PARTICIPATION IN CHEMICAL ENGINEERING LABORATORY

The Chemical Engineering Laboratory course has been set up as an open-ended learning experience rather than the traditional data sheet oriented laboratories you may have had in other courses. You will normally receive assignments in the form of memos, and you are expected to respond with reports as might be written if you were working in industry.

Pre-Lab Preparation

Each laboratory experiment has a well-defined objective. This objective will be represented by a numerical value, or a correlation allowing the calculation of this value, for the unit operation under study. This objective is accomplished through data taking, reduction, and analysis. These tasks may be successfully completed and the objective achieved only when the nature of the desired result is known, before the experiment is undertaken. Otherwise insufficient data may be taken, the results may be incorrectly analyzed, or preposterous results may be reported.

Read your current assignment. Familiarize yourself with the theory relevant to the experiment and the equipment necessary to take the required data. Read the appropriate sections of your textbook and references such as Perry's Handbook. When you are sure you understand the problem, prepare in writing, a Pre-Lab Plan. This exercise is intended to provide the preparation necessary to undertake a successful experimental study.

The written plan should include a sketch of the experimental equipment, showing vessels, interconnecting piping, valves, and relevant instrumentation. Make sure you understand the purpose and safe operation of each of these equipment items before you start the experiment. The correct analysis of data, its accuracy, and correction for nonstandard operating conditions all require a thorough understanding of the instrumentation. The written plan should discuss the independent and dependent variables and how and over what range these independent variables will be measured. The plan should address the experimental procedure and the sequence of operations undertaken during the experiment. The plan should describe how the data would be correlated, based on what models and which literature information. This pre-lab must be completed before coming to lab. Bring the completed plan to the lab and have the instructor approve your work before undertaking any experimental work. Include the exercise as an appendix in your laboratory report.

In the Lab

Attendance: All students are to report to the lab at the start of the period. Attendance in all sessions for the full 2 hours 50 minutes of each period is required, unless you are excused by the instructor. Unauthorized absences for part or all of any laboratory session will result in a reduced course grade. Normally work will be performed in the lab; work in another area requires prior approval by the instructor.

Safety and Housekeeping: Good safety practices must be observed at all times in the laboratory. This practice includes the wearing of proper clothing, footwear, and eye protection. All equipment must be safely operated and all chemicals must be safely handled. Broken or damaged equipment should be reported to the instructor immediately. You are

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reminded that all equipment is to be cleaned and returned to its proper location after you finish the day's work. A clean laboratory is the responsibility of all group members. Do not leave the lab until it is clean.

Lab Rules:

1. Come to lab dressed properly. Appropriate dress is required for safety reasons. Wear shoes that cover the feet. Long pants are required. No sleeveless shirts or tank tops are permitted. Long sleeve shirts are preferred. Do not wear a hat. Do not wear long flowing clothes (that includes a tie). Long, lose hair must be restrained. Wear safety glasses. When required wear a face shield. When required wear gloves appropriate for the task.

2. At all times good safety practices must be observed in the laboratory. An individualwho violates this rule will not be permitted in the lab.

3. Come prepared and bring your Pre-Lab Plan.

4. Every group must have a lab notebook (with duplicate numbered pages). During each lab make notes of everything you are doing and every measurement you make. Your notebook will be evaluated so make sure it is legible and understandable. Each page of the notebook should be signed by the data taker and witnessed by a group member and the instructor. Original data sheets should appear in the lab report appendix while the carbon copies should be given to the instructor at the end of each data taking session. In your lab notebook:

Write in ink and record all observations directly in the notebookDo not erase. If necessary line out errors with one lineAt the top of each page put the date and the experiment nameWhen you finish a page, sign and witness your name with dateNever write on a page after you have signed itRotate recording among group members

Making the most out of your time in the lab

- Good experimental work demands that data and runs be replicated. Since we cannot duplicate all conditions exactly, we make replicate runs, meaning that the conditions are almost the same but not necessarily identical. Not only should individual data be replicated (for instance a second, third, and fourth measurement of a flow rate) but also the whole run (something from which you can calculate an experimental result) should be replicated.

