00697nam2 22002171i 450 990002317350403321000231735FED01000231735(Aleph)000231735FED0100023173520030801d--------km-y0itay50------baPhysiological (phenotypic) mechanisms responsible for drug resistance. London, 1957,p. 165-179.001000221609Davis,Bernard David90988ITUNINARICAUNIMARCBK990002317350403321FFABCPhysiological (phenotypic) mechanisms responsible for drug resistance. London, 1957,p. 165-179389384UNINAING0105289nam 2200649Ia 450 991014400350332120170810192842.01-281-08795-597866110879513-527-60947-43-527-60922-9(CKB)1000000000376062(EBL)481299(OCoLC)180924729(SSID)ssj0000203895(PQKBManifestationID)11188362(PQKBTitleCode)TC0000203895(PQKBWorkID)10152153(PQKB)11353014(MiAaPQ)EBC481299(EXLCZ)99100000000037606220060721d2006 uy 0engur|n|---|||||txtccrModel based control[electronic resource] case studies in process engineering /Paul Serban Agachi ... [et al.]Weinheim Wiley-VCHc20061 online resource (291 p.)Description based upon print version of record.3-527-31545-4 Includes bibliographical references and index.Model Based Control; Table of Contents; Preface; 1 Introduction; 1.1 Introductory Concepts of Process Control; 1.2 Advanced Process Control Techniques; 1.2.1 Key Problems in Advanced Control of Chemical Processes; 1.2.1.1 Nonlinear Dynamic Behavior; 1.2.1.2 Multivariable Interactions between Manipulated and Controlled Variables; 1.2.1.3 Uncertain and Time-Varying Parameters; 1.2.1.4 Deadtime on Inputs and Measurements; 1.2.1.5 Constraints on Manipulated and State Variables; 1.2.1.6 High-Order and Distributed Processes1.2.1.7 Unmeasured State Variables and Unmeasured and Frequent Disturbances1.2.2 Classification of the Advanced Process Control Techniques; 2 Model Predictive Control; 2.1 Internal Model Control; 2.2 Linear Model Predictive Control; 2.3 Nonlinear Model Predictive Control; 2.3.1 Introduction; 2.3.2 Industrial Model-Based Control: Current Status and Challenges; 2.3.2.1 Challenges in Industrial NMPC; 2.3.3 First Principle (Analytical) Model-Based NMPC; 2.3.4 NMPC with Guaranteed Stability; 2.3.5 Artificial Neural Network (ANN)-Based Nonlinear Model Predictive Control; 2.3.5.1 Introduction2.3.5.2 Basics of ANNs2.3.5.3 Algorithms for ANN Training; 2.3.5.4 Direct ANN Model-Based NMPC (DANMPC); 2.3.5.5 Stable DANMPC Control Law; 2.3.5.6 Inverse ANN Model-Based NMPC; 2.3.5.7 ANN Model-Based NMPC with Feedback Linearization; 2.3.5.8 ANN Model-Based NMPC with On-Line Linearization; 2.3.6 NMPC Software for Simulation and Practical Implementation; 2.3.6.1 Computational Issues; 2.3.6.2 NMPC Software for Simulation; 2.3.6.3 NMPC Software for Practical Implementation; 2.4 MPC General Tuning Guidelines; 2.4.1 Model Horizon (n); 2.4.2 Prediction Horizon (p); 2.4.3 Control Horizon (m)2.4.4 Sampling Time (T)2.4.5 Weight Matrices (Γ(/)(y) and Γ(/)(u)); 2.4.6 Feedback Filter; 2.4.7 Dynamic Sensitivity Used for MPC Tuning; 3 Case Studies; 3.1 Productivity Optimization and Nonlinear Model Predictive Control (NMPC) of a PVC Batch Reactor; 3.1.1 Introduction; 3.1.2 Dynamic Model of the PVC Batch Reactor; 3.1.2.1 The Complex Analytical Model of the PVC Reactor; 3.1.2.2 Morphological Model; 3.1.2.3 The Simplified Dynamic Analytical Model of the PVC Reactor; 3.1.3 Productivity Optimization of the PVC Batch Reactor; 3.1.3.1 The Basic Elements of GAs3.1.3.2 Optimization of the PVC Reactor Productivity through the Initial Concentration of Initiators3.1.3.3 Optimization of PVC Reactor Productivity