Bayesian-LOPA Methodology Development for LNG Industry

Transcription

1 OUTLINE OF RESEARCH Bayesan-LOPA Methodology Development or LNG Industry Geun Woong Yun, Texas A&M Unversty In order to meet the ast growng LNG (Lqueed Natural Gas) demand, many LNG mportaton termnals are operatng currently. Thus, t s mportant to estmate the potental rsks n LNG termnals wth LOPA (Layer o Protecton Analyss) whch can provde quanted results wth less tme and eorts than other methods. For LOPA applcaton, alure data are essental to compute rsk requences. However, the alure data rom the LNG ndustry are very sparse and have statstcally shake grounds. Thereore, Bayesan estmaton, whch can update the generc data wth plant specc data, was used to compensate or ts weaknesses. Based on Bayesan estmaton, the requences o ntatng events were obtaned usng a conjugate gamma dstrbuton such as OREDA (Oshore Relablty Data) and Posson lkelhood dstrbuton. I there s no normaton, Jereys nonnormatve may be used. The LNG plant alure database was used as plant specc normaton. The PFDs (Probablty o Falure on Demand) o IPLs (Independent Protecton Layers) were estmated wth the conjugate beta such as EIReDA (European Industry Relablty Data Bank) and bnomal dstrbuton. In some cases, EIReDA dd not provde alure data, so the newly developed Frequency-PFD converson method was used nstead. By the combnaton o Bayesan estmaton and LOPA procedures, the Bayesan-LOPA methodology was developed and was appled to an LNG termnal. The ound rsk values were compared to the tolerable rsk crtera to make rsk decsons and compared to each other to make a rsk rankng. The Bayesan- LOPA methodology can be used n other ndustres. Furthermore, t can be used wth other requency assessment methods such as Fault Tree Analyss (FTA) and Event Tree Analyss (ETA) to strengthen ther results.

2 LNG (Lqueed Natural Gas) s one o the astest growng energy sources n the U.S. to ulll the ncreasng energy demands and dversy the energy portolo. In order to meet the LNG demand, many LNG acltes ncludng LNG mportaton termnals are operatng currently. Moreover, there are many proposed projects or LNG termnals to ll the gap between supply and demand o LNG n North Amerca. Thereore, t s mportant to control and estmate the potental rsks n LNG termnals to ensure ther saety and relablty. Fgure 1. Descrpton o an LNG mportaton termnal ( One o the most cost eectve ways to estmate the rsk s LOPA (Layer o Protecton Analyss) because t can provde quanted rsk results wth less tme and eorts than other methods. Thus, LOPA was appled n ths research. For LOPA applcaton, alure data are essental to compute rsk requences (see Fgure ). However, the alure data rom the ndustry are very sparse and have statstcally shaky grounds due to nsucent populaton o sample data and relatvely short-term operatonal hstory. Bayesan estmaton s dented as one o the better methods to use to compensate or the weaknesses ound n the LNG ndustry s alure data. It can update the generc data wth plant specc data. In other words, the data updated by Bayesan logc can relect both long-term based hstorcal experences and plant specc

3 condtons. Thus, n ths research, the new Bayesan-LOPA methodology was developed as shown n Fgure 3, and t was appled to an LNG mportaton termnal to estmate the potental rsks. IPL1 IPL IPL3 Consequence occurs Intatng event Estmated requency Note Impact event SUCCESS SUCCESS Undesred but tolerable outcome SUCCESS Undesred but FAILURE tolerable outcome PFD 1 =p 1 1 = * p 1 FAILURE LOPA path Consequences PFD = p = * p 1 * p FAILURE exceed crtera PFD 3 = p 3 3 = * p 1 * p * p 3 Frequency Sae outcome Fgure. Descrpton o LOPA methodology Based on Bayesan estmaton, the requences o ntatng events were obtaned usng a conjugate gamma dstrbuton as the normaton and Posson dstrbuton as the lkelhood uncton. OREDA (Oshore Relablty Data) database was used as a dstrbuton because t was produced rom a gamma dstrbuton (see Fgure 4). I there s no normaton, Jereys nonnormatve may be used. The LNG plant alure database was used as plant specc lkelhood normaton. The PFDs (Probablty o Falure on Demand) o IPLs (Independent Protecton Layers) were estmated wth the conjugate beta dstrbuton and bnomal lkelhood dstrbuton. EIReDA (European Industry Relablty Data Bank) database was used as