- When first doing a new experiment, try different things and become familiar with the experiment and the equipment. Then determine the widest possible ranges of the independent variables. In almost all cases you will want to take data over wide ranges of the independent variables. If the independent variables can easily be changed over a wide range, it is often good practice to conduct the first three experimental runs at the lowest, intermediate, and highest levels of the independent variable.

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- Summarize and record in your laboratory notebook the information obtained from automatic data collection (by computer, by recorder, or whatever) as soon as possible.

- Get in the habit of working up your data as you go. Sometimes you can plot preliminary data to see trends. Other times you can calculate the experimental values and see if you are in the ballpark.

- In almost all cases you will be able to compare experimental calculations with theoretical and/or correlated values. In presenting your data and comparisons plan to use graphs if at all possible. If you are using tables, aim to make them concise, clear, and informative.

- Acquire the habit of clearly identifying your experimental runs. Run 1, then 2, then 3 is appropriate. Or you can use letters; A. B. C. etc. If a run is a failure (this sometimes happens) the next run is still given a new number or letter.

- Most of the reference books listed for the experiments, as well as other reference books, will be available for perusal during the laboratory period.

Collection and Analysis of Experimental Data

The fundamental basis for a good laboratory report is data collection and record keeping. It is not uncommon that a considerable amount of time elapses between the data collection phase of an experiment and the eventual presentation of the results. The data, therefore, must be well defined and recorded accurately in a permanent bound laboratory notebook. An important requirement is that the notebook, “should be so clear and complete that any intelligent person familiar with the field to which it relates but unfamiliar with the specific investigation could, from the notebook alone, write a satisfactory report on the experimental work”1, 2.

1. Always use your data as you are taking it to make sample calculations of your results, dimensioning all numbers with their correct units. This simple step will insure that all the information is being recorded that will be necessary for you to make subsequent calculations for the report. Graph your results, with units, as the experiment progresses, not after you have taken all the data. Graph your data in the same way you intend to present it in your report, although it is not always necessary to make all unit conversions for an initial plot done in the lab. This plot may then be used to determine if the current experimental conditions are in the desired range, if the equipment is being incorrectly operated or is not functioning properly, or if the degree of experimental scatter is unacceptable. The plot may also be used to determine experimental conditions for subsequent data points. More data should be taken under conditions where the dependent variable changes more rapidly and less where the changes are smaller. The independent variable(s) should be changed sufficiently to make noticeable changes in the dependent variable.

1 Rhodes, F. H., “Technical Report Writing”, Mc Graw-Hill.2 Hanesian, D. and Perna A. J., “A Laboratory Manual for Fundamentals of Engineering Design”, NJIT.

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Example:

Figure 1. Pressure drop in a bed of particulate solids.

Data can be collected evenly, however the important point of fluidization should not be lost because the gas flow settings, hence Reynolds number, were not properly chosen.

2. Always indicate units of quantities that are measured.

3. Never record a calculation, dimension or note on a loose sheet of paper.

4. Sketches of experimental apparatus should be part of notebook. All important dimensions should be recorded.

5. Data from recorders should be put into a laboratory notebook. Label the data with a title, data, and description of the data. Prepare your data sheet based on your Pre-Lab plan. In many experiments you will be taking a series of readings. Record more data rather than less and, of course, include units with all numerical values. The original data sheet and any graphs should appear in the appendix of your report, and the data sheet carbon copy should be turned in to the instructor at the end of each data taking session. The complete data notebook should be turned in to the instructor at the end of the course for a grade.

Example:

UNSTEADY STATE HEAT TRANSFERJanuary 18, 1999Heating curve for water with aluminum cube

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6. Always record the reading that you actual measure.

Example:Manometer

0

6 in.

5 in.

L 6 in.

R - 5 in.

²P 11 in.

