Results using simulated process

No industrial process enjoys a knowledge of mechanism and kinetics so complete that models can be compared to it. Aris (1975) and Cropley (1978) simulated experimental results using a rate model. From the data a new model was derived and compared with the original. 

Both preformed and in situ ferrite lowered plutonium concentrations in simulated process waste from 10-4 g/1 to 10-8 g/1 in one treatment step. Two or three flocculant precipitations, as currently used for waste processing, were required to achieve the same result. Ferrite waste treatment produced 4.1 g/1 solids, while production waste processing during the past year, using the flocculant process, produced 7.9 g/1 solids. 

Hunt, R. J., Assessment of the results from data processing systems using a digital chromatogram simulator, /. HRC CC, 8, 347, 1985. 

The results obtained appeared quite promising, but the real sensation was the detection of pyruvate, the salt of 2-oxopropanoic acid (pyruvic acid), which is one of the most important substances in contemporary metabolism. Pyruvic acid was first obtained in 1835 by Berzelius from dry distillation of tartaric acid. The labile pyruvate was detected in a reaction mixture containing pure FeS, 1-nonanethiol and formic acid, using simulated hydrothermal conditions (523 K, 200 MPa). The pyruvate yield, 0.7%, was certainly not overwhelming, but still remarkable under the extreme conditions used, and its formation supports Wachtershauser s theory. Cody concludes from these results that life first evolved in a metabolic system prior to the development of replication processes. 

In optimization using a process simulator to represent the model of the process, the degrees of freedom are the number of decision variables (independent variables) whose values are to be determined by the optimization, hence the results of an optimization yield a fully determined set of variables, both independent and dependent. Chapter 2 discussed the concept of the degrees of freedom. Example 15.1 demonstrates the identification of the degrees of freedom in a small process. 

The example simulation THERMFF illustrates this method of using a dynamic process model to develop a feedforward control strategy. At the desired setpoint the process will be at steady-state. Therefore the steady-state form of the model is used to make the feedforward calculations. This example involves a continuous tank reactor with exothermic reaction and jacket cooling. It is assumed here that variations of inlet concentration and inlet temperature will disturb the reactor operation. As shown in the example description, the steady state material balance is used to calculate the required response of flowrate and the steady state energy balance is used to calculate the required variation in jacket temperature. This feedforward strategy results in perfect control of the simulated process, but limitations required on the jacket temperature lead to imperfections in the control. 

The remainder of this chapter is devoted to describing the results of computer simulations which have used the ideas discussed above. The overall goal of these studies is to describe and understand phenomena which depend for the most part on bonding ( medium-range ) interactions. For example, simulations of the reaction of small molecules on metal surfaces are discussed in section 3.1, where bond formation occurs at thermal energies. The major drawback for using simulations to study these types of processes is that the... 

Figure 6.32 Simulation results using the melting model for the conventional melting process that is, melting is occurring in all four melt films...

There are many reasons why deconvolution algorithms produce unsatisfactory results. In the deconvolution of actual spectral data, the presence of noise is usually the limiting factor. For the purpose of examining the deconvolution process, we begin with noiseless data, which, of course, can be realized only in a simulation process. When other aspects of deconvolution, such as errors in the system response function or errors in base-line removal, are examined, noiseless data are used. The presence of noise together with base-line or system transfer function errors will, of course, produce less valuable results. 

As illustrated, based on simulation results, using the plotted graphs and screens, management can easily evaluate different design alternatives, machine and human behavior models, control systems, sensory feedback processing, and the need of a balanced server architecture, and even investigate what if scenarios further, without committing to major upfront investment. 

Another point to be noted is that this calculation would be done more easily today by using a process simulator. However, the details are presented here to help the reader gain familiarity with the key assumptions and results. 

Table 6.12. Some simulation experiment results using the SSMAE to estimate the vertical distribution of radionuclides in the Arctic Basin. The contribution of ecological processes to formation of the vertical distribution in the radionuclide content of the water is represented by the parameter (%). The average content of phytoplankton is represented by the parameter pw (g/m2).

Figures 7.13 and 7.14 give results using the FS2 flowsheet with the furnace for this hot-reaction case. Figure 7.13 shows that a 10% decrease in recycle flowrate can be handled, but a 20% decrease produces a reactor mnaway. This occurs despite the fact that the reactor inlet temperature increases only slightly ( 0.5 K) during the transient. Figure 7.14 gives results for changes in the setpoint of the reactor inlet temperature controller. Rather surprisingly, inlet temperature can be increased by 2 K without a runaway. This is unexpected since the isolated reactor (Fig. 7.12) showed a runaway with a +2 K change in Tm. The difference may be due to the effect of pressure. In the isolated reactor simulation, pressure is held constant at 50 bar. In the simulation of the whole process, pressure drops as reactor temperature increases due to the increased consumption of reactants. Since the reaction rate depends on the square of the total pressure (P2), the decrease in pressure lowers the reaction rates. However, a 3 K increase cannot be handled.

The fit is excellent. The parameters have physically plausible values, and the residual standard deviations are reasonable compared to likely experimental error. If the data were from a real reactor, the fitted values would be perceived as close to the truth, and it would be concluded that the kA term is negligible. In fact, the data are not from a real reactor but were contrived by adding random noise to a simulated process. The true parameters are k0 = 4 x 10y h 1. 7= 7500 K, kA = 0.5, and V = 1 h and the kA term has a significant effect on the reaction rate. When the error-free results are compared with the data, the standard deviation is higher than that of the fitted model for concentration, aA =0.0024, but lower for temperature, oT = 0.9 K. A fit closer to the truth can be achieved by using a weighted sum of aA and aT as the objective function, but it would be hard to anticipate the proper weighting in advance. 

---