Mass flow measurement distillation

Figure 12-26. The SIMULAR reaction calorimeter. Features include pumped liquid feed, gas mass flow control, gas evolution measurement, and distillation equipment. (Source Hazard Evaluation Laboratory Ltd.)...

Sometimes it is possible to evaluate the range of validity of measurements and correlations of physical properties, phase equilibrium behavior, mass and heat transfer efficiencies and similar factors, as well as the fluctuations in temperature, pressure, flow, etc., associated with practical control systems. Then the effects of such data on the uncertainty of sizing equipment can be estimated. For example, the mass of a distillation column that is related directly to its cost depends on at least these factors ... 

For example, in a distillation column we are usually interested in controlling the purity of the distillate and bottoms product streams. In chemical reactors, heat exchangers, and furnaces the usual controlled variable is temperature. In most cases these choices are fairly obvious. It should be remembered that controlled variables need not be simple, directly measured variables. They can also be computed from a number of sensor inputs. Common examples are heat removal rates, mass flow rates, and ratios of flow rates. 

Reflux ratios are controlled by measuring distillate flow rates, sending this signal into a multiplier whose other input is the design RR and whose output sets the mass flow rate of the reflux. Note that mass units can be used here because the distillate and the reflux have the same composition. 

In this chapter, all of the process flow rates were considered to be constrained by zero flow and the maximum flow allowable by the valve size and span of the flow measurement. The main column constraint because of flooding is associated with the vapor traffic and pressure drop across the trays or packing in the distillation column. The mass transfer rate limit for stripping light key impurity from the bottoms stream was presented. The mass transfer rate limit for absorbing heavy key impurity from the overhead vapor stream was also presented. 

The first measure is to use the evaporative cooling or controlled depressurisation to keep the reaction mass under control. The distillation system must be designed for such a purpose and has to function, even in the case of failure of utilities. A backup cooling system, dumping of the reaction mass, or quenching could also be used. Alternatively, a pressure relief system may be used, but this must be designed for two-phase flow that may occur, and a catch pot must be installed in order to avoid any dispersion of the reaction mass outside the equipment. Of course, all these measures must be designed for such a purpose and must be ready to work immediately after the failure occurs. The use of thermal characteristics of the scenario for the choice of technical measures is presented in detail in Chapter 10. 

One thousand kilograms per hour of a mixture containing equal parts by mass of methanol and water is distilled. Product streams leave the top and the bottom of the distillation column. The flow rate of the bottom stream is measured and found to be 673 kg/h, and the overhead stream is analyzed and found to contain 96.0 wt% methanol. 

When the sample is introduced into the column, usually in the form of a zone of vapor, it takes the form of a narrow band. During transit through the column, various factors influence the width of this band, which is continuously increased due to various dispersion processes. These include diffusion of the solute, resistance to mass transfer between and within phases, and the influence of flow irregularities and pertur-bations.f A simple concept, the theoretical plate, carried over from distillation processes, has been used to compare columns and account for the degree of dispersion that influences bandwidth. A chromatographic column may be considered to consist of numerous theoretical plates where the distribution of sample components between the stationary and mobile phase occurs. Hence, a measure of the efficiency of a GC column may be obtained by calculating the number of theoretical plates, N, in the column from ... 

The symbols are P for profit, / for equality constraints, g for inequality constraints, x for optimization variables, y for dependent variables, and / (constant) for updated parameters. The objective function is a scalar measure of plant profit it is usually the instantaneous profit ( /hr), because the optimization variables do not involve the time value of money. Typical equality constraints include material and energy balances, heat and mass transfer relationships, and thermodynamic and kinetic models, and typical inequality constraints include equipment limitations limit compressor horsepower, and distillation tray hydraulics. The optimization variables are flow rates, pressures, temperatures, and other variables that can be manipulated directly. The dependent variables involve intermediate values required for the detailed models for example, all distillation tray compositions, flow rates, and temperatures. Because of the fundamental models often used in RTO, the number of dependent variables can be quite large, on the order of hundreds of thousands. 

The Rh (III)/ clay dispersion modified with pillaring agent was centrifuged and washed six times with distilled water and air dried at 80°C. The catalyst precursor namely Rh (phen)3 in the form of dry clay mass, was then crushed into a powder and calcined at 450°C for 4 hours in air. This was subsequently reduced at 350°C under a flow of hydrogen for 8 hours. A known amount of catalyst was digested in aqua regia for 16 hours and filtered. The filtrate was quantitatively made up to a known volume. The Rh content in this solution was measured by Atomic Absorption Spectroscopy (GBC Model 932). The value so obtained (1.5 %) matched with the theoretical value. 

Forty years ago these computed variables were calculated using pneumatic devices. Today they are much more easily done in the digital control computer. Much more complex types of computed variables can now be calculated. Several variables of a process can be measured, and all the other variables can be calculated from a rigorous model of the process. For example, the nearness to flooding in distillation columns can be calculated from heat input, feed flow rate, and temperature and pressure data. Another application is the calculation of product purities in a distillation column from measurements of several tray temperatures and flow rates by the use of mass and energy balances, physical property data, and vapor-liquid equilibrium information. Successful applications have been reported in the control of polymerization reactors. 

Generally speaking, the overall simulated result of a distillation tray column by using two-equation model and different Reynolds mass flux models is very close each other and checked with experimental measurements, but if detailed mass transfer and flow information on the trays are needed, the standard Reynolds mass flux model is the better choice. 

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