Record left and right readings, not only the derived quantity P = 11 in.

Each table should be numbered, have a title and show all units.

Example:Table 1.

PRESSURE DROP IN PACKED COLUMNColumn diameter: 2 inchesPacked height:24 inchesColumn packing: 1/4 inch Raschig ring

Flow Rate of Flow Rate of Pressure DropWater Air Inches of water per

kg/m2.s kg/m2.s foot of packing

0 0.202 0.0750 0.279 0.1570 0.387 0.2750 0.436 0.3010 0.558 0.4030 0.610 0.4710.86 0.164 0.1070.86 0.199 0.1590.86 0.297 0.2620.86 0.362 0.4710.86 0.501 0.7850.86 0.597 1.247

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All graphs should have a Figure number, have clearly labeled coordinates and all parameters labeled. Label symbols should be used for different parameters.

Example:

Figure 1 Pressure drop in packed column.

Dimensionless numbers should be used if possible. For example: a plot of friction factor versus Reynolds number is better than a plot of P versus velocity.

7. Notebooks become legal evidence in patent cases. Therefore, always date, sign and witness.

Example:

Experiment #1 January 18, 1999Extended Surface Heat Transfer

Signed ____________________ 10/1/02 ____________________ 10/1/02 ____________________ 10/1/02

Witness (instructor) ____________________ 10/1/02

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8. Notebooks should be neat. Write in ink. Do not erase. If necessary line out with one line.

Error Analysis of Experimental Data

An error analysis should always be an integral part of your data analysis. It is important to take sufficient replicate data points, where possible, to enable you to perform a subsequent error analysis of the results. The ultimate objective of this analysis is to minimize this error.

The treatment of experimental data does not end when the desired numerical results have been obtained. An important part of the treatment is the determination of the uncertainty associated with the numerical results3 . Every measurement of data is subject to error that can not be determined exactly. If the exact error were known it would be equivalent to measuring the quantity without error, since correction can be made for any known error. But it is possible to specify the highest amount by which the quantity might be in error.

Inaccuracies in measurements can occur due to mistakes and errors2. A mistake is the recording of a wrong reading (215 recorded as 251). An error can be consistent or random. A consistent error that usually can be corrected might come from a defect in the measuring device or the improper use of an instrument. For example: the use of a flow meter calibrated for air but used for ammonia or the use of a wrong scale on a wet test meter. Random errors are caused by fluctuations and sensitivity of the instrument or the inconsistent judgment of the experimenter. An example would be a fluctuating manometer.

Treatment of random errors.

The more measurements we take the more likely the average will be the most probable value. Let consider six measurements for the flow rate of a process stream.

Reading Flow rate F, cm3/s1 139.12 142.33 138.44 140.65 139.06 141.5

The estimate mean for the flow rate F is

= 140.15 where N = 6

If N is very large (N --> ) then the mean for the flow rate will be the true mean average Fm. The estimate standard deviation is given by

3 Shoemaker, D. P. et al, “Experiments in Physical Chemistry”, McGraw-Hill, (1981)

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s = = 1.5579

Note: The estimate standard deviation can be obtained by the Matlab function std(F) where F = [139.1 142.3 138.4 140.6 139.0 141.5].

Since is an estimated mean from a finite measurement, how confident are we that this is a true mean? The confidence level, (1 - ) is defined as the probability that the measured mean for a small sample lies between a certain confidence interval. A 95 percent confidence means that = 0.05. The lower confidence interval is

- c/2

and the upper confidence interval is

+ c/2

where c/2 = and t/2 is given from the Student-t distribution for small, finite sample

size (N < 30). Student-t distribution and Student-t statistic were developed by W.S. Gosset in 1908 who used the pen name of “Student”. Table 1 gives values of t/2 for various confidence levels and N-1.

