Design Parameters and Procedure

In the quaternary case, the flowrates of the two fresh feeds, the distillate, and the bottoms were all set to 12.6mol/s. Then the relaxation model adjusted the reflux flowrate to drive the purity of the bottoms product to the desired value, xb,d = 0-95 mol fraction D for the 95% conversion case. The vapor boUup was adjusted to control the base level. This approach gave the same impurity levels in both product streams and the same conversions of the two reactants fed to the system. 

The ternary system without inerts has a diHerent column structure and requires a different approach for designing the column. There are still two feedstreams and a bottoms stream, but there is no distillate. In addition, the impurity in the bottoms product will be mostly the heavier of the two reactants, component B. This means that the flowrates of the two fresh feedstreams will not be equal. Moreover, the reaction is not equimolar. Two moles of reactants produce 1 mol of product. Thus, there is a decrease in the molar liquid flowrates in the reaction section that is attributable to the nonequimolar reaction. 

The procedure used for this system is to fix the production rate of product C at 12.6 mol/s and the purity of the bottoms product at 0.98 mol fraction C. This means that the bottoms flowrate is 12.6/0.98= 12.857 mol/s. The production rate requires that 12.6mol/s of both A and B be consumed. Thus, at least this amount must be fed to the column. In addition, however, there is a loss of reactants in the bottoms to account for the 2 mol% impurity. It is mostly B, but there is also a small amount of A. The concentrations of A and B change with the various designs. Therefore, the flowrates of the fresh feeds are slightly different in each design. At each point in time during the dynamic simulation, the fresh feed flowrates are calculated from the fixed value of the bottoms flowrate B and the present value of the bottoms composition x j, which changes with time until a steady-state solution is achieved. 

Because B is heavier than A, the fresh feed flowrate of B is somewhat larger than that of A. The reflux flowrate is changed to drive the bottoms composition to 98 mol% C. The vapor boilup controls the level in the base. There is no distillate. The reflux dmm level is not controlled. 

Another change made in the equations accounts for the reduction in moles as 1 mol of product is produced by the consumption of 2 mol of reactant. The liquid rates on the reactive trays are reduced by the rate of reaction and by the vaporization caused by the heat of reaction. 

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One of the best definitions is by Attilio Bisio (1) The successful startup and operation of a commercial size unit whose design and operating procedures are in part based on experimentation and demonstration at a smaller scale of operation. He also points out that Smith (2) argued in 1968 that the starting point for scaleup studies is the ultimate intended commercial unit. The professional should scaledown from the design parameters and constraints of that commercial unit so that the smaller scale experiments were most useful in reducing the uncertainties of the commercial run. Smith wrote that scaleup from small-scale studies is a misleading concept. 

When the estimation procedure is clearly specified, an approximate covariance matrix of the estimate, Sj, can also be calculated. This matrix reflects the degree of precision of the estimate, and depends on the experimental design, parameters, and the noise statistics. A well-designed experiment with small random fluctuations will lead to precise estimations ( small covariance), while a small number of iminformative data and/or a high level of noise will produce unreliable estimates ( large covariance). 

Another industry initiative, the Chemical Manufacturers Association (CMA) Responsible Care Process Safety Code of Management Practices, refers to operating procedures by noting the need for "current, complete documentation of process design, operating parameters, and procedures" (emphasis added). 

Analyze the problem in more detail. You may have to identify cridcal design parameters and consider their influence in your final design. At this sts, you need to make sure that all cal-culadons are performed correcdy. If there are some uncertaindes in your analysis, you must perform experimental invest adon. When possible, working models must be created and tested. At this stage of the des procedure, the best soludon must be idendfied from altema-dves. Details of how the product is to be febricated must be worked out fully. 

This work addresses the optimal design and operation of a batch protein ultrafiltration plant. The dynamic optimisation procedure adopted identifies simultaneously the optimal design parameters and operating policy of the installation. It should be emphasised that the approach can be directly applied to other ultrafiltration processes. This is the first work in which a formal dynamic optimisation methodology is applied to batch ultrafiltration. 

The utility of statistical experiment designs are many fold. First of aU, a priori design of experiments requires that the researcher carefully consider the dependent and independent parameters. Second, crosscorrelations between the independent parameters can be explored. Last but most important of aU, the designed experiments minimize experimental effort while maximizing the obtained information. Several experimental design protocols and procedures exist in the literature, and the interested reader is directed to the textbooks by Montgomery and Runger (1994), Box et al. (2005), and Lazic (2004). 

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