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Advanced Process Monitoring for Industry 4.0
| Advanced Process Monitoring for Industry 4.0 |
| Autore | Reis Marco S |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (288 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
artificial generation of variability
auto machine learning classification combustion condition monitoring construction industry continuous casting control chart pattern convolutional neural network curve resolution data augmentation data mining data reconciliation decision support systems digital processing discriminant analysis disruption management disruptions expert systems failure mode and effects analysis (FMEA) failure mode effects analysis fault detection fault diagnosis high-dimensional data imbalanced data Industry 4.0 latent variables models load identification membrane monitoring multi-mode model multi-phase residual recursive model multiscale multivariate data analysis n/a neural networks non-intrusive load monitoring online optical sensors OPTICS pasting process PCA plaster production PLS principal component analysis process control process image process monitoring quality control quality prediction real-time risk priority number rolling bearing semiconductor manufacturing signal detection Six Sigma spatial-temporal data spectroscopy measurements statistical process control statistical process monitoring time series classification yield improvement |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557491503321 |
Reis Marco S
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes
| Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes |
| Autore | Gao Zhiwei |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (514 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
acetonitrile
additive white Gaussian noises (AWGN) association matrix behavior transfer belief statistic cascading failures CFSFDP algorithm close distance coal seams coarse model complex network theory condition monitoring constitutive model control chart control problem cumulative distribution function data-driven methods dead time deep deterministic policy gradient deep reinforcement learning deviation control distributed generation (DG) distributed model predictive control drilling machine energy conversion systems event-based EWT failure criterion fast Fourier transform (FFT) Fast-Newman algorithm fault classification fault diagnosis fault prognosis flexible flow shop flexible flow shop scheduling flotation process full-heat integration fuzzy logic gamma distribution goaf gob-side entry retaining by roof cutting graph theory hardware experiment Holmquist-Johnson-Cook constitutive model of briquette Hopfield neural network Hybrid Attack Graph improved compact genetic algorithm in-situ monitoring incomplete data INSGA-II Level-of-Resilience limited buffer link-addition strategy load identification local scheduling loop design memetic salp swarm algorithm microalgae MIMO temperature control in heating process system multi-input and multi-output system multi-input multi-output (MIMO) temperature system multi-linear principal component analysis (MPCA) multi-objective optimization multi-queue limited buffers multiscale fuzzy entropy n/a neutrosophic statistic non-pillar nonlinear adaptive backstepping controller disturbance observer numerical simulation oil and gas optimal nonlinear adaptive control parameter acquisition parameter uncertainties perturbation observer PID PNN pole-zero cancelation potential crowding distance power systems pressure swing distillation probability density function of the Gaussian distribution process control public buffer raceway reactive power optimization reagent dosage refrigeration reinforcement learning resilient control roadway supporting self-learning semiconductor bonding equipment-grouping method separation simulated annealing algorithm slow-mode-based control split Hopkinson pressure bar experiment stability state-action chains static and dynamic planning steam power plant steam/water loop stress distribution temperature difference temperature differences time series froth image topology transfer bees optimizer transient response tunnel boring machine uncorrelated multi-linear principal component analysis (UMPCA) variable geometry turbocharger voltage source converter water wind turbine wind turbine systems |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557338303321 |
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Gao Zhiwei
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent
| Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent |
| Autore | Gani Rafiqul |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (254 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
autoassociative neural network