Table 1. Values of t/2

N-1 /2 = .05 /2 = .025 /2 = .01 1 6.314 12.706 31.8212 2.920 4.303 6.9653 2.353 3.182 4.5414 2.132 2.776 3.7475 2.015 2.571 3.3656 1.943 2.447 3.1437 1.894 2.365 2.9988 1.860 2.306 2.8969 1.833 2.262 2.82110 1.812 2.228 2.764

In this example, let = 0.05 which is the normal practice of giving 95 percent confidence limits for random errors based on small sample. Then

t/2 = 2.571 for N -1 = 5 from Table 1.

Therefore the lower confidence interval is

140.15 - (2.571)*(1.5579)/61/2 = 138.51

and the upper confidence interval is

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140.15 + (2.571)*(1.5579)/61/2 = 141.79

Hence, 138.51 141.79

and, we are 95 percent confident that the true flow rate mean, m, lies between 138.51 cm3/s and 141.79 cm3/s and = 140.15 cm3/s is an estimate of the true mean. It must be emphasized, however, that systematic errors may be much higher than the random errors in unfavorable conditions. Therefore the sources of systematic errors must be identified and eliminated if possible.

Linear Regression and Matrix Algebra

Error is inherent in data. When data exhibits substantial error rigorous techniques must be used to fit the "best" curve to the data. Otherwise prediction of intermediate values, or the derivatives of values, may yield unsatisfactory results.

Visual inspection may be used to fit the "best" line through data points, but this method is very subjective. Some criterion must be devised as a basis for the fit. One criterion would be to derive a curve that minimizes the discrepancy between the data points and the curve. Least-squares regression is one technique for accomplishing this objective.

It is easiest to interpolate between data points, and to develop correlation, when the dependent variable is linearly related to the independent variable. While individual variables may not be linearly related, they may be grouped together or mathematically manipulated, such as having their log or square root taken, to yield a linear relationship. Often theory serves as a guide for such manipulations. Since we are mainly interested in these linear relationships, we will discuss only the technique for linear least-squares regression.

We wish to fit the "best" straight line to the set of paired data points: (x1,Y1), (x2,Y2), …, (xi,Yi). The mathematical expression for the calculated values is:

yi = a1 + a2 xi

where yi is the calculated (linear) value approximating the experimental value Yi. The model error, or residual, ei can be represented as

ei = Yi a1 a2 xi

where ei is discrepancy between the measured value Yi and the approximated value yi as predicted by the linear equation and shown in Figure 1.

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Figure 1. Relationship between the model equation and the data

We wish to find values for a1 and a2 to give the "best" fit for all the data. One strategy would be to select values for a1 and a2 to yield a straight line that minimizes the sum of the errors ei's. Since error is undesirable regardless of sign this criterion is inadequate because negative errors can cancel positive errors. The problem may be fixed if one selects a1 and a2

such that the absolute value of the sum of errors is minimized. However one may show that this criterion does not yield a unique "best" fit. A third criterion for fitting the "best" line is the minimax criterion. With this technique one selects a line that minimizes the maximum distance that an individual data point deviates from the calculated line. Unfortunately this strategy gives an undue influence to an outliner, a single point with a large error.

A strategy that overcomes the shortcomings of these previous approaches is to minimize the sum of the squares of the errors or residuals, between the measured Yi's and the yi's calculated from the linear model. This criterion has a number of advantages. A unique line results for a given data set. This criterion also leads to the to the most likely a1 and a2

from a statistical standpoint.

Regression analysis is used to determine the constants in a relationship between variables. We only consider the simple case where y is a linear function of x. In other words we wish to find an equation y = a1 + a2x to best fit the obtained experimental data xi and Yi. At the values xi, the experimental values Yi are subject to random errors. Let’s define

ei = Yi yi

to be the difference between the experimental and predicted values. The least-squares criterion requires that S defined by Eq. (1.3-1) be a minimum

S = e12 + e2

2 + ..... + eN2 = (1.3-1)

or

S = {Yi [a1 + a2 (xi)]}2 (1.3-2)

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Setting the derivative of this sum with respect to each coefficient equal to zero will result in a minimum for the sum. Thus the coefficients a1 and a2 must satisfy the conditions

