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

Abstract

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.

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region, China

“…This theory overlaps with many other uncertainty theories, especially Boolean reasoning methods, evidence theory, fuzzy set theory and rough set theory (Yang et al, 2008). Emphasizing the relativity and fuzziness in information processing, the SPA theory can identify the relative certainty and relative uncertainty, and describe them quantitatively and systematically using identity, discrepancy and contrary (Zhao and Xuan, 1996;Ye and Wu, 2000;Wang and Li, 2012;Yan et al, 2017). The SPA theory has been widely applied in many fields, including artificial intelligence, system engineering, multi-attribute assessments, urban ecosystem health assessments, energy consumption forecasting, water resource evaluation, weather forecasting and hazard assessment (Wang et al, 2004;Zhou et al, 2007;Wang et al, 2008;Su et al, 2009;Wang and Li, 2012;Wang et al, 2014;Yan et al, 2017).…”

“…In this study, we set a=S/N, b=F/N and c=P/N. Obviously, a+b+c=1 is undisputed (Wang and Li, 2012;Yan et al, 2017). The connection degree of H is then defined as follows:…”

“…where μ is the connection degree of H; i is the uncertainty coefficient of the discrepancy degree, with an uncertainty value between -1 and 1, which corresponds to various circumstances or can be considered only as a measure of discrepancy; and j is the coefficient of the contrary degree and is specified as -1 (Su et al, 2009;Wang and Li, 2012;Yan et al, 2017). Equation 9is considered to be the three-element connection degree (Wang and Li, 2012;Yan et al, 2017). The multi-element connection degree is defined as follows:…”

“…are components of the discrepancy degree, explaining different grades of discrepancy; and i 1 , i 2 , …, i k-2 are the uncertainty component coefficients of the discrepancy degree (Wang and Li, 2012). The connection degree μ(A, B) may overcome the drawbacks of using a single index, such as the correlation coefficient, subordinate degree or grey correlation degree (Wang and Li, 2012;Yan et al, 2017). The k-element connection degree μ(A, B) is described as follows:…”

“…For the performance assessment of k (k≥2) grades of climate reconstruction, there are k-1 thresholds (s 1 , s 2 , …, s k-1 ) whose intervals (0−s 1 , s 1 −s 2 , …, s k-2 −s k-1 , s k-1 −+∞) correspond to different grades. For example, k=3 means three grades, namely good, normal and bad performance(Wang and Li, 2012;Yan et al, 2017).…”