LEADER 05545nam 2200673 a 450 001 9910830448703321 005 20221209000215.0 010 $a1-282-78325-4 010 $a9786612783258 010 $a0-470-93519-7 010 $a1-59124-596-6 035 $a(CKB)111086367652150 035 $a(EBL)589020 035 $a(SSID)ssj0000072116 035 $a(PQKBManifestationID)11110187 035 $a(PQKBTitleCode)TC0000072116 035 $a(PQKBWorkID)10091632 035 $a(PQKB)10649048 035 $a(MiAaPQ)EBC589020 035 $a(OCoLC)669165931 035 $a(EXLCZ)99111086367652150 100 $a19971010d1998 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aGuidelines for pressure relief and effluent handling systems$b[electronic resource] /$fCenter for Chemical Process Safety of the American Institute of Chemical Engineers 210 $aNew York, N.Y. $cAmerican Institute of Chemical Engineers$dc1998 215 $a1 online resource (564 p.) 300 $aDescription based upon print version of record. 311 1 $a0-8169-0476-6 320 $aIncludes bibliographical references and index. 327 $aGuidelines for Pressure Relief and Effluent Handling Systems; Contents; Preface; Acknowledgments; Acronyms and Abbreviations; 1 Introduction; 1.1. Objective; 1.2. Scope; 1.3. Design Codes and Regulations, and Sources of Information; 1.4. Organization of This Book; 1.5. General Pressure Relief System Design Criteria; 1.5.1 Process Hazards Analysis; 1.5.2 Process Safety Information; 1.5.3 Problems Inherent in Pressure Relief and Effluent Handling System Design; 2 Relief Design Criteria and Strategy; 2.1. Limitations of the Technology; 2.2. General Pressure Relief Strategy 327 $a2.2.1 Mechanism of Pressure Relief2.2.2 Approach to Design; 2.2.3 Limitations of Systems Actuated by Pressure; 2.2.4 Consideration of Consequences; 2.3. Codes, Standards, and Guidelines; 2.3.1 Scope of Principal USA Documents; 2.3.2 General Provisions; 2.3.3 Protection by System Design; 2.4. Relief Device Types and Operation; 2.4.1 General Terminology; 2.4.2 Pressure Relief Valves; 2.4.3 Rupture Disk Devices; 2.4.4 Devices in Combination; 2.4.5 Miscellaneous Nonreclosing Devices; 2.4.6 Miscellaneous Low-Pressure Devices; 2.4.7 Miscellaneous Relief System Components 327 $a2.4.8 Selection of Pressure Relief Devices2.5. Relief System Layout; 2.5.1 General Code Requirements; 2.5.2 Pressure Relief Valves; 2.5.3 Rupture Disk Devices; 2.5.4 Low-Pressure Devices; 2.5.5 Series/Parallel Devices; 2.5.6 Header Systems; 2.5.7 Mechanical Integrity; 2.5.8 Material Selection; 2.5.9 Drainage and Freeze-up Provisions; 2.5.10 Noise; 2.6. Design Flows and Code Provisions; 2.6.1 Safety Valves; 2.6.2 Relief Valves; 2.6.3 Low Pressure Devices; 2.6.4 Rupture Disk Devices; 2.6.5 Devices in Combination; 2.6.6 Miscellaneous Nonreclosing Devices; 2.7. Scenario Selection Considerations 327 $a2.7.1 Events Requiring Relief Due to Overpressure2.7.2 Design Scenarios; 2.8. Fluid Properties and System Characterization; 2.8.1 Data Sources/Determination/Estimation; 2.8.2 Pure-Component Properties; 2.8.3 Mixture Properties; 2.8.4 Phase Behavior; 2.8.5 Chemical Reaction; 2.8.6 Miscellaneous Fluid Characteristics; 2.9. Fluid Behavior in Vessel; 2.9.1 Accounting for Chemical Reaction; 2.9.2 Two-Phase Venting Conditions and Effects; 2.10. Flow of Fluids through Relief Systems; 2.10.1 Conditions for Two-Phase Flow; 2.10.2 Nature of Compressible Flow 327 $a2.10.3 Stagnation Pressure and Critical Pressure Ratio2.10.4 Flow Rate to Effluent Handling System; 2.11. Relief System Reliability; 2.11.1 Relief Device Reliability; 2.11.2 System Reliability; Appendix 2A. International Codes and Standards; Appendix 2B. Property Mixing Rules; Appendix 2C. Code Case: Protection by System Design; 3 Relief System Design and Rating Computations; 3.1. Introduction; 3.1.1 Purpose and Scope; 3.1.2 Required Background; 3.2. Vessel Venting Background; 3.2.1 General; 3.2.2 Material and Energy Balances; 3.2.3 Phase Behavior; 3.2.4 Two-Phase Venting Technology 327 $a3.2.5 Methods of Solution 330 $aCurrent industry, government and public emphasis on containment of hazardous materials makes it essential for each plant to reduce and control accidental releases to the atmosphere. Guidelines for Pressure Relief and Effluent Handling Systems meets the need for information on selecting and sizing pressure relief devices and effluent handling systems that will maintain process integrity and avoid discharge of potentially harmful materials to the atmosphere. With a CD-ROM enclosed containing programs for calculating flow through relief devices, effluent handling systems, and associated piping, t 606 $aChemical plants$xWaste disposal 606 $aHazardous wastes$xManagement 606 $aRelief valves 606 $aSewage disposal 615 0$aChemical plants$xWaste disposal. 615 0$aHazardous wastes$xManagement. 615 0$aRelief valves. 615 0$aSewage disposal. 676 $a620.106 676 $a660.0286 676 $a660/.028/6 712 02$aAmerican Institute of Chemical Engineers.$bCenter for Chemical Process Safety. