Using the method outlined above, calculations were performed for ten ternary systems. All binary parameters are shown in Table 4. Some typical results are shown in Figures 16 to 19.  [c.69]

Appendix C-6 gives parameters for all the condensable binary systems we have here investigated literature references are also given for experimental data. Parameters given are for each set of data analyzed they often reflect in temperature (or pressure) range, number of data points, and experimental accuracy. Best calculated results are usually obtained when the parameters are obtained from experimental data at conditions of temperature, pressure, and composition close to those where the calculations are performed. However, sometimes, if the experimental data at these conditions are of low quality, better calculated results may be obtained with parameters obtained from good experimental data measured at other conditions.  [c.144]

Once the flowsheet structure has been defined, a simulation of the process can be carried out. A simulation is a mathematical model of the process which attempts to predict how the process would behave if it was constructed (see Fig. 1.1b). Having created a model of the process, we assume the flow rates, compositions, temperatures, and pressures of the feeds. The simulation model then predicts the flow rates, compositions, temperatures, and pressures of the products. It also allows the individual items of equipment in the process to be sized and predicts how much raw material is being used, how much energy is being consumed, etc. The performance of the design can then be evaluated.  [c.1]

Once the basic performance of the design has been evaluated, changes can be made to improve the performance in other words, we optimize. These changes might involve the synthesis of alternative structures, i.e., structural optimization. Thus we simulate and  [c.2]

Thus the complexity of chemical process synthesis is twofold. First, can we identify all possible structures Second, can we optimize each structure for a valid comparison When optimizing the structure, there may be many ways in which each individual task can be performed and many ways in which the individual tasks can be interconnected. This means that we must simulate and optimize  [c.4]

Since process design starts with the reactor, the first decisions are those which lead to the choice of reactor. These decisions are among the most important in the whole design. Good reactor performance is of paramount importance in determining the economic viability of the overall design and fundamentally important to the environmental impact of the process. In addition to the desired products, reactors produce unwanted byproducts. These unwanted byproducts create environmental problems. As we shall discuss later in Chap. 10, the best solution to environmental problems is not elaborate treatment methods but not to produce waste in the first place.  [c.15]

Having made a choice of the reaction path, we need to choose a reactor type and make some assessment of the conditions in the reactor. This allows assessment of reactor performance for the chosen reaction path in order for the design to proceed.  [c.18]

Before we can explore how reactor conditions can be chosen, we require some measure of reactor performance. For polymerization reactors, the most important measure of performance is the distribution of molecular weights in the polymer product. The distribution of molecular weights dictates the mechanical properties of the polymer. For other types of reactors, three important parameters are used to describe their performance  [c.22]

Because there are two feeds to this process, the reactor performance can be calculated with respect to both feeds. However, the principal concern is performance with respect to toluene, since it is much more expensive than hydrogen.  [c.25]

In describing reactor performance, selectivity is usually a more meaningful parameter than reactor yield. Reactor yield is based on the reactant fed to the reactor rather than on that which is consumed. Clearly, part of the reactant fed might be material that has been recycled rather than fresh feed. Because of this, reactor yield takes no account of the ability to separate and recycle unconverted raw materials. Reactor yield is only a meaningful parameter when it is not possible for one reason or another to recycle unconverted raw material to the reactor inlet. By constrast, the yield of the overall process is an extremely important parameter when describing the performance of the overall plant, as will be discussed later.  [c.25]

The choice of catalyst and the conditions of reaction can be critical in the performance of the process because of the resulting influence on selectivity.  [c.48]

Catalytic degradation. The performance of most catalysts deteriorates with time. The rate at which the deterioration takes place is another important factor in the choice of catalyst and the choice of reactor conditions. Deterioration in performance lowers the rate of reaction, which, for a given reactor design, manifests itself as a lowering of the conversion. This often can be compensated by increasing the temperature of the reactor. However, significant increases in temperature can degrade selectivity considerably and often accelerate the mechanisms that cause catalyst degradation. Loss of catalyst performance can occur in a number of ways a. Physical loss. Physical loss is particularly important with homogeneous catalysts, which need to be separated from reaction products and recycled. Unless this can be done with high efficiency, it leads to physical loss (and subsequent environmental problems). However, physical loss as a problem is not restricted to homogeneous catalysts. It also can be a problem with heterogeneous catalysts. This is particularly the case when catalytic fluidized-bed reactors are employed. Attrition of the particles causes the catalyst particles to be broken down in size. Particles which are carried over from the fluidized bed are normally separated from  [c.48]

