The dependence of average crystal size with time over the initial batch operation period was determined for Runs R0-R3. Hie data was fit to a power-law model of the form [Pg.325]

Component B is the desired product of the reaction, and the aim is to find the optimum batch time and temperature to maximise the selectivity for B. Saturated steam density data are taken from steam tables and fitted to a polynomial. The model and data for this example are taken from Luyben (1973). [Pg.253]

A relatively simple example of a confounded reactor is a nonisothermal batch reactor where the assumption of perfect mixing is reasonable but the temperature varies with time or axial position. The experimental data are fit to a model using Equation (7.8), but the model now requires a heat balance to be solved simultaneously with the component balances. For a batch reactor. [Pg.224]

The first use that we can make of our constitutive equations is to fit and smooth our data and so enable us to discuss experimental errors. However, in doing this we have the material parameters from the model. Of course it is these that we need to record on our data sheets, as they will enable us to reproduce the experimental curves and we will then be able to compare the values from batch to batch of a product or formulation. This ability to collapse more or less complicated curves down to a few numbers is of great value whether we are engaged in the production of, the application of, or research into materials. [Pg.6]

Mass transfer coefficients may be obtained by fitting to process data. Including DPC loss, production capacity (reaction time), and unwanted side products in a cost function, the optimization leads to a balancing of mass transfer and reaction rate. This means that an optimal process is neither entirely mass-transfer nor ki-netically controlled. To avoid side reactions that impair product quality, the lowest temperature that kinetics and mass transfer allow is chosen. The results of the semi-batch optimization can be transferred to the design of a staged, continuous process [154]. [Pg.97]

The curves in Figure 7.2 plot the natural variable a t)laQ, versus time. Although this accurately portrays the goodness of fit, there is a classical technique for plotting batch data that is more sensitive to reaction order for irreversible Hth-order reactions. The reaction order is assumed and the experimental data are transformed to one of the following forms [Pg.219]

Fig. 9.1. Sorption of selenate (SeO ) to a loamy soil, showing mass sorbed per gram of dry soil, as a function of concentration in solution. Symbols show results of batch experiments by Alemi et al. (1991 their Fig. 1) and lines are fits to the data using the reaction KA, reaction Freundlich, and Langmuir approaches. |

Suppose the desired product is the single-step mixed acidol as shown above. A large excess of the diol is used, and batch reactions are conducted to determine experimentally the reaction time, which maximizes the yield of acidol. Devise a kinetic model for the system and explain how the parameters in this model can be fit to the experimental data. [Pg.72]

Abstract Removal of catechol and resorcinol from aqueous solutions by adsorption onto high area activated carbon cloth (ACC) was investigated. Kinetics of adsorption was followed by in-situ uv-spectroscopy and the data were treated according to pseudo-first-order, pseudo-second-order and intraparticle drfiusion models. It was fotmd that the adsorption process of these compotmds onto ACC follows pseudo-second-order model. Furthermore, intraparticle drfiusion is efiective in rate of adsorption processes of these compoimds. Adsorption isotherms were derived at 25°C on the basis of batch analysis. Isotherm data were treated according to Langmuir and Freundhch models. The fits of experimental data to these equations were examined. [Pg.213]

The first step in developing a mass balance model is to compute the difference in the concentration of each element between the final and initial solution. In an ideal batch reactor this difference is the result of reactions between the solution and the solid phases. The next step is to postulate reactions that might have caused those changes in the solution composition and to construct a set of linear equations to represent the effect of each reaction. Finally, the linear equations are solved to determine the extent of each of the reactions that must have occurred. If a reasonable solution is not found, the postulated reactions are revised imtil a fit is found between the reactions and the changes in solution chemistry. This means that the model may not produce a unique fit to the data. [Pg.170]

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