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830448703321 996 $aGuidelines for pressure relief and effluent handling systems$92238502 997 $aUNINA LEADER 07176nam 22006735 450 001 9910484791403321 005 20251225174941.0 010 $a3-030-72087-X 024 7 $a10.1007/978-3-030-72087-2 035 $a(CKB)4100000011807070 035 $a(MiAaPQ)EBC6527475 035 $a(Au-PeEL)EBL6527475 035 $a(OCoLC)1246579234 035 $a(PPN)254719236 035 $a(BIP)79632353 035 $a(BIP)79236774 035 $a(DE-He213)978-3-030-72087-2 035 $a(EXLCZ)994100000011807070 100 $a20210325d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries $e6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II /$fedited by Alessandro Crimi, Spyridon Bakas 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (539 pages) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12659 311 08$a3-030-72086-1 327 $aBrain Tumor Segmentation -- Lightweight U-Nets for Brain Tumor Segmentation -- Efficient Brain Tumour Segmentation using Co-registered Data and Ensembles of Specialised Learners -- Efficient MRI Brain Tumor Segmentation using Multi-Resolution Encoder-Decoder Networks -- Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework -- HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation -- H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task -- 2D Dense-UNet: A Clinically Valid Approach to Automated Glioma Segmentation -- Attention U-Net with Dimension-hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image Segmentation -- MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation -- Glioma Segmentation with 3D U-Net Backed with Energy- Based Post- Processing -- nnU-Net for Brain Tumor Segmentation -- A Deep Random Forest Approach forMultimodal Brain Tumor Segmentation -- Brain tumor segmentation and associated uncertainty evaluation using Multi-sequences MRI Mixture Data Preprocessing -- A Deep supervision CNN network for Brain tumor Segmentation -- Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans -- Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation -- Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features Fusion -- Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge -- 3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction -- Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI using Selective Kernel Networks -- 3D brain tumor segmentation and survival prediction using ensembles of Convolutional Neural Networks -- Brain Tumour Segmentation using Probabilistic U-Net -- Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets -- A Deep Supervised U-Attention Net for Pixel-wise Brain Tumor Segmentation -- A two stage atrous convolution neural network for brain tumor segmentation -- TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI data -- Brain Tumor Segmentation and Survival Prediction using Automatic Hardmining in 3D CNN Architecture -- Some New Tricks for Deep Glioma Segmentation -- PieceNet: A Redundant UNet Ensemble -- Cerberus: A Multi-headed Network for BrainTumor Segmentation -- An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients based on Volumetric and Shape Features -- Squeeze-and-Excitation Normalization for Brain Tumor Segmentation -- Modified MobileNet for Patient Survival Prediction -- Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation -- Brain Tumor Segmentation and Survival Prediction Using Patch Based Modi?ed U-Net -- DR-Unet104 for Multimodal MRI brain tumor segmentation -- Glioma Sub-region Segmentation on Multi-parameter MRI with Label Dropout -- Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation -- Learning Dynamic Convolutions for Multi-Modal 3D MRI Brain Tumor Segmentation -- Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification -- Automatic Glioma Grading Based on Two-stage Networks by Integrating Pathology and MRI Images -- Brain Tumor Classification Based on MRI Images and Noise Reduced Pathology Images -- Multimodal brain tumor classification -- A Hybrid Convolutional Neural Network Based-Method for Brain Tumor Classification Using mMRI and WSI -- CNN-based Fully Automatic Glioma Classification with Multi-modal Medical Images -- Glioma Classification Using Multimodal Radiology and Histology Data. 330 $aThis two-volume set LNCS 12658 and 12659 constitutes the thoroughly refereed proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in Lima, Peru, in October 2020.* The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (16 selected papers from 21 submissions); brain tumor image segmentation (69 selected papers from 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions). *The workshop and challenges were held virtually. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12659 606 $aComputer vision 606 $aMachine learning 606 $aPattern recognition systems 606 $aBioinformatics 606 $aComputer Vision 606 $aMachine Learning 606 $aAutomated Pattern Recognition 606 $aComputational and Systems Biology 615 0$aComputer vision. 615 0$aMachine learning. 615 0$aPattern recognition systems. 615 0$aBioinformatics. 615 14$aComputer Vision. 615 24$aMachine Learning. 615 24$aAutomated Pattern Recognition. 615 24$aComputational and Systems Biology. 676 $a616.99281 702 $aCrimi$b Alessandro 702 $aBakas$b Spyridon 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484791403321 996 $aBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries$92032252 997 $aUNINA