Download Batch fermentation: modeling, monitoring, and control by Ali Cinar, Satish J. Parulekar, Cenk Undey, Gulnur Birol PDF

By Ali Cinar, Satish J. Parulekar, Cenk Undey, Gulnur Birol

Bargains beneficial simulation and keep watch over techniques for batch fermentation purposes within the nutrition, pharmaceutical, and chemical industries. more advantageous through a corresponding web site detailing the newest advances within the box, in addition to helpful updates to the cloth. offers techniques for opting for optimum reference trajectories and working stipulations.

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Additional resources for Batch fermentation: modeling, monitoring, and control

Example text

These metabolites are usually referred to as secondary metabolites. The growth phase is followed by a stationary phase upon near complete exhaustion of one or more nutrients essential for cell growth. Synthesis of secondary metabolites is usually promoted in the stationary phase.

For example, spaghetti should not be added to water that is not hot enough, otherwise the strings will stick to each other. A good landmark is boiling of water which can be detected easily as opposed to water temperature reaching 200 °F. The latter would work equally well for the cooking operation but will be more difficult to detect, monitor (a thermometer would be needed) and regulate. The duration of keeping the spaghetti in hot water will change because of many factors. These include the relative amounts of water and spaghetti (the initial charge of ingredients), the tenderness of cooked spaghetti (a quality variable that varies with personal taste and weight watching - it is said that absorption of the carbohydrates by the body increases as the spaghetti gets tender), type of spaghetti flour (whole wheat or bleached flour), and the amount of heat provided (one can turn the heat off and keep the strings in hot water longer).

4) GAM Generalized additive model GC Gas chromatography GLR Generalized likelihood ratio GUI Graphical user interface HMM Hidden Markov model HPCA Hierarchical principal components analysis HPLC High pressure liquid chromatography HPLS Hierarchical partial least squares ILC Iterative Learning Control IO Innovational outlier IV Indicator variable KBS Knowledge-based System LCL Lower control limit Copyright © 2003 by Taylor & Francis Group, LLC Nomenclature LMS Least median squares LPV Linear parameter varying LQC Linear quadratic Gaussian control LTS Least trimmed squares LTV Linear time varying LV Latent variable LWL Lower warning limit MA Moving average MAC Model algorithmic control MARS Multivariate adaptive regression splines MIMO Multiple-input, multiple-output control/system MFC Model predictive control NMPC Nonlinear model predictive control MBO Model-based optimization MBPCA Multiblock principal components analysis MBPLS Multiblock partial least squares MCA Metabolic control analysis MFA Metabolic flux analysis MHBE Moving horizon Bayesian estimator MIMO Multi-input multi-output MLE Maximum likelihood estimate MLR Multiple linear regression MOBECS Model-Object Based Expert Control System MPCA Multiway principal component analysis MPLS Multiway partial least squares mRNA Messenger ribonucleic acid Copyright © 2003 by Taylor & Francis Group, LLC xxix xxx Nomenclature MS Mass spectrometer MSB Least squares mean squared error MSMPCA Multiscale Multiway principal component analysis MSPM Multivariate statistical process monitoring MV Multivariate NAR Nonlinear auto regressive NARMAX Nonlinear autoregressive moving average with exogenous inputs NLTS Nonlinear time series NO Normal operation NOC Normal operating conditions NPETM Nonlinear polynomial models with exponential and trigonometric functions OD Optical density OE Output error OVAT One-variable-at-a-time PARAFAC Parallel factor analysis PC Principal component PCA Principal components analysis PCD Parameter change detection (method) PCR Principal components regression PDA Principal differential analysis PDF Probability distribution function PLS Partial least squares (Projection to latent structures) PRESS Prediction sum of squares PSSE Penalized sum of squared error PSSH Pseudo-steady state synthesis Copyright © 2003 by Taylor & Francis Group, LLC Nomenclature QQ Quantile-Quantile RGA Relative gain array RQ Respiratory quotient RTKBS Real-time knowledge-based systems RVWLS Recursive variable weighted least squares RWLS Recursive weighted least-squares SISO Single-input single-output SNR Signal-to-noise ratio SPC Statistical process control SPE Squared prediction error SPM Statistical process monitoring SS Sum of squares SSE Sum of squares explained SSR Regression sum of squares SSY Sum of squares on Y-block STFT Short-time Fourier transform SV Singular values SVD Singular value decomposition TFM Transfer function matrix UCL Upper control limit UWL Upper warning limit VIP Variable influence on projection Copyright © 2003 by Taylor & Francis Group, LLC xxxi Introduction Batch processes have been around for many millennia, probably since the beginning of human civilization.

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