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Record Nr. |
UNINA9910462083303321 |
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Titolo |
Lee's loss prevention in the process industries [[electronic resource] ] : hazard identification, assessment, and control |
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Pubbl/distr/stampa |
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Boston, : Butterworth-Heinemann |
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Amsterdam, : Elsevier, 2012 |
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ISBN |
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Edizione |
[4th ed. /] |
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Descrizione fisica |
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1 online resource (3685 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Petroleum chemicals industry - Great Britain - Safety measures |
Petroleum chemicals industry - United States - Safety measures |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Rev. ed. of: Loss prevention in the process industries / Frank P. Lees. |
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Nota di bibliografia |
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Includes bibliographical references and indexes. |
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Nota di contenuto |
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Front Cover; Lees' Loss Prevention in the Process Industries; Copyright Page; Preface to Fourth Edition; Preface to Third Edition; Preface to Second Edition; Preface to First Edition; Acknowledgements; Terminology; Notation; Use of References; List of Contributors; Contents for Volume 1; Contents for Volume 2; Contents for Volume 3; 1 Introduction; 1.1 Management Leadership; 1.2 Industrial Safety and Loss Trends; 1.3 Safety and Environmental Concerns; 1.4 Loss Prevention - 1; 1.5 Large Single-Stream Plants; 1.6 Loss Prevention - 2; 1.7 Total Loss Control; 1.8 Quality Assurance |
1.9 Total Quality Management1.10 Risk Management; 1.11 Safety-Critical Systems; 1.12 Environment and Sustainable Development; 1.13 Responsible Care; 1.14 Academic and Research Activities; 1.15 Overview; 2 Incidents and Loss Statistics; 2.1 The Incident Process; 2.1.1 The Houston Model; 2.1.2 The Fault Tree Model; 2.1.3 The MORT Model; 2.1.4 The Rasmussen Model; 2.1.5 The ACSNI Model; 2.1.6 The Bellamy and Geyer Model; 2.1.7 The Kletz Model; 2.2 Standard Industrial Classification; 2.3 Injury Statistics; 2.3.1 United States of America |
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2.3.1.1 National Response Center's (NRC) Incident Reporting Information System (IRIS)2.3.1.2 EPA's Risk Management Program (RMP) Rule's 5-Year Accident History Database; 2.3.1.3 EPA's Accidental Release Information Program (ARIP) Database; 2.3.1.4 Bureau of Labor Statistics' (BLS) Databases for the US Occupational Safety and Health Administration (OSHA); 2.3.1.5 U.S. Centers for Disease Control and Prevention's (CDC) Wide-ranging On-line Data for Epidemiological Reporting (WO... |
2.3.1.6 U.S. Department of Health and Human Services' Agency for Toxic Substances and Disease Registry's (ATSDR) Hazardous ...2.3.2 United Kingdom; 2.4 Major Disasters; 2.5 Major Process Hazards; 2.5.1 The Inventory; 2.5.2 The Energy Factor; 2.5.3 The Time Factor; 2.5.4 The Intensity-Distance Relationship; 2.5.5 The Exposure Factor; 2.5.6 The Intensity-Damage and Intensity-Injury Relationships; 2.6 Fire Loss Statistics; 2.7 Fire and Explosion; 2.8 Causes of Loss; 2.9 Down-Time Losses; 2.10 Trend of Injuries; 2.11 Trend of Losses; 2.12 Case Histories; 3 Legislation and Law; 3.1 US Legislation |
3.2 US Regulatory Agencies3.3 Codes and Standards; 3.4 Occupational Safety and Health Act 1970; 3.5 US Environmental Legislation; 3.6 US Toxic Substances Legislation; 3.7 US Accidental Chemical Release Legislation; 3.8 US Transport Legislation; 3.8.1 Natural Gas; 3.8.2 FERC History; 3.8.3 USCG and MARAD History; 3.9 US Security Legislation; 3.10 US Developing Legislation; 3.11 EU Legislations; 3.12 Other Legislation; 3.13 Regulatory Support; 3.14 US Chemical Safety Board; 4 Major Hazard Control; Foreword by Jerry Havens; 4.1 Superstar Technologies; 4.2 Hazard Monitoring; 4.3 Risk Issues |
4.4 Risk Perception |
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Sommario/riassunto |
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Safety in the process industries is critical for those who work with chemicals and hazardous substances or processes. The field of loss prevention is, and continues to be, of supreme importance to countless companies, municipalities and governments around the world, and Lees' is a detailed reference to defending against hazards. Recognized as the standard work for chemical and process engineering safety professionals, it provides the most complete collection of information on the theory, practice, design elements, equipment, regulations and laws covering the field of process safety. An |
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2. |
Record Nr. |
UNINA9910744507403321 |
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Autore |