big data bio-jet diesel co-hydrotreating coproduction cryogenic separation curricula design distribution energy systems engineering fuzzy optimization generalized disjunctive programming higher education hydrodeoxigenation hydrodesulphurisation input-output model integration LNG Lurgi syngas mathematical programming methanol synthesis MINLP mixed-integer programming n/a nonlinear principal component analysis nonlinear programming oil supply chain optimization parallel neural networks planning preliminary stage design process control process design process modeling process monitoring process scheduling process synthesis process system engineering process systems engineering quadratic optimization quadratically-constrained quadratic optimization reactive distillation robust optimization scheduling symmetry uncertainty visualization |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557292803321 |
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Gani Rafiqul
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mathematics and Engineering
| Mathematics and Engineering |
| Autore | Li Mingheng |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (306 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
agricultural division of labor
agro-hydrological systems amplitudes change anonymous signature artificial intelligence autonomous vehicle block methods cement chemical processes chemical reactor control comparative advantage control engineering control systems controller verification convergence analysis cyber-attacks digital computation direction cosines distillation column control economic model predictive control energy energy-based control ensemble kalman filter Euler angles extended kalman filter fusion Green's symmetry problem group signature homotopy analysis method industrial cyber-physical systems infra-marginal model interpretability kinematics kinetics larger Lipschitz constants linear matrix inequalities linear response eigenvalue problem mathematical model MATLAB simulation mechanics Micro Grid mining vibrating screen model predictive control moving horizon estimation multi-sensors nanofluids network classification network evolution network internal dynamics network invariants network symmetries neural network nonlinear chemical processes nonlinear processes opening capability optimization optimization theory parameter estimation payload swing attenuation Phoronomics power-law fluids process control propensities quadrotor real-time optimization responsive control richards equation seashore soft soil security smart grid space dynamics specialized production spring failures diagnosis state estimation stress-strain behavior sulfuric acid erosion symmetry ensembles target detection temperature-jump theoretical rigid body model thick restart transaction cost velocity-slip weighted Golub-Kahan-Lanczos algorithm YOLO |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557474903321 |
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Li Mingheng
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Modeling and Simulation of Energy Systems / Thomas A. Adams
| Modeling and Simulation of Energy Systems / Thomas A. Adams |
| Autore | Adams Thomas A |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (496 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
FCMP
modeling and simulation multiphase equilibrium modeling polymer electrolyte membrane fuel cell (PEMFC) dynamic simulation simulation multi-scale systems engineering process simulation cycling time-delay exergy loss gas path analysis oil and gas solar PV optimization second law efficiency auto thermal reformer friction factor optimal battery operation biodiesel energy time-varying operation efficiency process synthesis and design nonsmooth modeling mixture ratio supercritical CO2 dynamic optimization technoeconomic analysis work and heat integration compressibility factor multi-objective optimisation circulating fluidized bed boiler wind power naphtha recovery unit cost optimization recompression cycle hybrid Life Cycle Assessment post-combustion CO2 capture piecewise-linear function generation solar energy industrial process heat kriging statistical model supercritical pulverized coal (SCPC) parabolic trough combined cycle H2O-LiBr working pair linearization process integration smith predictor process design analysis by synthesis MINLP methyl-oleate diagnostics offshore wind double-effect system shale gas condensate geothermal energy multi-loop control R123 waste to energy hybrid system cogeneration energy storage energy efficiency nonlinear mathematical programming superstructure concentrating solar thermal desalination modelling binary cycle organic Rankine cycle refuse derived fuel power plants WHENS process control compressor modeling energy systems PTC life cycle analysis natural gas transportation isentropic exponent top-down models thermal storage supercritical carbon dioxide operations sustainable process design hybrid solar energy management R245fa building blocks energy economics micro gas turbine CSP fuel cost minimization problem CST palladium membrane hydrogen separation battery degradation optimal control RK-ASPEN process systems engineering supervisory control absorption refrigeration concentrating solar power shale gas condensate-to-heavier liquids Dieng DMR liquefaction processes dynamic modeling Organic Rankine Cycle (ORC) load-following demand response Indonesia |