The performance of fluidized-bed reactors is not approximated by either the well-stirred or plug-flow idealized models. The solid phase tends to be well-mixed, but the bubbles lead to the gas phase having a poorer performance than well mixed. Overall, the performance of a fluidized-bed reactor often lies somewhere between the well-stirred and plug-flow models.  [c.58]

If the mixture to be separated is homogeneous, a separation can only be performed by the addition or creation of another phase within the system. For example, if a gaseous mixture is leaving the reactor, another phase could be created by partial condensation. The vapor resulting from the partial condensation will be rich in the more volatile components and the liquid will be rich in the less volatile components, achieving a separation. Alternatively, rather than creating another phase, one can be added to the gaseous mixture, such as a solvent which would preferentially dissolve one or more of the components from the mixture. Further separation is required to separate the solvent from the process materials allowing recycle of the solvent, etc. A number of physical properties can be exploited to achieve the separation of homogeneous mixtures.If a heterogeneous or multiphase mixture leaves the reactor, then separation can be done physically by exploiting differences in density between the phases.  [c.67]

Another important class of dryer is the fluidized-bed dryers. Some designs combine spray and fluidized-bed dryers. Choice between dryers is usually based on practicalities such as the materials handling characteristics, product decomposition, product physical form (e.g., if a porous granular material is required), etc. Also, dryer efficiency can be used to compare the performance of different dryer designs. This is usually defined as follows -.  [c.91]

This might he worthwhile if the FEED-BYPRODUCT separation is expensive. To use a purge, the FEED and BYPRODUCT must be adjacent to each other in order of volatility (assuming distillation is used as the means of separation). Of course, care should be taken to ensure that the resulting increase in concentration of BYPRODUCT in the reactor does not have an adverse effect on reactor performance. Too much BYPRODUCT might, for example, cause a deterioration in the performance of the catalyst.  [c.97]

The fourth option, shown in Fig. 4.4[c.100]

Rather than relying on heuristics which can be ambiguous or in conflict, a parameter would be preferred that can measure quantitatively the relative performance of different sequences. The vapor flow rate up the column is a good measure of both capital and operating costs. There is clearly a relationship between the heat duty required to run the distillation and the vapor rate, since the latent heat relates these two parameters. However, there is also a link between vapor rate and capital cost, since a high vapor rate leads to a large-diameter column. The high vapor rate also requires large reboilers and condensers. Thus vapor rate is a good measure of both capital and operating costs on individual columns. Consequently, sequences with a lower total vapor load would be preferred to those with a high total vapor load. But how is the total vapor load predicted  [c.135]

The design philosophy started at the heart of the onion with the reactor and moved out to the next layer, which is the separation and recycle system. Acceptance of the major processing steps (reactors, separators, and recycles) in the inner two layers fixes the material and energy balance. Thus the heating and cooling duties for the outer two layers of the onion (i.e., the heat exchanger network and utilities) are now known. To complete a first pass through the total problem, a design for these two outer layers must now be provided. However, completing the design of the heat exchanger network is not necessary in order to assess the completed design. Targets can be set for the heat exchanger network and utilities to assess the performance of the complete process design without actually having to carry out the design. These targets allow both energy arid capital costs of the outer two layers to be assessed. Moreover, the targets allow the designer to suggest process changes for the inner layers of the onion (reactor, separation and recycle systems) which improve the targets for energy and capital costs of the two outer layers (heat exchanger network and utilities).  [c.159]

It must be emphasized that Eq. (6.7) is only an approximate method for calculating the performance of refrigeration cycles. If greater accuracy is required, the refrigeration cycle must be followed using thermodynamic properties of the refrigerant being used. °  [c.209]

Screening design options in the material and energy balance. For example, changes in reactor or separation system design can be screened effectively without performing repeated network design.  [c.233]

The performance of anaerobic digestion processes varies according to the type of unit, throughput, and feed concentration, but such processes are typically capable of removing between 75 and 85 percent of COD.  [c.314]

The inability to produce high-quality effluents is one significant disadvantage. Another disadvantage is that anaerobic processes must be maintained at temperatures between 35 and 40°C to get the best performance. If low-temperature waste heat is available from the production process, then this is not a problem.  [c.314]