by obtaining an Optimal Temperature Policy; 3.1.4 NMPC of the PVC Batch Reactor; 3.1.4.1 Multiple On-Line Linearization-Based NMPC of the PVC Batch Reactor; 3.1.4.2 Sequential NMPC of the PVC Batch Reactor; 3.1.5 Conclusions; 3.1.6 Nomenclature; 3.2 Modeling, Simulation, and Control of a Yeast Fermentation Bioreactor; 3.2.1 First Principle Model of the Continuous Fermentation Bioreactor3.2.2 Linear Model Identification and LMPC of the BioreactorFilling a gap in the literature for a practical approach to the topic, this book is unique in including a whole section of case studies presenting a wide range of applications from polymerization reactors and bioreactors, to distillation column and complex fluid catalytic cracking units. A section of general tuning guidelines of MPC is also present.These thus aid readers in facilitating the implementation of MPC in process engineering and automation. At the same time many theoretical, computational and implementation aspects of model-based control are explained, with a look at both linear and Chemical engineeringChemical process controlCase studiesPredictive controlCase studiesElectronic books.Chemical engineering.Chemical process controlPredictive control660.2815Agachi Paul ȘerbanAgachi Paul Serban883805MiAaPQMiAaPQMiAaPQBOOK9910144003503321Model based control2288656UNINA02946oam 22005654a 450 991031644930332120230621135718.01-950192-20-210.21983/P3.0248.1.00(CKB)4100000007881609(OAPEN)1004825(OCoLC)1100539650(MdBmJHUP)muse77058(oapen)https://directory.doabooks.org/handle/20.500.12854/26991(oapen)doab26991(EXLCZ)99410000000788160920190314d2019 uy 0engurmu#---auuuutxtrdacontentcrdamediacrrdacarrierThe Imagery of Interior SpacesDominique Bauer ; [edited by] Dominique Bauer, Michael J. Kelly1st edition.Brooklyn, NYpunctum books2019Santa Barbara, CA :Punctum Books,2019.©2019.1 online resource (241 pages) illustrations; PDF, digital file(s)1-950192-19-9 Includes bibliographical references.On the unstable boundaries between “interior” and “exterior,” “private” and “public,” and always in some way relating to a “beyond,” the imagery of interior space in literature reveals itself as an often disruptive code of subjectivity and of modernity. The wide variety of interior spaces elicited in literature — from the odd room over the womb, secluded parks, and train compartments, to the city as a world under a cloth — reveal a common defining feature: these interiors can all be analyzed as codes of a paradoxical, both assertive and fragile, subjectivity in its own unique time and history. They function as subtexts that define subjectivity, time, and history as profoundly ambiguous realities, on interchangeable existential, socio-political, and epistemological levels. This volume addresses the imagery of interior spaces in a number of iconic and also lesser known yet significant authors of European, North American, and Latin American literature of the nineteenth, twentieth, and twenty-first centuries: Djuna Barnes, Edmond de Goncourt, William Faulkner, Gabriel García Márquez, Benito Pérez Galdós, Elsa Morante, Robert Musil, Jules Romains, Peter Waterhouse, and Émile Zola.Literary theorybicsscElectronic books. literary studiesinterior designarchitecturecultural studiesspatialityLiterary theory809.93358Bauer Dominique932972Kelly Michael JedtBauer DominiqueothKelly Michael JothMdBmJHUPMdBmJHUPBOOK9910316449303321The Imagery of Interior Spaces2428725UNINA