000			03544cam a2200529Ka 4500
001			9780429287800
003			FlBoTFG
005			20220509193125.0
006			m o d
007			cr un|||||||||
008			200821s2021 flu ob 000 0 eng d
040			_aOCoLC-P _beng _cOCoLC-P
020			_a0367254409 _q(Cloth)
020			_a9780367254407 _q(Cloth)
020			_a1000174220 _q(electronic bk.)
020			_a9781000174229 _q(electronic bk.)
020			_a1000174239 _q(electronic bk.)
020			_a9781000174236 _q(electronic bk.)
020			_a9781000174212 _q(Proquest Ebook Central)
020			_a1000174212 _q(Proquest Ebook Central)
020			_a9780429287800 _q(electronic bk.)
020			_a0429287801 _q(electronic bk.)
024	7		_a10.1201/9780429287800 _2doi
035			_a(OCoLC)1191465418 _z(OCoLC)1182836556
035			_a(OCoLC-P)1191465418
050		4	_aTP752
072		7	_aTEC _x047000 _2bisacsh
072		7	_aTH _2bicssc
082	0	4	_a620.1/07 _223
100	1		_aUṇṇikr̥ṣṇan, Ji., _d1944- _eauthor.
245	1	0	_aOil and gas processing equipment : _brisk assessment with Bayesian networks / _cG. Unnikrishnan.
250			_aFirst edition.
260			_aBoca Raton : _bCRC Press, _c2021.
300			_a1 online resource.
336			_atext _2rdacontent
337			_acomputer _2rdamedia
338			_aonline resource _2rdacarrier
520			_aOil and gas industries apply several techniques for assessing and mitigating the risks that are inherent in its operations. In this context, the application of Bayesian Networks (BNs) to risk assessment offers a different probabilistic version of causal reasoning. Introducing probabilistic nature of hazards, conditional probability and Bayesian thinking, it discusses how cause and effect of process hazards can be modelled using BNs and development of large BNs from basic building blocks. Focus is on development of BNs for typical equipment in industry including accident case studies and its usage along with other conventional risk assessment methods. Aimed at professionals in oil and gas industry, safety engineering, risk assessment, this book Brings together basics of Bayesian theory, Bayesian Networks and applications of the same to process safety hazards and risk assessment in the oil and gas industry Presents sequence of steps for setting up the model, populating the model with data and simulating the model for practical cases in a systematic manner Includes a comprehensive list on sources of failure data and tips on modelling and simulation of large and complex networks Presents modelling and simulation of loss of containment of actual equipment in oil and gas industry such as Separator, Storage tanks, Pipeline, Compressor and risk assessments Discusses case studies to demonstrate the practicability of use of Bayesian Network in routine risk assessments
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aGas manufacture and works _xRisk assessment _xMathematics.
650		0	_aPetroleum refineries _xRisk assessment _xMathematics.
650		0	_aGas manufacture and works _xEquipment and supplies _xSafety measures _xMathematics.
650		0	_aPetroleum refineries _xEquipment and supplies _xSafety measures _xMathematics.
650		0	_aBayesian statistical decision theory.
650		7	_aTECHNOLOGY / Petroleum _2bisacsh
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9780429287800
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c130368 _d130368