= {2}{Yi [a1 + a2 (xi)]} = 0 (1.3-3.a)

= {2(xi)}{Yi [a1 + a2 (xi)]} = 0 (1.3-3.b)

We have two equations in the two unknowns a1 and a2, so we may solve for a unique set of coefficients. Dividing Eqs. (1.3-3.a) and (1.3-3.b) by (2) and rearranging

a1 N + a2 xi = Yi (1.3-4.a)

a1 xi + a2 xixi = xiYi (1.3-4.b)

The system can be expressed in the matrix notation

A.a = B (1.3-5.a)

or

= (1.3-5.b)

The column vector a can be easily solved using the matrix capability of Matlab

a = A\B (1.3-6)

Example 1: The following data represent the concentration of reactant A in a constant volume reactor. (Ref. Module 3: Linear Regression by Bequette4 )

Time (min) 0 1 2 3 4 5CA, kmol/m3 8.47 5.00 2.95 1.82 1.05 0.71

If the reaction is first order, A --> B, determine the reaction rate constant k where rA(kmol/m3.min) = kCA.

Solution:The material balance for reactant A in a constant volume batch reactor is

4 Bequette, B.W., ”Process Dynamics Modeling, Analysis, and Simulation”, Prentice Hall, 1998

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= kCA

Separating variables and integrating:

= kdt

ln CA = ln CAo kt

where CAo is the initial concentration of A.

For this example: N = 6, y = ln CA, a1 = ln CAo, and x = t. The calculated values CAo and k can be obtained from the solution of Eqs. (a3-6) by using the matrix algebra capability of Matlab.

The coefficient matrix A and the column vector B can be determined by defining a new matrix w

w =

and the transpose of w

wT =

so that

A = wT*w and B = wT*Y

Matrix A can be obtained by the following Matlab statements:

>> f1=ones(6,1);

>> f2=[0; 1; 2; 3; 4; 5];

>> w=[f1 f2]

w = 1 0 1 1 1 2 1 3

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1 4 1 5

>> A=w'*w Note: w' is the Matlab notation for the transpose of w

A = 6 15 15 55

The right hand vector B is obtained by the following Matlab statements:

>> Ca=[8.47; 5; 2.95; 1.82; 1.05; 0.71];

>> Y=log(Ca)

Y = 2.1365e+000 1.6094e+000 1.0818e+000 5.9884e-001 4.8790e-002 -3.4249e-001

>> B=w'*Y

B = 5.1329e+000 4.0523e+000

The solution vector a is then

>> a=A\B

a = 2.1098e+000 -5.0171e-001

and the linear relationship between the variables is

ln CA = 2.1098 - 0.50171t

The same values for the parameters can also be obtained by using polyfit, a function provided by Matlab, to find the best linear fit of the data. % Matlab program for Example 1 % Least square curve fitting of ln(Ca) = ln(Cao) - kt%t=[0 1 2 3 4 5];

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Ca=[8.47 5 2.95 1.82 1.05 0.71];Y=log(Ca);ap=polyfit(t,Y,1)

ap = -0.5017 2.1098

You should notice that the first element in vector ap is the coefficient of the highest degree term. This is the convention used by Matlab in any polynomial functions. The experimental data and the best fitted line can be plotted by the following Matlab statements >> ycal=polyval(ap,t)

ycal = 2.1098e+000 1.6081e+000 1.1063e+000 6.0463e-001 1.0291e-001 -3.9880e-001

>> plot(t,ycal,t,Y,'o')

>> ylabel ('ln Ca'); xlabel ('t, min')

The parameters ap(1) and ap(2) are converted back to the physical parameters:

>> Cao_cal=exp(ap(2))Cao_cal = 8.2464e+000k=-ap(1)k = 5.0171e-001

The experimental data and the fitted model can also be compared on a time-concentration plot by the following Matlab statements. The results are presented in Figure a3.1