| ISBN |
9783039215195
3039215191 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367747603321 |
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Adams Thomas A
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Product/Process Fingerprint in Micro Manufacturing / Guido Tosello
| Product/Process Fingerprint in Micro Manufacturing / Guido Tosello |
| Autore | Tosello Guido |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (274 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
Fresnel lenses
Electro sinter forging micro-injection moulding surface roughness charge relaxation time optimization gratings plasma-electrolytic polishing micro structures replication micro-grinding electrical discharge machining injection molding quality control commercial control hardware electrical current damping process monitoring fingerprints impact analysis current monitoring process control quality assurance surface integrity microfabrication microinjection moulding electro chemical machining superhydrophobic surface surface modification haptic actuator electrical discharge machining (EDM) surface morphology inline metrology optical quality control finishing flow length precision injection molding laser ablation micro metrology Halbach linear motor 2-step analysis computer holography PeP satellite drop process fingerprint materials characterisation current density micro drilling multi-spectral imaging lithography manufacturing signature artificial compound eye electrohydrodynamic jet printing ECM positioning platform diffractive optics bioceramics resistance sintering uncertainty budget product fingerprint confocal microscopy spectral splitting dental implant desirability function injection compression molding electrochemical machining (ECM) high strain rate effect process fingerprints |
| ISBN |
9783039210350
3039210351 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910346841703321 |
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Tosello Guido
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Recent Advances in Water and Wastewater Treatment with Emphasis in Membrane Treatment Operations
| Recent Advances in Water and Wastewater Treatment with Emphasis in Membrane Treatment Operations |
| Autore | Zouboulis Anastasios |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 online resource (230 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
adsorption
adsorption kinetics antimony antimony treatment arsenic arsenic adsorption backwash duration bioethanol recovery biofilm membrane bioreactor cake resistance ceramic membranes chromate co-treatment coagulation crossflow membrane filtration desalination dewatering drinking water Fe-based coagulants fouling granular ferric hydroxide HA-BSA mixtures hexavalent chromium hollow fiber membranes hollow fibre interaction energy mass transfer Membrane Bioreactor membrane filtration membrane fouling membrane resistance membranes microfiltration modeling multiphase multivariate statistics municipal wastewater sludge natural organic matter natural organic matter (NOM) operation optical sensors ozonation particle size distribution peroxone polluted waters polydimethylsiloxane process control produced water treatment Sb(III) Sb(V) second cheese whey separation small sized powdered ferric hydroxide sodium alginate solution conditions temperature TMP trickling biofilter ultrafiltration vacuum membrane distillation wastewater treatment water crisis water matrix water treatment winery effluents xDLVO theory |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910669804603321 |
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Zouboulis Anastasios
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Thermochemical Conversion Processes for Solid Fuels and Renewable Energies
| Thermochemical Conversion Processes for Solid Fuels and Renewable Energies |
| Autore | Alobaid Falah |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (222 p.) |
| Soggetto topico |
Research & information: general
Technology: general issues |
| Soggetto non controllato |
absorption chiller
