Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Reusable Polyacrylonitrile-Sulfur Extractor of Heavy Metal Ions from Wastewater

Mercury, lead, and cadmium are among the most toxic and carcinogenic heavy metal ions (HMIs), posing serious threats to the sustainability of aquatic ecosystems and public health. There is an urgent need to remove these ions from water by a cheap but green process. Traditional methods have insufficient removal efficiency and reusability. Structurally robust, large surface-area adsorbents functionalized with high-selectivity affinity to HMIs are attractive filter materials. Here, an adsorbent prepared by vulcanization of polyacrylonitrile (PAN), a nitrogen-rich polymer, is reported, giving rise to PAN-S nanoparticles with cyclic π-conjugated backbone and electronic conductivity. PAN-S can be coated on ultra-robust melamine (ML) foam by simple dipping and drying. In agreement with hard/soft acid/base theory, N- and S-containing soft Lewis bases have strong binding to Hg²⁺, Pb²⁺, Cu²⁺, and Cd²⁺, with extraordinary capture efficiency and performance stability. Furthermore, the used filters, when collected and electrochemically biased in a recycling bath, can release the HMIs into the bath and electrodeposit on the counter-electrode as metallic Hg⁰, Pb⁰, Cu⁰, and Cd⁰, and the PAN-S@ML filter can then be reused at least 6 times as new. The electronically conductive PAN-S@ML filter can be fabricated cheaply and holds promise for scale-up applications.     

Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney,Melbourne and Brisbane

In recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.     

A novel method of shrimp blanching by CO2 heat pump: Quality,energy, and economy analysis

To reduce the energy consumption of the shrimp blanching process and improve the economic value of the blanched product, a transcritical CO₂ heat pump blanching system (THPB system) was designed in this paper. The trends of astaxanthin were investigated at atmospheric pressure near boiling temperature, combined with the color and structural properties of shrimp samples, and the optimal blanching times of 270 s and 240 s were obtained at 90°C and 95°C, respectively. In contrast to the fuel blanching system (FB system) at 100°C, the annual standard coal consumption of the THPB system with 90°C blanching is decreased by 79%, and the annual operating cost can be saved by CNY 63,800, with a payback period of about 3.13 years.Industrial relevanceBlanching is one of the effective ways to prolong the shelf life of shrimp. However, the research on the blanching time and temperature of shrimp is not comprehensive. In addition, the traditional fuel blanching process has high energy consumption and pollution, and can no longer meet the quality requirements of the modern food processing industry. Heat pump has been shown to have better performance in food drying, but it is less used in blanching. The information presented in this study may provide other insights into food processing.     

Monomer casting nylon-6-b-polyether amine copolymers: Synthesis and properties

MC nylon-6-b-polyether amine copolymers were prepared with macro-initiator based on amino-terminated polyether amine functionalized with isocyanate via in-situ polymerization. It was found that the introduction of polyether amine delayed the polymerization process of caprolactam by increasing apparent activation energy and pre-exponential factor, resulting in the decrease of molecular weight of nylon-6. The motion of molecular chain of the copolymers was easy because of the decreased hydrogen bonds and weakened inter-molecular forces. The physical entanglement of molecular chains of the copolymers was significant and strong which increased the entanglement density. Only the nylon-6 phase crystallized in the copolymers and the crystal grain size, spherulite size and crystallinity of the copolymers decreased. A small amount of γ crystal formed at high polyether amine content. The copolymers presented obvious strain hardening behavior in stress-strain curves and the loss factor dramatically increased while the glass transition temperature and storage module decreased. The fracture surface of the copolymers became rough and presented hairy structure, indicating that the toughening mechanism of the copolymers corresponded to the multi-layer crack extension mechanism.     

Controlling Au electrode patterns for simultaneously monitoring electrical actuation and shape recovery in shape memory polymer

This paper presents an effective approach to achieve efficient electrical actuation and monitoring of shape recovery based on patterned Au electrodes on shape memory polymer (SMP). The electrically responsive shape recovery behavior was characterized and monitored by the evolution change in electrical resistance of patterned Au electrode. Both electrical actuation and temperature distribution in the SMP have been improved by optimizing the Au electrode patterns. The electrically actuated shape recovery behavior and temperature evolution during the actuation were monitored and characterized. The resistance changes could be used to detect beginning/finishing points of the shape recovery. Therefore, the Au electrode not only significantly enhances the electrical actuation performance to achieve a fast electrical actuation, but also enables the resistance signal to detect the free recovery process.     

Value chain analysis of bio-coal business in Finland: Perspectives from multiple value chain members

The purpose of this study is to provide a comprehensive outline of the Value Chain (VC)¹ of bio-coal in Finland, define the proper business models of bio-coal business, and investigate the interrelationships between VC activities. The major findings are: 1) the study found significant interrelationships between support and primary activities in the bio-coal VC which suggest that we should not emphasis only the core value/primary activities of the bio-coal business, but pay more attentions to the “side values”/support activities; 2) The study identified political and environmental factors as the effective drivers, while technology and cost-profit uncertainty as major restrictions for the Finnish bio-coal business. The authors would like to suggest that instead of purely “environmental concerns”, the term “sustainability credentials” should be emphasized, which means value creating and adding items should not limit to monetary values, but consider social and environmental value propositions. 3) The study identified “low competition” in the current bio-coal business in Finland, which implies opportunities of market entrance for Finnish and international companies; 4) The study identified that the current Finnish bio-coal model is the decentralized model at the local level. However, the future Finnish bio-coal model will be a large scaled model, with a focus on export sales.