Online parameters

Figure 7.48 Reaction of the switched online parameter and state estimation scheme to a change in one Henry parameter of column 3 (Kupperand Engell, 2006).

Kosaka R., Sankai Y., likuya T., Yamane T., and Tsutsui T. 2004. Resonant frequency control for artificial heart using online parameter identification. Artif. Organs. 28 921-6. 

The parameter estimation presented in the previous section is based on a least squares minimisation of the errors between measured system outputs and outputs of a system model evaluated by using estimated parameter values. If the real system is replaced by a model in preferred integral causality, measured outputs can be obtained by solving the model equations for given initial conditions and can be used for offline parameter estimation in order to isolate multiple faults deliberately introduced into the system model. In real-time FDI, initial conditions are either not known or difficult to obtain. Therefore, in online parameter estimation, they have to be considered as additional unknowns that are to be estimated. 

Knowledge of the physiological state of cells and the physicochemical conditions of the bioreactor environment is required for an optimal bioprocess control. The analysis of bioprocesses is based on physical, chemical, and biological parameters. A selection of offline and online parameters, which are used for the analysis of bioprocesses, is shown in Table 1. 

Figure A2.1 Waters ProMonix On-Line HPLC analyzer. The upper compartment door contains a keypad for programming and operation of the analyzer. The upper window allows viewing of indicator lights and a liquid crystal display that provides the operator with analyzer interface, programmed parameters, and instrument status results. The lower chamber contains the pumps, valves, injector, and detector(s) required for the chromatographic separation. The sample conditioning plate for online process monitoring is to the right of the analyzer. This is a typical process HPLC. (From Cotter, R.L. and Li, J.B., Lab Rob Autom., 1, 251,1989. With permission of VCH Publishers.)...

Reactive scheduling is an online procedure which modifies nominal schedules in reaction to the occurrence of an unexpected event. Reactive scheduling is traditionally used to handle short-term uncertainties in parameters as, e.g., processing times, or equipment failures. The underlying-models themselves usually do not incorporate information on the uncertainty. 

A variety of measurement methods have been developed for determining the water activity of food materials and are well described in texts such as Rahman (1995), Wiederhold (1997), and Bell and Labuza (2000). In general, water activity is a relatively easy parameter to measure, which can be an advantage, especially for use in the food industry. Depending on the technique selected, the water activity of a food material can be measured in a time frame of minutes (e.g., electronic instrument). In addition, individuals can be trained, with a limited amount of instruction, to make water activity measurements. Consequently, when appropriate, water activity measurements can be made relatively quickly by personnel overseeing a manufacturing line for quality assurance purposes. Measurement protocols, such as calibration procedures and proper temperature control, should be implemented to assure the accuracy of online c/w measurements. 

When using NMR as a detector for online separations, additional consideration must be given to how the sensitivity is affected by the movement of nuclei past the detector cell. Aside from the physical hardware setup, the chromatographic and spectroscopic parameters also play a role in the quality of the resulting data. Flow rate, solvent composition, and residence and acquisition times can be optimized to provide optimal results. NMR sensitivity and chromatographic resolution tend to have an inverse relationship with respect to online LC-NMR experiments. By slowing the flow rate, more scans can be acquired for a particular analyte in the flow cell, but... 

Extended Luen-berger Observer Process model, (including process kinetics, but it is possible to estimate some kinetic parameters online), process inputs. Well known approach. It allows tuning the convergence rate by pole placement. Model hnearization Inputs knowledge Stability and convergence are only locally vahd. [6]... 

Surveyor is suitable for parallel process development and optimization with online sampling and integrated HPLC analysis. It employs ten reaetion vessels (working volume 15-45 mL), with individually controlled reaction temperatures from —40° to -l-150°C, with the ability to reflux. Reagent addition, reaction parameter control, sampling, and HPLC injection are controlled by built-in software. 

The use of chemically informative online analysers in the early stages of route development enable identification of critical processing parameters. 

Preferential crystallisation is one option for optical resolution on a manufacmr-ing scale. Online polarimetry and refractometry have been used to d3mamically optimise the process for resolution of DL-threonine in aqueous solution by variation of process parameters such as degree of supersaturation, seed quantity, initial enantiomeric excess and scale [148]. The method is claimed to be suitable for control of quasi-continuous processes. 

Gruendling T, Junkers T, Gullhaus M, Bamer-Kowolllk C (2010) Mark-Houwink parameters for the universal calibration of acrylate, methacrylate and vinyl-acetate polymers determined by online size-exclusion chromatography-mass-spectroscopy. Macromol Chem Phys 211 520-528... 

Evolving from efforts [22] to use the best features of trial-and-error, process model, expert system, and expert model approaches, QPA [23-25] combines KBES traits with online dielectric, pressure, and temperature data to implement autoclave curing control. QPA combines extensive sensor data with KBES rules to determine control actions. These rules determine curing progress based upon process feedback, and implement control action. QPA adjusts production parameters on-line as such—within the limits of its heuristics—QPA can accommodate batch-to-batch prepreg variations. 

The Tandem system (Bruker Optics) is available for use during tablet production to measure tablet weight, thickness, hardness, and diameter, as well as online NIR content uniformity. The system can provide online analysis for drug substance uniformity, moisture content, and excipients. The advantage of systems like this is that the necessary data are available immediately to make adjustments to the production parameters in order to improve product rmiformity. Therefore, adjustments can be made to tablet weight in real time in order to achieve 100% of the label claim. 

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