biogas upgrading biomass biomass gasification carbon capture and storage/utilization CFB combustion chemical looping CO2 absorption CO2 capture combustion conversion model CSP dual fluidized bed dynamic process simulation energy analysis energy and exergy analyses exergy analysis experimental study fluctuating electricity generation fluidized-bed gasifier fuel feeding rate gasification hydrochar hydrodynamics hydrothermal carbonization integrated solar combined cycle ISCC kinetic model kinetics lignite lignite gasification load transients low-rank coal olivine one-dimensional SEG model operational flexibility performance and analysis power plant pressure swing adsorption process control process simulation PTC reaction model renewables sensitivity analyses solar cooling solar cooling system solar energy solar-driven air-conditioning sorbent deactivation tar absorption thermochemical conversion technologies thermodynamic modeling TRNSYS validation study |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557418803321 |
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Alobaid Falah
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Tuning and control loop performance / / Gregory K. McMillan
| Tuning and control loop performance / / Gregory K. McMillan |
| Autore | McMillan Gregory K. <1946-, > |
| Edizione | [Fourth edition.] |
| Pubbl/distr/stampa | New York, [New York] (222 East 46th Street, New York, NY 10017) : , : Momentum Press, , 2015 |
| Descrizione fisica | 1 online resource (584 pages) |
| Disciplina | 629.83 |
| Collana | Manufacturing and engineering collection |
| Soggetto topico |
Process control
Feedback control systems |
| Soggetto genere / forma | Electronic books. |
| Soggetto non controllato |
adaptive control
advanced regulatory control analyzer response auto tuner automation system batch optimization bioreactor control cascade control compressor control control loop performance control valve response external reset feedback feedforward control inverse response lambda tuning level control measurement response pH control PID control PID execution rate PID filter PID form PID structure PID tuning pressure control process control process disturbances process dynamics process interaction process metrics process nonlinearity process performance process response proportional-integral-derivative controller reactor control runaway reaction temperature control valve deadband valve position control valve resolution variable frequency drive response wireless control wireless response |
| ISBN | 1-60650-171-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
1. Fundamentals -- 1.1 Introduction -- 1.1.1 Perspective -- 1.1.2 Overview -- 1.1.3 Recommendations -- 1.2 PID controller -- 1.2.1 Proportional mode -- 1.2.2 Integral mode -- 1.2.3 Derivative mode -- 1.2.4 ARW and output limits -- 1.2.5 Control action and valve action -- 1.2.6 Operating modes -- 1.3 Loop dynamics -- 1.3.1 Types of process responses -- 1.3.2 Dead times and time constants -- 1.3.3 Open loop self-regulating and integrating process gains -- 1.3.4 Deadband, resolution, and threshold sensitivity -- 1.4 Typical mode settings -- 1.5 Typical tuning methods -- 1.5.1 Lambda tuning for self-regulating processes -- 1.5.2 Lambda tuning for integrating processes -- 1.5.3 IMC tuning for self-regulating processes -- 1.5.4 IMC tuning for integrating processes -- 1.5.5 Skogestad internal model control tuning for self-regulating processes -- 1.5.6 SIMC tuning for integrating processes -- 1.5.7 Traditional open loop tuning -- 1.5.8 Modified Ziegler-Nichols reaction curve tuning -- 1.5.9 Modified Ziegler-Nichols ultimate oscillation tuning -- 1.5.10 Quarter amplitude oscillation tuning -- 1.5.11 SCM tuning for self-regulating processes -- 1.5.12 SCM tuning for integrating processes -- 1.5.13 SCM tuning for runaway processes -- 1.5.14 Maximizing absorption of variability tuning for surge tank level -- 1.6 Test results -- 1.6.1 Performance of tuning settings on dead time dominant processes -- 1.6.2 Performance of tuning settings on near-integrating processes -- 1.6.3 Performance of tuning settings on true integrating processes -- 1.6.4 Performance of tuning settings on runaway processes -- 1.6.5 Slow oscillations from low PID gain in integrating and runaway processes -- 1.6.6 Performance of tuning methods on various processes -- Key points --
2. Unified methodology -- 2.1 Introduction -- 2.1.1 Perspective -- 2.1.2 Overview -- 2.1.3 Recommendations -- 2.2 PID features -- 2.2.1 PID form -- 2.2.2 External reset feedback -- 2.2.3 PID structure -- 2.2.4 Split range -- 2.2.5 Signal characterization -- 2.2.6 Feedforward -- 2.2.7 Decoupling -- 2.2.8 Output tracking and remote output -- 2.2.9 Setpoint filter, lead-lag, and rate limits -- 2.2.10 Enhanced PID for wireless and analyzers -- 2.3 Automation system difficulties -- 2.3.1 Open loop gain problems -- 2.3.2 Time constant problems -- 2.3.3 Dead time problems -- 2.3.4 Limit cycle problems -- 2.3.5 Noise problems -- 2.3.6 Accuracy and precision problems -- 2.4 Process objectives -- 2.4.1 Maximize turndown -- 2.4.2 Maximize safety and environmental protection -- 2.4.3 Minimize product variability -- 2.4.4 Maximize process efficiency and capacity -- 2.5 Step-by-step solutions -- 2.6 Test results -- Key points -- 3. Performance criteria -- 3.1 Introduction -- 3.1.1 Perspective -- 3.1.2 Overview -- 3.1.3 Recommendations -- 3.2 Disturbance response metrics -- 3.2.1 Accumulated error -- 3.2.2 Peak error -- 3.2.3 Disturbance lag -- 3.3 Setpoint response metrics -- 3.3.1 Rise time -- 3.3.2 Overshoot and undershoot -- Key points -- 4. Effect of process dynamics -- 4.1 Introduction -- 4.1.1 Perspective -- 4.1.2 Overview -- 4.1.3 Recommendations -- 4.2 Effect of mechanical design -- 4.2.1 Equipment and piping dynamics -- 4.2.2 Common equipment and piping design mistakes -- 4.3 Estimation of total dead time -- 4.4 Estimation of open loop gain -- 4.5 Major types of process responses -- 4.5.1 Self-regulating processes -- 4.5.2 Integrating processes -- 4.5.3 Runaway processes -- 4.6 Examples -- 4.6.1 Waste treatment pH loops (self-regulating process) -- 4.6.2 Boiler feedwater flow loop (self-regulating process) -- 4.6.3 Boiler drum level loop (integrating process) -- 4.6.4 Furnace pressure loop (near-integrating process) -- 4.6.5 Exothermic reactor cascade temperature loop (runaway process) -- 4.6.6 Biological reactor biomass concentration loop (runaway process) -- Key points -- 5. Effect of controller dynamics -- 5.1 Introduction -- 5.1.1 Perspective -- 5.1.2 Overview -- 5.1.3 Recommendations -- 5.2 Execution rate and filter time -- 5.2.1 First effect via equation for integrated error -- 5.2.2 Second effect via equations for implied dead time -- 5.3 Smart reset action -- 5.4 Diagnosis of tuning problems -- 5.5 Furnace pressure loop example (near-integrating) -- 5.6 Test results -- Key points -- 6. Effect of measurement dynamics -- 6.1 Introduction -- 6.1.1 Perspective -- 6.1.2 Overview -- 6.1.3 Recommendations -- 6.2 Wireless update rate and transmitter damping -- 6.2.1 First effect via equation for integrated error -- 6.2.2 Second effect via equations for implied dead time -- 6.3 Analyzers -- 6.4 Sensor lags and delays -- 6.5 Noise and repeatability -- 6.6 Threshold sensitivity and resolution limits -- 6.7 Rangeability (turndown) -- 6.8 Runaway processes -- 6.9 Accuracy, precision, and drift -- 6.10 Attenuation and deception -- 6.11 Examples -- 6.11.1 Waste treatment pH loop (self-regulating process) -- 6.11.2 Boiler feedwater flow loop (self-regulating process) -- 6.11.3 Boiler drum level loop (integrating process) -- 6.11.4 Furnace pressure loop (near-integrating process) -- 6.11.5 Exothermic reactor cascade temperature loop (runaway process) -- 6.11.6 Biological reactor biomass concentration loop (runaway process) -- 6.12 Test results -- Key points -- 7. Effect of valve and variable frequency drive dynamics -- 7.1 Introduction -- 7.1.1 Perspective -- 7.1.2 Overview -- 7.1.3 Recommendations -- 7.2 Valve positioners and accessories -- 7.2.1 Pneumatic positioners -- 7.2.2 Digital positioners -- 7.2.3 Current to pneumatic (I/P) transducers -- 7.2.4 Solenoid valves -- 7.2.5 Volume boosters -- 7.3 Actuators, shafts, and stems -- 7.3.1 Diaphragm actuators -- 7.3.2 Piston actuators -- 7.3.3 Linkages and connections -- 7.4 VFD system design -- 7.4.1 Pulse width modulation -- 7.4.2 Cable problems -- 7.4.3 Bearing problems -- 7.4.4 Speed slip -- 7.4.5 Motor requirements -- 7.4.6 Drive controls -- 7.5 Dynamic response -- 7.5.1 Control valve response -- 7.5.2 VFD response -- 7.5.3 Dead time approximation -- 7.5.4 Deadband and resolution -- 7.5.5 When is a valve or VFD too slow? -- 7.5.6 Limit cycles -- 7.6 Installed flow characteristics and rangeability -- 7.6.1 Valve flow characteristics -- 7.6.2 Valve rangeability -- 7.6.3 VFD flow characteristics -- 7.6.4 VFD rangeability -- 7.7 Best practices -- 7.7.1 Control valve design specifications -- 7.7.2 VFD design specifications -- 7.8 Test results -- Key points -- 8. Effect of disturbances -- 8.1 Introduction -- 8.1.1 Perspective -- 8.1.2 Overview -- 8.1.3 Recommendations -- 8.2 Disturbance dynamics -- 8.2.1 Load time constants -- 8.2.2 Load rate limit -- 8.2.3 Disturbance dead time -- 8.2.4 Disturbance oscillations -- 8.3 Disturbance location -- 8.4 Disturbance troubleshooting -- 8.4.1 Sources of fast oscillations -- 8.4.2 Sources of slow oscillations -- 8.5 Disturbance mitigation -- 8.6 Test results -- Key points -- 9. Effect of nonlinearities -- 9.1 Introduction -- 9.1.1 Perspective -- 9.1.2 Overview -- 9.1.3 Recommendations -- 9.2 Variable gain -- 9.2.1 Cascade control -- 9.2.2 Reversals of process sign -- 9.2.3 Signal characterization -- 9.2.4 Gain scheduling -- 9.2.5 Adaptive control -- 9.2.6 Gain margin -- 9.3 Variable dead time -- 9.4 Variable time constant -- 9.5 Inverse response -- 9.6 Test results -- Key points -- 10. Effect of interactions -- 10.1 Introduction -- 10.1.1 Perspective -- 10.1.2 Overview -- 10.1.3 Recommendations -- 10.2 Pairing -- 10.2.1 Relative gain array -- 10.2.2 Distillation column example -- 10.2.3 Static mixer example -- 10.2.4 Hidden control loops -- 10.2.5 Relative gains