4 v normaton because t provded the alure data made rom beta dstrbuton. In some cases EIReDA dd not provde alure data, so the newly developed Frequency-PFD converson method was used nstead. By the combnaton o Bayesan estmaton and LOPA procedures, the Bayesan-LOPA methodology was developed. The method was appled to an LNG mportaton termnal. For seven ncdent scenaros, t produced vald rsk values. The eror values o every ntatng event or IPLs are located between and lkelhood values. Ths means that the eror values are vald and wellupdated. The ound rsk values were compared to the tolerable rsk crtera gven by CCPS (Center or the Chemcal Process Saety) to make rsk decsons. Fnally, the estmated rsk values o seven ncdent scenaros were compared to each other to make a rsk rankng n vew o probablstc rsk analyss whch consders only alure requency wthout consderng consequence analyss. In concluson, as the good saety records o LNG ndustres speak, n ths research, t can be generally concluded that the LNG termnal has good saety protectons to prevent dangerous events (see Fgure 5). The newly developed Bayesan- LOPA methodology as one o the rsk assessment methods really does work well n an LNG mportaton termnal and t can be appled n other ndustres ncludng reneres, petrochemcals, nuclear plants, and aerospace ndustres. Moreover, t can be used wth other requency analyss methods such as Fault Tree Analyss (FTA) and Event Tree Analyss (ETA).

5 v Process normaton Process Flow Dagram, P&ID, Process Data Process Hazard Analyss (PHA) HAZOP Estmate Consequence & Severty Category approach Develop Scenaros From PHA results Scenaro : Intatng event + Consequence Identy Intatng Event Frequency Identy Related IPLs & Estmatng PFDs o IPLs Bayesan Engne Generc Data & Plant Specc Data IPL : Independence, Eectveness, Audtablty PFD o IPLs : Generc, plant specc data Estmate Scenaro Frequency c c J I = j = 1 = I PFD PFD 1 j PFD... PFD J Rsk Rankng, Make Rsk Decsons Rsk Rankng Compare wth tolerable rsk crtera Recommendatons or Saety Enhancement Add IPLs or saety measures Fgure 3. The low dagram o ths research

6 v Frequency o ntatng event wth normatve Pror dstrbuton (requency) Gamma dstrbuton β 1 λβ ( λ) = λ e Γ( ) OREDA μ μ = = γ V σ, V σ = = μ μ Lkelhood uncton (probablty) Posson dstrbuton λt x e ( λt) Pr( X = x) = x! LNG aclty alure data x : No. o alures t:tmetoalure OREDA 1 ( γ ) 1 ( λ) = λ e Γ( ) β = γ 1 1 λ( γ ) Bayesan Engne λ ( x+ ) 1 λ( t+ β ) e Posteror dstrbuton (requency) Gamma dstrbuton, = x +, β = t + β = t + 1/ γ Mean o Posteror requency x + x + μ = = = β t + β t +1/ γ 90% Bayes credble nterval λ = χ 0.05( ) / β, λ χ 0. 95( ) / β = Fgure 4. The schematc dagram o Bayesan estmaton or ntatng events

7 v Comparson o scenaro rsk 1.00E E-01 Pror Lkelhood Posteror 1.00E E-03 requency (/year) 1.00E E E E E E E E E E-05.60E E-07.45E E E E E E E E E E-05.90E-06.80E E E E-11 scenaro 1 scenaro scenaro 3 scenaro 4 scenaro 5 scenaro 6 scenaro E E E E-14 Fgure 5. The rsk value graphs o seven ncdent scenaros rom an LNG termnal