DON'T GUESS USE YOUR TECHNICAL TRAINING

In a previous article entitled HOW TO BECOME A GOOD CHEMICAL ENGINEER, I listed 5 important categories as shown below:

? Defining Success

? Developing Relational Skills

? Using Your Technical Training

? Expanding Your Technical Skills

? Putting Safety First

Since there was so much interest in the article, I plan to expand on the last 3 categories over the next few weeks. My discussion will be focused on chemical engineering because that is my background and expertise. The selection of these 3 categories is based on my belief that these are not necessarily the most important, but these are the areas where I see the most need in the industrial world. The new graduate entering today’s environment finds intense pressure regardless of the organization or career path. This will often cause the new graduate or even experienced engineer to be forced to make a choice between two approaches:

1. Select an approach that uses excessive assumptions so that any calculations turn into guess work. While there are always assumptions in every engineering, research, or business calculation, the goal should be to minimize these assumptions. 

2. Select an approach that requires excessive amounts of time from management’s viewpoint.

The dilemma that one often finds that they are in is pictured below:

 On one hand, the pressure from management is often present indicating the serious of the problem. The engineer is usually faced with the realities of developing a solution when he/she is forced to rely on a logical approach that seems to make good sense because, he/she doesn’t have a computer program that covers the issue. An example of this might be a set of data that when analyzed using statistics provides an excellent correlation. However, it makes very little theoretical sense because it is the correlation between two dependent variables. The independent variable is not shown and not known. 

The dilemma described above can often be resolved for the majority of chemical engineering processes by recognizing that most of these processes can be represented by first order kinetics using simple but theoretically correct techniques. This approach of using simplified first order response can be described as shown below:

R = C*DF

Where:

R = The kinetic rate. The units will be some sort of amount of the material per unit time. Examples are BTU/hr or lbs. converted/minute

C = A constant that can be determined by plant data or laboratory data. The units will involve quantity, size and time.

DF = The driving Force. The units are variables such as temperature, concentration, and densities.

When considering this relationship two things are obvious:

1. It is a theoretically correct relationship that provides a rapid technique for solving problems, scaling up laboratory results, and analyzing data with a minimum number of assumptions. It can be used to describe reaction rate, settling rate, volatile removal rate, heat transfer rate, fluid flow rate, steam turbine rate of energy input, and a host of other chemical, mechanical and electrical engineering concepts.

2. Obviously, the keys are obtaining and using correct driving forces and correct constants. 

Not only do the values of the driving force need to be correct, but the form of the relationship must be correct. For example, the driving force to transfer heat by radiation is the difference between the absolute temperature of the hot surface raised to the 4th power and the absolute temperature of the cooler surface raised to the 4th power. While driving force for heat transfer by convection is just the temperature difference.

 In a similar fashion when considering reaction, the form of the driving force must be considered. For example, the reaction can be first order with respect to one reactant or first order with respect to more than one reactant. The same type of driving force analysis should be applied to any kinetically limited process.

While the approach focuses on kinetically limited processes the same approach can be used for equilibrium limited processes. In the case of an equilibrium limited process, the driving force is simply the difference between the actual concentration and the equilibrium concentration. An example of this might be a stripping bin process for removing volatiles from a polymer. In this case the driving force is the difference between the actual concentration and the equilibrium concentration of volatile in the polymer. The equilibrium concentration of the volatile in the polymer is based on the concentration of the volatile in the gas phase. These concentrations can be related by a relationship similar to Henry’s Law. Expressing this concept in terms of mathematical relationships is:

R = C*(X-E)

E = H*Y

Where:

 R = The rate of volatiles removal, ppm/minute.

C = A constant determined from laboratory or plant data with units of 1/minute.

X = The actual concentration, ppm.

E = The equilibrium concentration, ppm.

H = The pseudo Henry’s law constant, ppm/mol fraction.

Y = The gas phase concentration of the volatile, mol fraction.

The development of the constant (designated by C) can be developed by laboratory or plant data or by a theoretical analysis. The most successful of these 3 approaches is to use either plant data or simplified laboratory data. An example of the different approaches will be described later. The key to using either plant data or laboratory data to determine the constant (C) will be using the theoretically correct driving force. In addition, consideration should be given to the physical differences between the actual facilities and the prototype. For example, in the case of removing a volatile from a polymer the constant C will depend on factors such as bed depth, bed L/D, superficial velocity, and polymer morphology. Even in the case of scaling up a vertical heat exchanger from existing plant data, the H/D can impact the heat transfer coefficient.

To help in connecting with these concepts, take an example of an olefin polymerization. I choose this for 2 reasons 

? Polymerizations are complicated and are often analyzed by techniques that approach guess work. 

? They can be analyzed by theoretically correct techniques as described earlier. 

A highly theoretical technique utilizes reaction rate constants that are associated with chain initiation, chain transfer, chain propagation, chain termination and other theoretical stages of the reaction. The problem with this approach is that most of the time these constants are not known.

A technique that can be labelled as “quick and dirty” is to only consider catalyst efficiency as defined as the lbs of polymer produced per lb of catalyst fed to the reactor. This technique does not take into account variables such as reactor temperature, reactor residence time or monomer concentration.

An approach based on the earlier described technique, and assuming the reaction is first order with respect to catalyst and monomer concentrations and is conducted at constant temperature would look like:

R =K*M*C

Where:

R = The rate of monomer converted, lbs/minute-ft3.

M = The monomer concentration in the reactor, lbs/ft3.

C = The catalyst concentration in the reactor, lbs/ft3.

K = The reaction rate concentration, ft3/lbs of monomer-minutes.  

This theoretically correct equation can be easily converted as follows:

R/C = K*M

P = R

CA = R/C =K*M

If both sides are multiplied by T (reactor residence time)

CA*T = K*M*T

CE=CA*T

CE =K*M*T

Where:

P = Polymer production rate, lbs/hr.

CA = Catalyst Activity, lbs polymer/lb catalyst-minute.

CE = Catalyst Efficiency, lbs polymer/lb catalyst.

T = Residence Time, minutes. 

This leads to a much more theoretically correct term that can be used to determine the status of the conditions in the reactor:

K = CE/(M*T)

The reaction rate constant estimated using this technique can be used to determine the presence of impurities, the impact of increasing the monomer concentration, the impact of changes in pressure for a gas phase reactor and other variables. In addition, inclusion of the Arrhenius equation allows for predictions of the impact of temperature on the reaction rate and hence catalyst efficiency. Similar examples could be developed for analysis of other operations occurring in typical petrochemical plants. In addition, this approach can be used to scale up laboratory data to full size commercial plants. 

This approach is described in more detail in my book -Process Engineering Problem Solving.

LAST WORDS

One might think that this approach is too simplified when considering the possible sources of error. However, when faced with the alternatives of guess work based on only logic or the highly theoretical approach which is likely to result in a beautiful solution that is finalized too late to be of value, it represents a theoretically correct solution that is a compromise between these two extremes.