less than zero -- 10.2.6 Relative gains from zero to one -- 10.2.7 Relative gains greater than one -- 10.2.8 Model predictive control -- 10.3 Decoupling -- 10.4 Directional move suppression -- 10.5 Tuning -- 10.6 Test results -- Key points -- 11. Cascade control -- 11.1 Introduction -- 11.1.1 Perspective -- 11.1.2 Overview -- 11.1.3 Recommendations -- 11.2 Configuration and tuning -- 11.3 Process control benefits -- 11.4 Process knowledge benefits -- 11.5 Watch-outs -- 11.6 Test results -- Key points -- 12. Advanced regulatory control -- 12.1 Introduction -- 12.1.1 Perspective -- 12.1.2 Overview -- 12.1.3 Recommendations -- 12.2 Feedforward control -- 12.2.1 Opportunities -- 12.2.2 Watch-outs -- 12.3 Intelligent output action -- 12.3.1 Opportunities -- 12.3.2 Watch-outs -- 12.4 Intelligent integral action -- 12.4.1 Opportunities -- 12.4.2 Watch-outs -- 12.5 Dead time compensation -- 12.5.1 Opportunities -- 12.5.2 Watch-outs -- 12.6 Valve position control -- 12.6.1 Opportunities -- 12.6.2 Watch-outs -- 12.7 Override control -- 12.7.1 Opportunities -- 12.7.2 Watch-outs -- 12.8 Test results -- Key points -- 13. Process control improvement -- 13.1 Introduction -- 13.1.1 Perspective -- 13.1.2 Overview -- 13.1.3 Recommendations -- 13.2 Unit operation metrics -- 13.3 Opportunities -- 13.3.1 Variability -- 13.3.2 Increasing capacity and efficiency -- 13.3.3 Effective use of models -- 13.3.4 Sizing and assessment -- 13.4 Key questions -- Key points -- 14. Auto tuners and adaptive control -- 14.1 Introduction -- 14.1.1 Perspective -- 14.1.2 Overview -- 14.1.3 Recommendations -- 14.2 Methodology -- Key points -- 15. Batch optimization -- 15.1 Introduction -- 15.1.1 Perspective -- 15.1.2 Overview -- 15.1.3 Recommendations -- 15.2 Cycle time -- 15.3 Profile -- 15.4 End point -- Key points -- Appendix A. Automation system performance top 10 concepts -- Appendix B. Basics of PID controllers -- Appendix C. Controller performance -- Appendix D. Discussion -- Appendix E. Enhanced PID for wireless and analyzer applications -- Appendix F. First principle process relationships -- Appendix G. Gas pressure dynamics -- Appendix H. Convective heat transfer coefficients -- Appendix I. Interactive to noninteractive time constant conversion -- Appendix. Jacket and coil temperature control -- Appendix K. PID forms and conversion of tuning settings -- Appendix L. Liquid mixing dynamics -- Appendix M. Measurement speed requirements for SIS -- References -- Bibliography -- About the author -- Index. |
| Record Nr. | UNINA-9910459794203321 |
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McMillan Gregory K. <1946-, >
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| New York, [New York] (222 East 46th Street, New York, NY 10017) : , : Momentum Press, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Tuning and control loop performance / / Gregory K. McMillan
| Tuning and control loop performance / / Gregory K. McMillan |
| Autore | McMillan Gregory K. <1946-, > |
| Edizione | [Fourth edition.] |
| Pubbl/distr/stampa | New York, [New York] (222 East 46th Street, New York, NY 10017) : , : Momentum Press, , 2015 |
| Descrizione fisica | 1 online resource (584 pages) |
| Disciplina | 629.83 |
| Collana | Manufacturing and engineering collection |
| Soggetto topico |
Process control
Feedback control systems |
| Soggetto non controllato |
adaptive control
advanced regulatory control analyzer response auto tuner automation system batch optimization bioreactor control cascade control compressor control control loop performance control valve response external reset feedback feedforward control inverse response lambda tuning level control measurement response pH control PID control PID execution rate PID filter PID form PID structure PID tuning pressure control process control process disturbances process dynamics process interaction process metrics process nonlinearity process performance process response proportional-integral-derivative controller reactor control runaway reaction temperature control valve deadband valve position control valve resolution variable frequency drive response wireless control wireless response |
| ISBN | 1-60650-171-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
