Reliability Analysis of HHV Prediction Models for Organic Materials Using Bond Dissociation Energies.

The purpose of this study is to analyze the reliability of predictive models for higher heating values related to organic materials. A theoretical model was developed, which utilizes bond dissociation energies (BDEs) to establish correlations between elemental composition and calorific values. Our analysis indicates that the energy contribution of one mole of hydrogen atoms is approximately equal to -144.4 kJ mol-1. Further investigation reveals significant variations in the bond dissociation energies of carbon atoms within organic compounds, resulting in a range of energy outputs from -414.30 to -275.34 kJ mol-1 per mole of carbon atoms. The presence of oxygen atoms in organic compounds has a negative impact on the magnitude of combustion heat, with values ranging from 131.1 to 207.17 kJ mol-1. The combustion mechanism imposes certain constraints, leading to the equation HHVg = -31.34·[C] - 144.44·[H] + 10.57·[O] for organic compounds. Based on the parameter sensitivity analysis, the coefficient associated with carbon mass fraction exhibits a significantly greater impact on result prediction accuracy, demonstrating a sensitivity value of 92.65%. The results of further analysis indicate that empirical correlations involving the mass fractions of the elements N and S in lignocellulosic materials may be prone to over-fitting, with sensitivity indices of 1.59% and 0.016%, respectively.

Fault Detection and Isolation Methods in Subsea Observation Networks.

Subsea observation networks have gradually become the main means of deep-sea exploration. The reliability of the observation network is greatly affected by the severe undersea conditions. This study mainly focuses on theoretical research and the experimental platform verification of high-impedance and open-circuit fault detection for an underwater observation network. With the aid of deep learning, we perform the fault detection and prediction of the network operation. For the high-impedance and open-circuit fault detection of submarine cables, the entire system is modeled and simulated, and the voltage and current values of the operating nodes under different fault types are collected. Numerous calibrated data samples are supervised by a deep learning algorithm, and a fault location system model is built in the laboratory to verify the feasibility and superiority of the scheme. This paper also studies the fault isolation of the observation network, focusing on the communication protocol and the design of the fault isolation system. Experimental results verify the effectiveness of the proposed algorithm for the location and prediction of high-impedance and open-circuit faults, and the feasibility of the fault isolation system has also been verified. Moreover, the proposed methods greatly improve the reliability of undersea observation network systems.