>> t1=0:0.25:5;>> Ca_cal=Cao_cal*exp(-k*t1);>> plot(t1,Ca_cal,t,Ca,'o')>> ylabel('Ca');xlabel('t,min')

A crude measure of the how well the data is fitted by a straight line is given by the linear correlation coefficient r, which is defined for two variables t and Y as

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r =

where

Ct = CY =

Values of r may range from (-1) to (1). The positive value indicates a positive correlation, i.e., the dependent variable is increasing with the independent variable. If |r| is exactly 1, the data is perfectly represented by the straight line.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

1

2

3

4

5

6

7

8

9

Ca

t,min

Figure 2. Experimental and fitted concentration as a function of time

The correlation coefficient for the straight line (Y = ln CA = 2.1098 - 0.50171t) can be evaluated by the following Matlab statements:

% corre.m% Linear correlation coefficient r of two vector t and Y%t = [0; 1; 2; 3; 4; 5];Ca = [8.47; 5; 2.95; 1.82; 1.05; 0.71];Y = log(Ca); N = length(t);sumt = sum(t); sumY= sum(Y);

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sumts = t’*t; sumYs = Y’*Y; sumtY = t’*Y;ct = N*sumts - sumt*sumt;cy = N*sumYs - sumY*sumY;r = (N*sumtY - sumt*sumY)/sqrt(ct*cy)

The linear correlation coefficient for this example is -9.9915e-001

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Laboratory Report

Many organizations that employ engineers have standardized their calculation methods for project progress reports. This is done to facilitate communication within the organization and to provide better control of work quality. Standardization permits routine checking of work as well as the opportunity to switch people to the tasks that are the most urgent.

In your career at Cal Poly you have used a variety of report formats. Some of these were quite structured and others were not. Now that you are a staff member with CAL CHEM Corporation, we would like you to use our style of reporting. The format is designed to facilitate the communication of information to your supervisor (instructor).

An engineering report is usually written to tell someone (fellow engineers, supervisor, etc.) about work you have done. Frequently, you will have been given a problem and will be responding with your answer or solution to the questions raised by the request. You should give your recommended solution, supporting evidence, and your opinion of the solution, at the least. You may want to tell other things as well, but there is no reason to load up the report with unnecessary information from standard texts or minute detail that is of no value to the reader. The written report should have the following structure:

Title Page—The title page should give the title or subject of the experiment, group number, group member names, course title, course number, course section, date, and university and department names.

Executive Summary—This should be a short letter to the instructor stating the problem you studied and summarizing your results and conclusions. A detailed description of an executive summary is given in page 19.

Results—This should be the most extensive portion of your report; concentrate on making this section informative and readable. Remember that results are the numbers you measured in the lab or calculations based on these numbers. Present the results of your experimental study here. State the most important results first. Use graphs and tables to summarize the information if possible. Critically analyze the results, both their magnitudes and trends, and compare them with literature values. Theory should always provide the underlying basis for this analysis. Provide sufficient detail to support all of your conclusions in the next report section. Discuss both the accuracy and precision of your data here.

Conclusions—State your conclusions or solution to the problem, based on the analysis of your results. Remember that conclusions are opinions based on the analysis of your measured data or values calculated from the data. These opinions are always formed after you compare your data with literature values and with predicted values based on a theoretical study of the system.

Bibliography—Cite any references used.

Appendix—Group all supplemental information in this section. Include your original data sheets and sketches and your experimental procedure. Include a detailed sample calculation here. List each equation you used, with definitions for variable symbols, and then substitute

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data values, with units, for each variable. This calculation should be applied to one data set in a logical progression through all the equations you used in your data analysis. Tables from Excel are not considered a sample calculation. Include your answers to questions here.

Because the lab handout will often include relevant theory to save you time, this report format does not contain a theory section. However, your results section should cover the agreement or disagreement of your results with theory. You must include a revised experimental procedure that reflects your experiences in taking the data. It is appropriate to discuss the experimental procedure when it influences the quality of your data.