1. Fundamentals -- 1.1 Introduction -- 1.1.1 Perspective -- 1.1.2 Overview -- 1.1.3 Recommendations -- 1.2 PID controller -- 1.2.1 Proportional mode -- 1.2.2 Integral mode -- 1.2.3 Derivative mode -- 1.2.4 ARW and output limits -- 1.2.5 Control action and valve action -- 1.2.6 Operating modes -- 1.3 Loop dynamics -- 1.3.1 Types of process responses -- 1.3.2 Dead times and time constants -- 1.3.3 Open loop self-regulating and integrating process gains -- 1.3.4 Deadband, resolution, and threshold sensitivity -- 1.4 Typical mode settings -- 1.5 Typical tuning methods -- 1.5.1 Lambda tuning for self-regulating processes -- 1.5.2 Lambda tuning for integrating processes -- 1.5.3 IMC tuning for self-regulating processes -- 1.5.4 IMC tuning for integrating processes -- 1.5.5 Skogestad internal model control tuning for self-regulating processes -- 1.5.6 SIMC tuning for integrating processes -- 1.5.7 Traditional open loop tuning -- 1.5.8 Modified Ziegler-Nichols reaction curve tuning -- 1.5.9 Modified Ziegler-Nichols ultimate oscillation tuning -- 1.5.10 Quarter amplitude oscillation tuning -- 1.5.11 SCM tuning for self-regulating processes -- 1.5.12 SCM tuning for integrating processes -- 1.5.13 SCM tuning for runaway processes -- 1.5.14 Maximizing absorption of variability tuning for surge tank level -- 1.6 Test results -- 1.6.1 Performance of tuning settings on dead time dominant processes -- 1.6.2 Performance of tuning settings on near-integrating processes -- 1.6.3 Performance of tuning settings on true integrating processes -- 1.6.4 Performance of tuning settings on runaway processes -- 1.6.5 Slow oscillations from low PID gain in integrating and runaway processes -- 1.6.6 Performance of tuning methods on various processes -- Key points --
2. Unified methodology -- 2.1 Introduction -- 2.1.1 Perspective -- 2.1.2 Overview -- 2.1.3 Recommendations -- 2.2 PID features -- 2.2.1 PID form -- 2.2.2 External reset feedback -- 2.2.3 PID structure -- 2.2.4 Split range -- 2.2.5 Signal characterization -- 2.2.6 Feedforward -- 2.2.7 Decoupling -- 2.2.8 Output tracking and remote output -- 2.2.9 Setpoint filter, lead-lag, and rate limits -- 2.2.10 Enhanced PID for wireless and analyzers -- 2.3 Automation system difficulties -- 2.3.1 Open loop gain problems -- 2.3.2 Time constant problems -- 2.3.3 Dead time problems -- 2.3.4 Limit cycle problems -- 2.3.5 Noise problems -- 2.3.6 Accuracy and precision problems -- 2.4 Process objectives -- 2.4.1 Maximize turndown -- 2.4.2 Maximize safety and environmental protection -- 2.4.3 Minimize product variability -- 2.4.4 Maximize process efficiency and capacity -- 2.5 Step-by-step solutions -- 2.6 Test results -- Key points -- 3. Performance criteria -- 3.1 Introduction -- 3.1.1 Perspective -- 3.1.2 Overview -- 3.1.3 Recommendations -- 3.2 Disturbance response metrics -- 3.2.1 Accumulated error -- 3.2.2 Peak error -- 3.2.3 Disturbance lag -- 3.3 Setpoint response metrics -- 3.3.1 Rise time -- 3.3.2 Overshoot and undershoot -- Key points -- 4. Effect of process dynamics -- 4.1 Introduction -- 4.1.1 Perspective -- 4.1.2 Overview -- 4.1.3 Recommendations -- 4.2 Effect of mechanical design -- 4.2.1 Equipment and piping dynamics -- 4.2.2 Common equipment and piping design mistakes -- 4.3 Estimation of total dead time -- 4.4 Estimation of open loop gain -- 4.5 Major types of process responses -- 4.5.1 Self-regulating processes -- 4.5.2 Integrating processes -- 4.5.3 Runaway processes -- 4.6 Examples -- 4.6.1 Waste treatment pH loops (self-regulating process) -- 4.6.2 Boiler feedwater flow loop (self-regulating process) -- 4.6.3 Boiler drum level loop (integrating process) -- 4.6.4 Furnace pressure loop (near-integrating process) -- 4.6.5 Exothermic reactor cascade temperature loop (runaway process) -- 4.6.6 Biological reactor biomass concentration loop (runaway process) -- Key points -- 5. Effect of controller dynamics -- 5.1 Introduction -- 5.1.1 Perspective -- 5.1.2 Overview -- 5.1.3 Recommendations -- 5.2 Execution rate and filter time -- 5.2.1 First effect via equation for integrated error -- 5.2.2 Second effect via equations for implied dead time -- 5.3 Smart reset action -- 5.4 Diagnosis of tuning problems -- 5.5 Furnace pressure loop example (near-integrating) -- 5.6 Test results -- Key points -- 6. Effect of measurement dynamics -- 6.1 Introduction -- 6.1.1 Perspective -- 6.1.2 Overview -- 6.1.3 Recommendations -- 6.2 Wireless update rate and transmitter damping -- 6.2.1 First effect via equation for integrated error -- 6.2.2 Second effect via equations for implied dead time -- 6.3 Analyzers -- 6.4 Sensor lags and delays -- 6.5 Noise and repeatability -- 6.6 Threshold sensitivity and resolution limits -- 6.7 Rangeability (turndown) -- 6.8 Runaway processes -- 6.9 Accuracy, precision, and drift -- 6.10 Attenuation and deception -- 6.11 Examples -- 6.11.1 Waste treatment pH loop (self-regulating process) -- 6.11.2 Boiler feedwater flow loop (self-regulating process) -- 6.11.3 Boiler drum level loop (integrating process) -- 6.11.4 Furnace pressure loop (near-integrating process) -- 6.11.5 Exothermic reactor cascade temperature loop (runaway process) -- 6.11.6 Biological reactor biomass concentration loop (runaway process) -- 6.12 Test results -- Key points -- 7. Effect of valve and variable frequency drive dynamics -- 7.1 Introduction -- 7.1.1 Perspective -- 7.1.2 Overview -- 7.1.3 Recommendations -- 7.2 Valve positioners and accessories -- 7.2.1 Pneumatic positioners -- 7.2.2 Digital positioners -- 7.2.3 Current to pneumatic (I/P) transducers -- 7.2.4 