Kaili Xu

Fuzzy Bayesian Network-Bow-Tie Analysis of Gas Leakage during Biomass Gasification.

Biomass gasification technology has been rapidly developed recently. But fire and poisoning accidents caused by gas leakage restrict the development and promotion of biomass gasification. Therefore, probabilistic safety assessment (PSA) is necessary for biomass gasification system. Subsequently, Bayesian network-bow-tie (BN-bow-tie) analysis was proposed by mapping bow-tie analysis into Bayesian network (BN). Causes of gas leakage and the accidents triggered by gas leakage can be obtained by bow-tie analysis, and BN was used to confirm the critical nodes of accidents by introducing corresponding three importance measures. Meanwhile, certain occurrence probability of failure was needed in PSA. In view of the insufficient failure data of biomass gasification, the occurrence probability of failure which cannot be obtained from standard reliability data sources was confirmed by fuzzy methods based on expert judgment. An improved approach considered expert weighting to aggregate fuzzy numbers included triangular and trapezoidal numbers was proposed, and the occurrence probability of failure was obtained. Finally, safety measures were indicated based on the obtained critical nodes. The theoretical occurrence probabilities in one year of gas leakage and the accidents caused by it were reduced to 1/10.3 of the original values by these safety measures.

A novel hazard assessment method for biomass gasification stations based on extended set pair analysis

Biomass gasification stations are facing many hazard factors, therefore, it is necessary to make hazard assessment for them. In this study, a novel hazard assessment method called extended set pair analysis (ESPA) is proposed based on set pair analysis (SPA). However, the calculation of the connection degree (CD) requires the classification of hazard grades and their corresponding thresholds using SPA for the hazard assessment. In regard to the hazard assessment using ESPA, a novel calculation algorithm of the CD is worked out when hazard grades and their corresponding thresholds are unknown. Then the CD can be converted into Euclidean distance (ED) by a simple and concise calculation, and the hazard of each sample will be ranked based on the value of ED. In this paper, six biomass gasification stations are introduced to make hazard assessment using ESPA and general set pair analysis (GSPA), respectively. By the comparison of hazard assessment results obtained from ESPA and GSPA, the availability and validity of ESPA can be proved in the hazard assessment for biomass gasification stations. Meanwhile, the reasonability of ESPA is also justified by the sensitivity analysis of hazard assessment results obtained by ESPA and GSPA.

Quantitative Investigation on Pyrolytic Behaviors of Agricultural Residues from Rice Husk and Various Performances of Their Relevant Ash Residues

In this study, the thermal process behavior of rice husk as a sustainable feedstock for bio-syngas production and the physicochemical properties of rice husk ash generated from thermal process as possible precursors for utilizations were intensively investigated. A preliminary analysis of the influences of heating rate on kinetics and on the formation of bio-syngas was also performed. The thermogravimetric results of rice husk indicate that its decomposition occurs in four stages, and the main weight loss occurs within 210–400°C. The emission intensities of CO, CO2, CH4, and H2 at 20°C/min are much higher than those of 5°C/min, indicating that pyrolysis at higher heating rates is more suitable for bio-syngas production. Chemical and phase analysis exhibits the presence of cristobalite, quartz, and tridymite as the major crystal phases of SiO2, which reveals the potential of rice husk ash as an alternative for silicon compounds. The porous structure analysis indicates that carbon residues developed from rice husk ash are good candidates for activated carbon production. Through analyzing its thermal properties, the internal reactions occurring within ash particles are predicted accurately, which is helpful to better understand the transformation and fusion mechanism of inorganic components in the complex-composition ash.