Remember, this lab write-up is supposed to be a report to your supervisor on the assignment you were given. Write in a readable style and be as concise and clear as possible. Clear and effective writing will always require a draft copy, which you should go over, correct, and improve before typing the final version. Good writing is expected, and it will be an important criterion for establishing your grade.

Unless given other instructions, students will submit group laboratory reports. Each group member is responsible for the entire written report. Inclusion of work from other, non-referenced sources will result in an "F" grade. Reports must be written in the format previously described. All reports must be double-spaced, typed.

Laboratory reports are due one week after the scheduled completion of experimental work, at the beginning of the laboratory period. Late reports will be penalized 20% per day. A ten minutes late report will be counted as one day late. Reports will be graded on both technical content and on the presentation of that content.

How to put together a laboratory report

1. ORGANIZE !!

2. Get good laboratory data and lots of it. (Read the section: Collection and Analysis of Experimental Data)

a. What is the error in the measurement of the variables?b. Identify runs as 1, 2, 3, etc. a different number for each run.c. Do all of this during the laboratory period.

3. Manipulate laboratory data so calculations can be performed. Do it in the laboratory.

4. Calculate results; for example Diameter, T, k, Cp or whatever.a. What is the error of a replicated experiment?b. Do it in laboratory.

5. Note that data and sample calculations go in the Appendix.

6. Correlate the experimental results with each other and with the literature or theoretical values.

a. Graphs are best if appropriate.

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b. If not a graph, then a table.c. Draw a curve through the data points of the graph. Draw the appropriate graph

(straight line or curve) according to what you know about the phenomenon. It is almost always incorrect to connect the data points by straight line. When drawing a curve, draw the proper one. If there is no inflection point do not show one.

d. Note that we usually compare experimental results to literature values. These are different from theoretical or correlated values that are calculated from a (probably very valid) theoretical or correlated equation.

e. Do as much of this as you can in the laboratory period.

7. Read the prescribed laboratory report format and FOLLOW IT.

8. Reports are prepared backwards!I) Do the Appendices and the experimental procedureII) Prepare result graphs or tables

a) Show consistency of experimental runsb) Compare and correlate experimental results with each otherc) Compare and correlate experimental results with literature data or with

theoretical results.d) Note that one of the best comparison is % error (be certain to show sign)

III) Discuss results. Put the result graphs or tables after the discussion.IV) Draw conclusions and make recommendationsV) Prepare the executive summary.

Note: Different formats may be used for other reports, in other courses. Some reports may ask for other information (Introduction, Background).

9. You are writing a technical report on a technical subject for a technical audience. Don't waste space and your reader's time with simplistic statements.

10. Number pages.

11. Number and title Graphs, Tables, Figures, and Appendix items

12. Aim to be "complete and concise" in the body of the report.

13. Watch significant figures. Usually three or four are appropriate. We are rarely justified in using more than four!

14. Prepare a Table of Contents.

15. The texts in reports will be read first. So any graph, table, figure or Appendix item should first be mentioned in the text. Be specific and state; for instance, "see Graph 3 for ∆T vs. time". Graph 3 should then appear on the next possible page.

16. Note that raw data tables or spreadsheets do not make concise, readable result tables. They should go in the Appendix.

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17. Do not forget the title page. An example title page is shown on page iii.

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EXECUTIVE SUMMARIES/ABSTRACTS(Excerpt from unknown source, CHE Department, Stanford University)

An abstract is a condensation of important information from a report or technical summary. It is usually one paragraph in length (approximately 200 words). It contains no equations, figures, tables, or graphs, and rarely contains references. It should be able to stand on its own, without either the title or the text, and should contain key words for referencing. An abstract should be written so that the maximum amount of information is contained in the minimum amount of space. There are two types of abstracts, those which describe the report and those which describe the work about which the report was written. The latter type is much more informative and will be used in this class.

An abstract should be written after the report is complete. It should be able to answer what was done, why it was done, how it was done, the outcome, and the significance of the results. Several general rules about abstracts are as follows.