Solenoid valves -- 7.2.5 Volume boosters -- 7.3 Actuators, shafts, and stems -- 7.3.1 Diaphragm actuators -- 7.3.2 Piston actuators -- 7.3.3 Linkages and connections -- 7.4 VFD system design -- 7.4.1 Pulse width modulation -- 7.4.2 Cable problems -- 7.4.3 Bearing problems -- 7.4.4 Speed slip -- 7.4.5 Motor requirements -- 7.4.6 Drive controls -- 7.5 Dynamic response -- 7.5.1 Control valve response -- 7.5.2 VFD response -- 7.5.3 Dead time approximation -- 7.5.4 Deadband and resolution -- 7.5.5 When is a valve or VFD too slow? -- 7.5.6 Limit cycles -- 7.6 Installed flow characteristics and rangeability -- 7.6.1 Valve flow characteristics -- 7.6.2 Valve rangeability -- 7.6.3 VFD flow characteristics -- 7.6.4 VFD rangeability -- 7.7 Best practices -- 7.7.1 Control valve design specifications -- 7.7.2 VFD design specifications -- 7.8 Test results -- Key points -- 8. Effect of disturbances -- 8.1 Introduction -- 8.1.1 Perspective -- 8.1.2 Overview -- 8.1.3 Recommendations -- 8.2 Disturbance dynamics -- 8.2.1 Load time constants -- 8.2.2 Load rate limit -- 8.2.3 Disturbance dead time -- 8.2.4 Disturbance oscillations -- 8.3 Disturbance location -- 8.4 Disturbance troubleshooting -- 8.4.1 Sources of fast oscillations -- 8.4.2 Sources of slow oscillations -- 8.5 Disturbance mitigation -- 8.6 Test results -- Key points -- 9. Effect of nonlinearities -- 9.1 Introduction -- 9.1.1 Perspective -- 9.1.2 Overview -- 9.1.3 Recommendations -- 9.2 Variable gain -- 9.2.1 Cascade control -- 9.2.2 Reversals of process sign -- 9.2.3 Signal characterization -- 9.2.4 Gain scheduling -- 9.2.5 Adaptive control -- 9.2.6 Gain margin -- 9.3 Variable dead time -- 9.4 Variable time constant -- 9.5 Inverse response -- 9.6 Test results -- Key points -- 10. Effect of interactions -- 10.1 Introduction -- 10.1.1 Perspective -- 10.1.2 Overview -- 10.1.3 Recommendations -- 10.2 Pairing -- 10.2.1 Relative gain array -- 10.2.2 Distillation column example -- 10.2.3 Static mixer example -- 10.2.4 Hidden control loops -- 10.2.5 Relative gains less than zero -- 10.2.6 Relative gains from zero to one -- 10.2.7 Relative gains greater than one -- 10.2.8 Model predictive control -- 10.3 Decoupling -- 10.4 Directional move suppression -- 10.5 Tuning -- 10.6 Test results -- Key points -- 11. Cascade control -- 11.1 Introduction -- 11.1.1 Perspective -- 11.1.2 Overview -- 11.1.3 Recommendations -- 11.2 Configuration and tuning -- 11.3 Process control benefits -- 11.4 Process knowledge benefits -- 11.5 Watch-outs -- 11.6 Test results -- Key points -- 12. Advanced regulatory control -- 12.1 Introduction -- 12.1.1 Perspective -- 12.1.2 Overview -- 12.1.3 Recommendations -- 12.2 Feedforward control -- 12.2.1 Opportunities -- 12.2.2 Watch-outs -- 12.3 Intelligent output action -- 12.3.1 Opportunities -- 12.3.2 Watch-outs -- 12.4 Intelligent integral action -- 12.4.1 Opportunities -- 12.4.2 Watch-outs -- 12.5 Dead time compensation -- 12.5.1 Opportunities -- 12.5.2 Watch-outs -- 12.6 Valve position control -- 12.6.1 Opportunities -- 12.6.2 Watch-outs -- 12.7 Override control -- 12.7.1 Opportunities -- 12.7.2 Watch-outs -- 12.8 Test results -- Key points -- 13. Process control improvement -- 13.1 Introduction -- 13.1.1 Perspective -- 13.1.2 Overview -- 13.1.3 Recommendations -- 13.2 Unit operation metrics -- 13.3 Opportunities -- 13.3.1 Variability -- 13.3.2 Increasing capacity and efficiency -- 13.3.3 Effective use of models -- 13.3.4 Sizing and assessment -- 13.4 Key questions -- Key points -- 14. Auto tuners and adaptive control -- 14.1 Introduction -- 14.1.1 Perspective -- 14.1.2 Overview -- 14.1.3 Recommendations -- 14.2 Methodology -- Key points -- 15. Batch optimization -- 15.1 Introduction -- 15.1.1 Perspective -- 15.1.2 Overview -- 15.1.3 Recommendations -- 15.2 Cycle time -- 15.3 Profile -- 15.4 End point -- Key points -- Appendix A. Automation system performance top 10 concepts -- Appendix B. Basics of PID controllers -- Appendix C. Controller performance -- Appendix D. Discussion -- Appendix E. Enhanced PID for wireless and analyzer applications -- Appendix F. First principle process relationships -- Appendix G. Gas pressure dynamics -- Appendix H. Convective heat transfer coefficients -- Appendix I. Interactive to noninteractive time constant conversion -- Appendix. Jacket and coil temperature control -- Appendix K. PID forms and conversion of tuning settings -- Appendix L. Liquid mixing dynamics -- Appendix M. Measurement speed requirements for SIS -- References -- Bibliography -- About the author -- Index. |
| Record Nr. | UNINA-9910787493803321 |
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McMillan Gregory K. <1946-, >
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| New York, [New York] (222 East 46th Street, New York, NY 10017) : , : Momentum Press, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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