The considerations addressed so far in network design have been restricted to those of energy performance and number of units. In addition, the problems have all been straightforward to design for  [c.385]

In most processes, the largest individual cost is raw materials. Raw materials costs and product prices tend to have the largest influence on the economic performance of the process. The value of raw materials and products depends on whether the materials in question are being bought and sold under a contractual arrangement (either within or outside the company) or on the open market (the spot price). Open-market prices can fluctuate considerably with time. Products are normally sold at below open-market price when under a contractual arrangement.  [c.407]

Subroutine REGRES. REGRES is the main subroutine responsible for performing the regression. It solves for the parameters in nonlinear models where all the measured variables are subject to error and are related by one or two constraints. It uses subroutines FUNG, FUNDR, SUMSQ, and SYMINV.  [c.217]

Examples of main programs calling subroutines FLASH and ELIPS for vapor-liquid and liquid-liquid separation calculations, respectively, are described in this Appendix. These are intended only to illustrate the use of the subroutines and to provide a means of quickly evaluating their performance on systems of interest. It is expected that most users will write their own main prograns utilizing FLASH and ELIPS, and the other subroutines presented in this monograph,to suit the requirements of their separation calculations.  [c.347]

There are many facets to the evaluation of performance. Good economic performance is an obvious first criterion, but it is certainly not the only one. Chemical processes should be designed as part of a sustainable industrial development which retains the capacity of ecosystems to support both industried activity and life. In practical terms this means that waste should be minimized and that any waste byproducts which are produced must not be environmentally harmful. Sustsiinable development also demands that the process should use as little energy as practicable. The process also must meet required health and safety criteria. Start-up, emergency shutdown, and ease of control are other important factors. Flexibility, i.e., the ability to operate under different conditions such as differences in feedstock and product specification, etc., may be important. Availability, i.e., the number of operating hours per year, also may be important. Some of these factors, such as economic performance, can be readily quantified others, such as safety, often cannot. Evaluation of the factors which are not readily quantifiable, the intangibles, requires the judgment of the designer.  [c.2]

The question now is, given that there are often constraints to deal with, how do we evaluate the effect of these constraints on the system performance The problem table algorithm cannot be used directly if constraints are imposed. However, often the effect of constraints on  [c.181]

Fundamentally, there are two possible ways to integrate a heat engine exhaust. In Fig. 6.31 the process is represented as a heat sink and heat source separated hy the pinch. Integration of the heat engine across the pinch as shown in Fig. 6.31a is coimterproductive. The process still requires QHmm, and the heat engine performs no  [c.193]

Figure 6.39 shows a heat pump appropriately integrated against a process. Figure 6.39a shows the overall balance. Figure 6.396 illustrates how the grand composite curve can be used to size the heat pump. How the heat pump performs determines its coefficient of performance. The coefficient of performance for a heat pump generally can be defined as the useful energy delivered to the process divided by the shaftwork expended to produce this useful energy. From Fig. 6.39a,  [c.204]

The cost of shaftwork required to run a refrigeration system can be estimated approximately as a multiple of the shaftwork required for an ideal system. The performance of an ideal system is given by  [c.207]

Consider first the design for minimum energy in a more complex problem than seen so far. If the problem table analysis is performed on the stream data, Qnmiji and Qcmin can be calculated. When the network is designed and a match placed, it would be useful to assess whether there will be any energy penalty caused by some feature of the match without having to complete the design. Whether there will be a penalty can be determined by performing a problem table analysis on the remaining problem. The problem table analysis is simply repeated on the stream data, leaving out those parts of the hot and cold stream satisfied by the match. One of two results would then occur  [c.386]

See pages that mention the term Performance : [c.25]    [c.48]    [c.22]    [c.109]    [c.133]    [c.150]    [c.182]    [c.197]    [c.204]    [c.207]    [c.282]    [c.334]    [c.338]    [c.383]   
See chapters in:

Rules of thumb for chemical engineers  -> Performance

Compressors selections and sizing  -> Performance

Introduction to computational chemistry  -> Performance

Turboexpanders and Process Applications (0) -- [ c.0 ]

Gas turbine engineering handbook (2002) -- [ c.0 ]

Advanced control engineering (2001) -- [ c.0 ]

Applied Process Design for Chemical and Petrochemical Plants, Volume 1 (1999) -- [ c.197 , c.358 , c.370 , c.375 ]