1) Delete any sentence that does not make a positive contribution.2) The first sentence should say what was done.3) Address the general approach.4) Quote the most general results.5) State conclusions and significance

The first sentence should inform the reader of what was done, and sometimes why it was done. Examples of first sentences could be:

A materials balance approach was used to determine the location of the flame front for an in-situ oil shale retort.

The drag on a sphere in a viscous fluid was measured as a function of sphere size and flow rate.

The diffusivity of bovine serum albumin (BSA) in K-quark electrophoretic gel was measure and compared to literature values for BSA diffusivity in the major competing gels.

The next sentence or two should describe the method that was used to make the evaluations or comparisons raised in the first sentence. For example:

Off gases from an Occidental Oil (OXY-9) oil shale retort were analyzed by gas chromatography. The resultant values were used in a computer model that calculated the mass of oil shale combusted as a function of off-gas production and the location of the flame front.

Sphere of various sizes were attached to an apparatus designed to measure torque and placed in a channel of fluid that could be pumped at different rates. The torque measurements were converted into drag for each of the experiments and compared to predictions calculated according to the Sangani method.

Concentrated radiolabeled BSA was applied to one side of the gel. Strips of the gel were removed at hourly intervals. Radiolabel content was analyzed as a function of distance from the source. The diffusivity was calculated from this information based on a one-dimensional model.

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The next part of the abstract states the results. See the examples below:

The flame front predicted by the off-gas analysis lagged that indicated by the buried thermocouples.

The experimental results agreed well with the Sangani calculations at low flow rates (Re < 1) but deviated significantly at higher flow rates (Re > 10).

The measured diffusivity of BSA in K-quark gel was less than half that of the competing gels.

Finally, the significance of the results is stated:

Later mine analysis showed this to be the result of significant tunneling of the flame front. Both in-place thermocouples and off-gas analysis gave useful information about the location of a flame from in an in-situ oil shale retort.

The Sangani approach is quite effective for the prediction of the drag on a sphere for creeping flow conditions but must be used carefully at higher Reynold’s number flow.

Thus the K-quark gels will give better separation of components for lengthy separations than will the competing gels.

Oral Presentation

Each laboratory participant will give a 10-minute oral presentation during the quarter. This presentation will discuss the appropriate session's experimental work. The oral report will be graded according to the following criteria:

Talk within the time limit.Quality of the visual aids. Clarity and organization of the talk.Good speaking voice (clear, loud enough, well paced) with a dynamic styleRapport and interest developed in the audience.Technical accuracy, correctness, and completeness.Appropriateness of technical level.

The ability to speak before an audience is an invaluable asset that you must develop. This development, like writing, is a lifetime endeavor to reach perfection. Each presentation should be an improvement over the preceding one. A good presentation has the following structure:

1. Introduce the major points2. Explain each point in detail3. Review and summarize the major points

A good presentation requires that you start the preparation ahead of time and:

Produce uncluttered visual aidsOrganize the material well so that it is easy and logical to followPractice the presentation, if possible in front of an audience

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Speak to the entire audience instead of only to key peopleUse a conversational speaking style

Suggested content of your presentation:

* Define your problem. Explain the objective of your investigation. Discuss your experimental plan.* Describe your experimental system. Show a drawing (schematic) of your apparatus and explain it was operated.* Describe the difficulties you encountered. Recommend how you would overcome these difficulties.* Discuss measurements that were made, or should be taken. Recommend the best operating range for your independent variables.* Discuss how the experimental data was, or should have been, analyzed. Compare the actual results with the anticipated ones. Discuss your conclusions.* Make recommendations for the next group performing the experiment to get better data.

Note: An oral presentation must be thoroughly prepared and not considered a quick and easy form of communication since there will be no opportunity to revise the presented material at a later time. An oral presentation should be not longer than is necessary to present the essential information. An oral report must be prepared specifically for a given audience; you should never read your written report to the audience.

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