporated to improve the reliability of leakage detection results. To verify the designed system, an experiential platform is established with several microphone data. The results indicate that the proposed MEMS-MAIS exhibits excellent anti-interference performance and leakage detection accuracy of 94.67%. It provides a reliable integrated system solution for pipeline leakage detection and verifying high engineering application value.

Pressure pipelines are prone to leakage under harsh working condition for a long time, and the leakage detection reaches unsatisfactory performance due to influence of background noise and insufficient sample for acoustic signals. Therefore, the acoustic signals adversarial augmentation method is proposed for pressure pipeline leakage detection based on noise reduction and sample generation. By deeply connecting with generative adversarial network (GAN), denoising autoencoder (DAE) and residual network (ResNet), the adversarial denoising and generation model (ADGM) is established to reduce the noise of acoustic signal. In addition, the trained DAE of ADGM is applied to augment the acoustic samples, thereby completing adversarial augmentation of acoustic signal, which is significant for pressure pipeline leakage detection. Besides, the pipeline leakage experiment is implemented to validate the proposed method on noise reduction and sample generation, which can reach pressure pipeline detection accuracy of 93.02% based on augmented acoustic signal. Further, the effectiveness and superiority of the proposed method are tested by ablation experiments and comparative methods.

During long-term operation in high-temperature and high-pressure environments, the pressure pipelines of boiler heating systems are prone to damage, which directly affects the safe and stable operation of pressure pipelines and boiler heating systems. Generally, the acoustic sensor is employed to detect the abnormal sound of pressure pipelines for condition monitoring. However, the signals obtained from the acoustic sensor are easily drowned out in background noise generated by fans and exhaust equipment, resulting in unsatisfactory performance for condition monitoring. Therefore, the intelligent acoustic sensor framework is proposed to establish a physics-informed digital twin for pressure pipelines, integrating condition monitoring as a core function. By implementing the digital twin, real-time synchronization between physical and virtual systems enables predictive maintenance, early fault diagnosis, and optimized operational strategies, thereby reducing unplanned downtime and enhancing industrial safety. Specifically, the traditional acoustic sensor system is improved based on the noise reduction model, which can obtain the de-noised acoustic signals for all conditions. Furthermore, the real-time decision-making model for abnormal sound detection is embedded in the proposed intelligent acoustic sensor framework based on the long short-term memory network, and the result is employed as the digital twin for pressures pipeline by monitoring their condition. In addition, the experimental platform is built to test the effectiveness and reliability of the proposed intelligent acoustic sensor framework. The results indicate that the quality of acoustic signals is improved by over 3 dB, and the accuracy of condition monitoring can reach 91.67% for different conditions. By comparing and analyzing with other methods, the superiority and effectiveness of the proposed intelligent acoustic sensor framework are further verified. This approach not only improves monitoring precision but also offers broader social benefits, including reduced energy waste in heating systems and minimized risks of industrial accidents.