Guiochet Jérémie |
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Titolo |
Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops : ASSURE, DECSoS, SASSUR, SENSEI, SRToITS, and WAISE, Toulouse, France, September 19, 2023, Proceedings / / edited by Jérémie Guiochet, Stefano Tonetta, Erwin Schoitsch, Matthieu Roy, Friedemann Bitsch |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (448 pages) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 14182 |
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Altri autori (Persone) |
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TonettaStefano |
SchoitschErwin |
RoyMatthieu |
BitschFriedemann |
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Disciplina |
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Soggetti |
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Computer engineering |
Computer networks |
Image processing - Digital techniques |
Computer vision |
Application software |
Software engineering |
Computer science |
Data protection |
Computer Engineering and Networks |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Computer and Information Systems Applications |
Software Engineering |
Theory of Computation |
Security Services |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Using Assurance Cases to Prevent Malicious Behaviour from Targeting Safety Vulnerabilities -- Constructing Security Cases Based on Formal Verification of Security Requirements in Alloy.-Assurance Cases for Timing Properties of Automotive TSN Networks -- Toward Dependability Assurance Framework for Automated Driving Systems -- A Quantitative Approach for System of Systems’ Resilience Analyzing Based on Archimate -- Towards DO-178C Compliance of a Secure Product -- The Need for Threat Modelling in Unmanned Aerial Systems -- Using Runtime information of controllers for safe adaptation at runtime: a Process Mining approach -- Safety and Robustness for Deep Neural Networks: An Automotive Use Case -- Towards Dependable Integration Concepts for AI-based Systems -- A Methodology for the Qualification of Operating Systems and Hypervisors for the deployment in IoT devices -- Computer-Aided Generation of Assurance Cases -- RACK: A Semantic Model and Triplestore for Curation of Assurance Case Evidence -- Patterns for Integrating NIST 800-53 Controls into Security Assurance Cases -- Analyzing Origins of Safety and Security Interactions using Feared Events Trees and Multi-level Model -- Utilising Redundancy to Enhance Security of Safety-Critical Systems -- Reliability Evaluation of Autonomous Transportation System Architecture Based on Markov Chain -- Uncertainty Quantification for Semantic Segmentation Models via Evidential Reasoning -- Research on the Reliability of High-Speed Railway Dispatching and Commanding Personnel with Multi Physiological Signals -- Research on Brain Load prediction based on machine learning for High-speed Railway -- Paired Safety Rule Structure for Human-machine Cooperation with Feature Update and Evolution -- Towards an Effective Generation of Functional Scenarios for AVs to Guide Sampling -- Rear-end Collision Risk Analysis for Autonomous Driving -- Improving road traffic safety and performance – barriers and directions towards cooperative automated vehicles -- A Group-Level Learning Approach Using Logistic Regression for Fairer Decisions -- Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning -- AIMOS: Metamorphic Testing of AI - An Industrial Application -- AERoS: Assurance of Emergent Behaviour in Autonomous Robotic Swarms -- A Reasonable Driver Standard for Automated Vehicle Safety -- Structuring Research Related to Dynamic Risk Management for Autonomous Systems -- Towards Safe Machine Learning Lifecycles with ESG Model Cards -- Towards Deep Anomaly Detection with Structured Knowledge Representations -- Evaluating and Increasing Segmentation Robustness in CARLA -- Safety Integrity Levels for Artificial Intelligence -- Can Large Language Models assist in Hazard Analysis -- Contextualised Out-of-Distribution Detection using Pattern Identification. . |
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Sommario/riassunto |
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This book constitutes the proceedings of the Workshops held in conjunction with SAFECOMP 2023, held in Toulouse, France, during September 19, 2023. The 35 full papers included in this volume were carefully reviewed and selected from 49 submissions. - 8th International Workshop on Assurance Cases for Software-intensive Systems (ASSURE 2023) - 18th International Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems (DECSoS 2023) - 10th International Workshop on Next Generation of System Assurance Approaches for Critical Systems (SASSUR 2023) - Second International Workshop on Security and Safety Interactions (SENSEI 2023) - First International Workshop on Safety/ Reliability/ Trustworthiness of Intelligent Transportation Systems (SRToITS 2023) - 6th International Workshop on Artificial Intelligence Safety Engineering (WAISE 2023) . |
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