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|a 629.8312
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|a Venkateswarlu, Ch.
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|a Optimal state estimation for process monitoring, fault diagnosis and control /
|c Ch. Venkateswarlu and Rama Rao Karri.
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|a Amsterdam :
|b Elsevier,
|c 2022.
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|a 1 online resource (400 pages)
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
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|a Print version record.
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|a Part I -- BASIC DETAILS AND STATE ESTIMATION ALGORITHMS 1.?Optimal state estimation and its importance in process systems engineering 2.?Stochastic process and filtering theory 3.?Linear filtering and observation techniques with examples 4.?Mechanistic model-based nonlinear filtering and observation techniques for state estimation 5.?Data-driven modelling techniques for state estimation 6.?Optimal sensor configuration methods for state estimation Part II -- APPLICATION OF MECHANISTIC MODEL-BASED NONLINEAR FILTERING AND OBSERVATION TECHNIQUES FOR STATE ESTIMATION IN CHEMICAL PROCESSES 7.?Optimal state estimation in multicomponent batch distillation 8.?Optimal state estimation in multicomponent reactive batch distillation with optimal sensor configuration 9.?Optimal state estimation in complex nonlinear dynamical systems 10.?Optimal state estimation of a kraft pulping digester? 11.?Optimal State Estimation of a High Dimensional Fluid Catalytic Cracking Unit 12.?Optimal state estimation of continuous distillation column with optimal sensor configuration 13.?Optimal state and parameter estimation in nonlinear CSTR Part III -- APPLICATION OF QUANTITATIVE MODEL-BASED NONLINEAR FILTERING AND OBSERVATION TECHNIQUES FOR STATE ESTIMATION IN BIOCHEMICAL PROCESSES 14.?Optimal state and parameter estimation in the nonlinear batch beer fermentation process 15.?Optimal state and parameter estimation for online optimization of an uncertain biochemical reactor Part IV -- APPLICATION OF DATA-DRIVEN MODEL-BASED TECHNIQUES FOR STATE ESTIMATION IN CHEMICAL PROCESSES 16.?Data-driven methods for state estimation in multi-component batch distillation 17.?Hybrid schemes for state estimation 18.?Future development, prospective and challenges in the application of soft sensors in industrial applications
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| 650 |
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|a Process control
|x Mathematics.
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| 650 |
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0 |
|a Mathematical optimization.
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| 650 |
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6 |
|a Fabrication
|0 (CaQQLa)201-0039314
|x Contr�ole
|0 (CaQQLa)201-0039314
|x Math�ematiques.
|0 (CaQQLa)201-0380112
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| 650 |
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6 |
|a Optimisation math�ematique.
|0 (CaQQLa)201-0007680
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| 650 |
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7 |
|a Mathematical optimization
|2 fast
|0 (OCoLC)fst01012099
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| 650 |
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7 |
|a Process control
|x Mathematics
|2 fast
|0 (OCoLC)fst01078033
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| 700 |
1 |
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|a Karri, Rama Rao.
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| 776 |
0 |
8 |
|i Print version:
|a Venkateswarlu, Ch.
|t Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control.
|d San Diego : Elsevier, �2022
|z 9780323858786
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| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780323858786
|z Texto completo
|
