ISIMS

Fig. 9. Mass spectra of lyso2yme having 14,305 mol wt. See text, (a) Isims spectmm (b) ESI spectmm (1). Courtesy of Kratos Analytical.

Prior to the introduction of ESI, ms /ms studies of peptides were generally limited to molecules mol wt < 3500 (33). This limitation was a consequence of the rapid drop in precursor ion intensity from Isims ion sources with increasing mass, and the inefficiency of coUisional activation. Good... 

Mass spectral analysis of quaternary ammonium compounds can be achieved by fast-atom bombardment (fab) ms (189,190). This technique rehes on bombarding a solution of the molecule, usually in glycerol [56-81-5] or y -nitroben2yl alcohol [619-25-0], with argon and detecting the parent cation plus a proton (MH ). A more recent technique has been reported (191), in which information on the stmcture of the quaternary compounds is obtained indirectly through cluster-ion formation detected via Hquid secondary ion mass spectrometry (Isims) experiments. 

ISIM PC Enclosed in this book, highly interactive, moderate computational power, easy-to-use. 

Probably one of the best ways to learn to use ISIM is by reference to existing programs as provided in Chapter 5, rather than by an initial detailed... 

Full details of the ISlM digital simulation programming language can be found in the appendix, and by reference to the ISIM programs associated with the simulation examples of Chapter 5. 

The following ISIM program solves the complex reaction problem... 

All the above changes are easily implementable in dynamic simulations, using ISIM and other digital simulation languages. The forms of response obtained differ in form, depending upon the system characteristics and can be demonstrated in the various ISIM simulation examples. The response characteristics of real systems are, however, more complex. In order to be able to explain such phenomena, it is necessary to first examine the responses of simple systems, using the concept of the simple, step-change disturbance. 

Optimisation may be used, for example, to minimise the cost of reactor operation or to maximise conversion. Having set up a mathematical model of a reactor system, it is only necessary to define a cost or profit functionOptimisation and then to minimise or maximise this by variation of the operational parameters, such as temperature, feed flow rate or coolant flow rate. The extremum can then be found either manually by trial and error or by the use of a numerical optimisation algorithms. The first method is easily applied with ISIM, or with any other simulation software, if only one operational parameter is allowed to vary at any one time. If two or more parameters are to be optimised this method however becomes extremely cumbersome. 

Non-linear parameter estimation is far from a trivial task, even though it is greatly simplified by the availability of user-friendly program packages such as a) SIMUSOLV (Steiner et al., 1986), b) ESL, c) a set of BASIC programs (supplied with the book of Nash and Walker-Smith, 1987) or d) by mathematical software (MATLAB). ISIM itself does not supply these advanced features, but ISIM programs can easily be translated into other more powerful languages. 

The SIMUSOLV listing is again very similar to ISIM, and starts with the INITIAL section for definition of constants followed by the DYNAMIC region containing the model equations. 

In both types of problem, solution is usually achieved by means of a step-by-step integration method. The basic idea of this is illustrated in the information flow sheet, which was considered previously for the introductory ISIM complex reaction model example (Fig. 1.4). 

ISIM uses four different integration methods. The user can select a particular method by assigning the variable ALGO a corresponding value. Other variables associated with the integration method are also explained below. 

The step-by-step evaluation is, of course, effected automatically by the computer, as shown in the ISIM simulation example VARMOL. 

At steady state, aCA/9t can be set to zero, and the equation becomes an ordinary second-order differential equation, which can be solved using ISIM. 

Each simulation example is identified by a file name and title, and each comprises the qualitative physical description with drawing, the model equation development, the nomenclature, the ISIM program, suggested exercises, sample graphical results and literature references. The diskette in the pocket at the back of the book contains the programs and the ISIM software. 

The program can be used to generate repeated runs for differing values of reactor temperature Tr, using the ISIM interactive facility. The range of interest is 190 to 250°C. 

Note These equations can be easily programmed using ISIM. 

To reduce stiffness at the beginning, an appropriate initial value of the steam density is calculated in the FORTRAN subroutine START, which uses the halfinterval method for the non-linear algebraic equation. Note that the execution may be very slow because of equation stiffness. Increasing the value of CINT during the initial heating period may terminate ISIM execution. 

Program THERM solves the dynamic model equations. The initial values of concentration and temperature in the reactor can be changed after each run using the ISIM interactive commands. The plot statement causes a composite phase-plane graph of concentration versus temperature to be drawn. Note that for comparison both programs should be used with the same parameter values. 

Proportional control can be based on the temperature of the third stage. Here FO is the base flow rate, KC is the proportional controller gain, and TSET is the temperature set point. Note that in order to guard against the unrealistic condition of negative flow, a limiter condition on F should be inserted into the DYNAMIC region. This can be accomplished with ISIM by the following statement... 

The dimensionless model equations are programmed into the ISIM simulation program HOMPOLY, where the variables, M, I, X and TEMP are zero. The values of the dimensionless constant terms in the program are realistic values chosen for this type of polymerisation reaction. The program starts off at steady state, but can then be subjected to fractional changes in the reactor inlet conditions, Mq, Iq, Tq and F of between 2 and 5 per cent, using the ISIM interactive facility. The value of T in the program, of course, refers to dimensionless time. 

The ISIM program for this example with typical output is shown. Example BENZHYD... 

The ISIM program RUN with KV = 0 represents operation of the reactor under zero venting (V = 0) conditions, i.e., the bursting disc remains closed. With KV > 0 ( 100 - 1000) the program simulates the performance under emergency venting conditions. The program runs stably until the user interacts with HOLD and causes the coolant failure to occur with HOLD, VAL FC = 0 and GO. 

Ideal plug-flow regions Vj are represented by time delay functions, which are programmed into the ISIM examples. 

Compare the results of the simulation to the analytical solution which can also be calculated with ISIM. For the constant density assumption, simply set b = 0. 

The program makes use of the random number generator in ISIM, RAND, to cause random fluctuations in the feed temperature, To. Eliminating RAND will shorten the run time. 

An example of the use of an ISIM sub-model representing a complex pole transfer function is also given in the ISIM manual (obtainable separately). 

Note that the derivative term in the expression for W2 can be programmed directly into the ISIM program as a variable. 

ISIM Simulation Manual, ISIM International Simulation Ltd. 

Simulate the effect of a process operator adding an additional charge of solvent part way through the extraction. Note that you now need to allow for both total mass balance and component mass balance changes. Use the interactive facility of ISIM, to effect the changes. 

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