7 research outputs found
Optimization of decision support technology for offshore oil condition monitoring with carbon neutrality as the goal in the enterprise development process
No full textThis study aims to explore the integration of the Faster R-CNN (Region-based Convolutional Neural Network) algorithm from deep learning into the MobileNet v2 architecture, within the context of enterprises aiming for carbon neutrality in their development process. The experiment develops a marine oil condition monitoring and classification model based on the fusion of MobileNet v2 and Faster R-CNN algorithms. This model utilizes the MobileNet v2 network to extract rich feature information from input images and combines the Faster R-CNN algorithm to rapidly and accurately generate candidate regions for oil condition monitoring, followed by detailed feature fusion and classification of these regions. The performance of the model is evaluated through experimental assessments. The results demonstrate that the average loss value of the proposed model is approximately 0.45. Moreover, the recognition accuracy of the model for oil condition on the training and testing sets reaches 90.51% and 93.08%, respectively, while the accuracy of other algorithms remains below 90%. Thus, the model constructed in this study exhibits excellent performance in terms of loss value and recognition accuracy, providing reliable technical support for offshore oil monitoring and contributing to the promotion of sustainable utilization and conservation of marine resources
Experimental investigation on gasification of food waste in supercritical water: Effects of NaCl on syngas production and corrosion of reactor
No full textAn advance for removing antinutritional protease inhibitors: Soybean whey purification of Bowman-Birk chymotrypsin inhibitor by combination of two oppositely charged polysaccharides
No full textCu-BTC-based composite adsorbents for selective adsorption of CO2 from syngas
No full textHigh CO2 content exists in the raw syngas produced from supercritical water gasification of food waste, CO2 removal to improve quality of the syngas is necessary before further utilization. In this study, Cu-BTC, Mg/Cu-BTC, Cu-BTC@MWCNT, and Mg/Cu-BTC@MWCNT were synthesized and characterized as CO2 adsorbents. The adsorption experiments were conducted to investigate the optimal adsorbent and adsorption conditions. The results indicated that Mg2+ and multi-walled carbon nanotubes (MWCNT) doped into Cu-BTC influenced the physical properties of adsorbents. Among of all studied adsorbents, Mg/Cu-BTC@MWCNT had the maximum CO2 adsorption capacity (3.63 mmol/g) and selectivity (14.28) at 25 °C and 100 kPa. Analysis of variance (ANOVA) exhibited that adsorption pressure had significant effects on CO2 adsorption capacity of Mg/Cu-BTC@MWCNT. The influences of different operating parameters on CO2 adsorption capacity and selectivity were discussed in Mg/Cu-BTC@MWCNT. The adsorption isotherms of gas components on Mg/Cu-BTC@MWCNT can be sorted as follows: CO2 > CO > CH4 > H2. In additional, the regeneration experiment with 5 times was conducted and the result showed that Mg/Cu-BTC@MWCNT has high stability.Post-print / Final draf
The catalytic activity of Ca/Fe-rich incineration ash in the pyrolysis of epidemic wood
No full textThe spread of pine nematode disease caused by epidemic wood poses a great challenge to the environment, and there is an urgent need to develop effective processing methods; however, Ca/Fe-rich sludge ash can improve the pyrolysis properties of biomass. Therefore, this paper focuses on the pyrolysis mechanism of epidemic wood with the addition of Ca/Fe-rich sludge ash. The presence of Ca-rich sludge ash was found to extend the pyrolytic temperature window of epidemic wood, intensify the cracking of its volatile constituents, and extend its reaction duration. At the same time, the Ca-rich sludge reduces the pyrolysis activation energy to 152.39 kJ/mol. The Fe-rich sludge ash demonstrated the capacity to lower the energy barriers during the initial phase of pyrolysis. Concurrently, the Ca-rich sludge ash accelerated the dehydration reaction of the epidemic wood, leading to 21.02% and 30.69% increases in the contents of acids and ketones in the pyrolytic oil, respectively. The Fe-rich sludge ash contributed to a notable 14.52% increase in aromatic compounds in the oil and a 19.14% decrease in alcoholic compounds. Additionally, the Ca-rich sludge ash accelerated the decomposition of lipid organic matter at elevated temperatures, enriching the pyrolytic char with more unsaturated bonds. This research lays a theoretical foundation for the safe and efficacious thermal decomposition of epidemic wood, thereby enhancing its utilization within the forestry industry.</p
Low-temperature sintering behavior of fly ash from hazardous waste incinerator: Effect of temperature and oxygen on ash properties
No full textOptimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process.
No full textThis study aims to explore the integration of the Faster R-CNN (Region-based Convolutional Neural Network) algorithm from deep learning into the MobileNet v2 architecture, within the context of enterprises aiming for carbon neutrality in their development process. The experiment develops a marine oil condition monitoring and classification model based on the fusion of MobileNet v2 and Faster R-CNN algorithms. This model utilizes the MobileNet v2 network to extract rich feature information from input images and combines the Faster R-CNN algorithm to rapidly and accurately generate candidate regions for oil condition monitoring, followed by detailed feature fusion and classification of these regions. The performance of the model is evaluated through experimental assessments. The results demonstrate that the average loss value of the proposed model is approximately 0.45. Moreover, the recognition accuracy of the model for oil condition on the training and testing sets reaches 90.51% and 93.08%, respectively, while the accuracy of other algorithms remains below 90%. Thus, the model constructed in this study exhibits excellent performance in terms of loss value and recognition accuracy, providing reliable technical support for offshore oil monitoring and contributing to the promotion of sustainable utilization and conservation of marine resources
