Mixed-model production

Mixed-model production A production scheme where the production line product mix matches what is sold each day. 

Heijunka A method of leveling production at the final assembly line that makes Just-in-Time production possible. This involves averaging both the volume and sequence of different model types on a mixed-model production line. 

Evaluation of Mixing Models. The micro-mixed reactor will produce polymer disttibutions with increasing amounts of high molecular weight tail as the degree of polymerization of the polymer product increases over that of the original seed polymer. 

The completely mixed model succeeds in representing part of the experimental data and predicts that at industrial conditions the reactor is open-loop unstable. Initiator productivity decreases are accounted quite accurately only by the second reactor model which details the mixing conditions at the initiator feed point. Independent estimates of the model parameters result in an excellent match with experimental data for several initiator types. Imperfect mixing is shown to have a tendency to stabilize the reactor. 

The mathematical equations for this imperfectly mixed model consist of 12 differential equations similar to equations (l)-(4), four for each of the three CSTR s. At steady state, they reduce to 12 non-linear algebraic equations which are solved numerically in order to calculate the dependence of initiator consumption on polymerization temperature. An overall balance reveals that the monomer conversion and polymer production rate are still given by equations (5) and (8), while the initiator consumption is affected by the temperature and radicals distribution in the three CSTR s, so that equations (7) and (9) become much more complex. 

In particular, this chapter wiU stress the need to look beyond the classic radical chain reaction. Lipid oxidation mechanisms have been proposed based on kinetics, usually of oxygen consumption or appearance of specific products (e.g., LOOK) or carbonyls (e.g., malonaldehyde), assuming standard radical chain reaction sequences. However, when side reactions are ignored or reactions proceed by a pathway different from that being measured, erroneous conclusions can easily be drawn. The same argument holds for catalytic mechanisms, as will be shown in the discussion about metals. In the past, separation and analysis of products was laborious, but contemporary methods allow much more sensitive detection and identification of a broad mix of products. Thus, multiple pathways and reaction tracks need to be evaluated simultaneously to develop an accurate picture of lipid oxidation in model systems, foods, and biological tissues. 

These models require information about mean velocity and the turbulence field within the stirred vessels. Computational flow models can be developed to provide such fluid dynamic information required by the reactor models. Although in principle, it is possible to solve the population balance model equations within the CFM framework, a simplified compartment-mixing model may be adequate to simulate an industrial reactor. In this approach, a CFD model is developed to establish the relationship between reactor hardware and the resulting fluid dynamics. This information is used by a relatively simple, compartment-mixing model coupled with a population balance model (Vivaldo-Lima et al., 1998). The approach is shown schematically in Fig. 9.2. Detailed polymerization kinetics can be included. Vivaldo-Lima et a/. (1998) have successfully used such an approach to predict particle size distribution (PSD) of the product polymer. Their two-compartment model was able to capture the bi-modal behavior observed in the experimental PSD data. After adequate validation, such a computational model can be used to optimize reactor configuration and operation to enhance reactor performance. 

Lansky D. Strip-plot designs, mixed models, and comparisons between linear and non-linear models for microtitre plate bioassays. In Brown W, Mire-Sluis AR, eds. The Design and Analysis of Potency Assays for Biotechnology Products. Dev Biol. Basel Karger, 2002 107 11—23. 

It is required to design a reverse osmosis unit to process 2500 mVh of seawater at 25°C containing 3.5 wt% dissolved salts, and produce purified water with 0.05 wt% dissolved salts. The pressure will be maintained at 135 atm on the residue side and 3.5 atm on the permeate side, and the temperature on both sides at 25°C. The dissolved salts may be assumed to be NaCl. With the proposed membrane, the salt permeance is 8.0 x 10 m/h and the water permeance is 0.085 kg/rn-.h.atrn. The density of the feed seawater is 1020 kg/m ( of the permeate, 997.5 kg/nv and of the residue (with an estimated salt content of 5 wt%), 1035 kg/rnc Assuming a perfect mixing model and neglecting the mass transfer resistances, determine the required membrane area and calculate the product flow rates and compositions. 

The pressure is 3500 kPa on the residue side and 140 kPa on the permeate side. The composition on the permeate side is specified at 95 mol%. Assuming a perfect-mixing model, calculate the required membrane area. Use a fraction of feed permeated, 0 = 0.15. Calculate the product rates and compositions, and check if 0 needs to be modified. 

Conversion of reactant for single, ideal CSTR, and as a function of internal flowrate In a 2-CSTR mixing model. . Yield of desired product C for single, ideal CSTR, and as a function of internal flowrate, p = QrIQ.z, in a 2-CSTR... 

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