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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 electronic resource (288 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
spatial-temporal data
pasting process process image convolutional neural network Industry 4.0 auto machine learning failure mode effects analysis risk priority number rolling bearing condition monitoring classification OPTICS statistical process control control chart pattern disruptions disruption management fault diagnosis construction industry plaster production neural networks decision support systems expert systems failure mode and effects analysis (FMEA) discriminant analysis non-intrusive load monitoring load identification membrane data reconciliation real-time online monitoring Six Sigma multivariate data analysis latent variables models PCA PLS high-dimensional data statistical process monitoring artificial generation of variability data augmentation quality prediction continuous casting multiscale time series classification imbalanced data combustion optical sensors spectroscopy measurements signal detection digital processing principal component analysis curve resolution data mining semiconductor manufacturing quality control yield improvement fault detection process control multi-phase residual recursive model multi-mode model process monitoring |
| 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 electronic resource (514 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
process control
separation oil and gas control chart fuzzy logic neutrosophic statistic incomplete data belief statistic gamma distribution microalgae raceway control problem PID event-based flexible flow shop scheduling multi-queue limited buffers improved compact genetic algorithm probability density function of the Gaussian distribution transfer bees optimizer reinforcement learning behavior transfer state-action chains reactive power optimization Holmquist-Johnson-Cook constitutive model of briquette parameter acquisition split Hopkinson pressure bar experiment numerical simulation pressure swing distillation full-heat integration acetonitrile water distributed model predictive control steam power plant steam/water loop multi-input and multi-output system loop design optimal nonlinear adaptive control memetic salp swarm algorithm voltage source converter perturbation observer hardware experiment MIMO temperature control in heating process system pole-zero cancelation temperature difference transient response dead time slow-mode-based control multi-input multi-output (MIMO) temperature system temperature differences non-pillar gob-side entry retaining by roof cutting close distance coal seams goaf stress distribution semiconductor bonding equipment-grouping method graph theory association matrix CFSFDP algorithm self-learning variable geometry turbocharger deep reinforcement learning deep deterministic policy gradient flexible flow shop limited buffer public buffer Hopfield neural network local scheduling simulated annealing algorithm load identification EWT multiscale fuzzy entropy PNN Hybrid Attack Graph Level-of-Resilience stability topology distributed generation (DG) INSGA-II multi-objective optimization potential crowding distance static and dynamic planning data-driven methods coarse model refrigeration tunnel boring machine roadway supporting constitutive model failure criterion in-situ monitoring power systems complex network theory Fast-Newman algorithm link-addition strategy cascading failures flotation process reagent dosage time series froth image cumulative distribution function fault diagnosis fault classification fast Fourier transform (FFT) multi-linear principal component analysis (MPCA) uncorrelated multi-linear principal component analysis (UMPCA) additive white Gaussian noises (AWGN) wind turbine systems deviation control drilling machine nonlinear adaptive backstepping controller disturbance observer parameter uncertainties wind turbine energy conversion systems condition monitoring fault prognosis resilient control |
| 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 electronic resource (254 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
input-output model
fuzzy optimization process synthesis preliminary stage design process systems engineering energy systems engineering process design optimization nonlinear programming process monitoring nonlinear principal component analysis parallel neural networks autoassociative neural network big data process scheduling process system engineering mixed-integer programming scheduling process control integration distribution planning oil supply chain robust optimization uncertainty bio-jet diesel co-hydrotreating hydrodesulphurisation hydrodeoxigenation reactive distillation coproduction Lurgi syngas cryogenic separation methanol synthesis LNG symmetry quadratic optimization quadratically-constrained quadratic optimization process modeling mathematical programming MINLP generalized disjunctive programming design higher education curricula 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 electronic resource (306 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
Phoronomics
mechanics kinetics kinematics direction cosines Euler angles space dynamics digital computation control systems control engineering industrial cyber-physical systems cyber-attacks neural network model predictive control nonlinear chemical processes network classification network evolution network symmetries Green’s symmetry problem network invariants network internal dynamics symmetry ensembles propensities energy opening capability security smart grid group signature anonymous signature autonomous vehicle target detection multi-sensors fusion YOLO linear response eigenvalue problem block methods weighted Golub-Kahan-Lanczos algorithm convergence analysis thick restart mining vibrating screen theoretical rigid body model spring failures diagnosis amplitudes change real-time optimization nonlinear processes process control chemical reactor control distillation column control energy-based control payload swing attenuation linear matrix inequalities quadrotor larger Lipschitz constants comparative advantage transaction cost specialized production infra-marginal model agricultural division of labor velocity-slip temperature-jump homotopy analysis method nanofluids power-law fluids state estimation parameter estimation moving horizon estimation extended kalman filter ensemble kalman filter richards equation agro-hydrological systems economic model predictive control chemical processes responsive control artificial intelligence interpretability controller verification seashore soft soil cement sulfuric acid erosion stress–strain behavior mathematical model Micro Grid optimization theory optimization MATLAB simulation |
| 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
| Modeling and Simulation of Energy Systems |
| Autore | Adams II Thomas A |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (496 p.) |
| 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 | 3-03921-519-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367747603321 |
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Adams II 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
| Product/Process Fingerprint in Micro Manufacturing |
| Autore | Tosello Guido |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (274 p.) |
| 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 | 3-03921-035-1 |
| 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 electronic resource (230 p.) |
| Soggetto non controllato |
hollow fiber membranes
modeling small sized powdered ferric hydroxide ceramic membranes multiphase antimony treatment winery effluents fouling municipal wastewater sludge particle size distribution ultrafiltration coagulation HA–BSA mixtures Sb(III) water treatment chromate separation backwash duration water crisis adsorption dewatering hexavalent chromium arsenic adsorption polydimethylsiloxane vacuum membrane distillation co-treatment desalination arsenic mass transfer produced water treatment membranes process control membrane resistance granular ferric hydroxide ozonation Membrane Bioreactor hollow fibre crossflow membrane filtration microfiltration membrane filtration xDLVO theory Fe-based coagulants wastewater treatment cake resistance Sb(V) peroxone temperature drinking water trickling biofilter natural organic matter (NOM) antimony second cheese whey sodium alginate optical sensors bioethanol recovery membrane fouling interaction energy TMP adsorption kinetics water matrix multivariate statistics polluted waters natural organic matter operation solution conditions biofilm membrane bioreactor |
| 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 electronic resource (222 p.) |
| Soggetto topico |
Research & information: general
Technology: general issues |
| Soggetto non controllato |
hydrochar
hydrothermal carbonization biogas upgrading CO2 capture pressure swing adsorption gasification kinetic model conversion model reaction model low-rank coal tar absorption process simulation validation study sensitivity analyses lignite lignite gasification fluidized-bed gasifier olivine solar cooling solar cooling system TRNSYS absorption chiller performance and analysis solar energy chemical looping biomass gasification process control CO2 absorption experimental study energy analysis exergy analysis CSP PTC ISCC power plant CFB combustion operational flexibility load transients fluctuating electricity generation renewables one-dimensional SEG model dual fluidized bed sorbent deactivation hydrodynamics kinetics fuel feeding rate biomass thermochemical conversion technologies combustion carbon capture and storage/utilization solar-driven air-conditioning integrated solar combined cycle energy and exergy analyses thermodynamic modeling dynamic process simulation |
| 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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