1. Experimental Data Analysis Prof. Terry A. Ring, Ph. D. Dept. Chemical & Fuels Engineering University of Utah http://www.che.utah.edu/~geoff/writing/index.html

2. Making Measurements Choice of Measurement Equipment Accuracy – systematic error associated with measurement. Precision – random error associated with measurement.

3. Definitions Error – the difference between the measured quantity and the ”true value.” The “true value” is not known!!! So how do you calculate the error?? Random errors - the disagreement between the measurements when the experiment is repeated Is repeating the measurement on the same sample a new experiment? Systematic errors - constant errors which are the same for all measurements. Bogus Data – mistake reading the instrument

4. Random Error Sources Judgement errors, estimate errors, parallax Fluctuating Conditions Digitization Disturbances such as mechanical vibrations or static electricty caused by solar activity Systematic Error Sources Calibration of instrument Environmental conditions different from calibration Technique – not at equilibrium or at steady state.

5. Statistics Meanx M Deviationx i -x M Standard Deviation  Confidence level or uncertainty, 95% confidence = 1.96  99% confidence = 2.58  Please note that Gaussian distributions do not rigorously apply to particles- log-normal is better. Mean and standard deviation have different definitions for non-Gaussian Distributions

6. Comparison of means – Student’s t- test v= n 1 +n 2 -2 use the t-value to calculate the probability, P, that the two means are the same.

7. Compare Two Instruments Measuring the Same Concentration

8. T-test – Cont.

9. Compare Two Instruments Measuring the Same Concentration

10. T-test - Cont

11. E stimating Uncertainties or Estimating Errors in Calculated Quantities –with Partial Derivatives G=f(y 1,y 2,y 3,…) http://www.itl.nist.gov/div898/handbook/mpc/section5/mpc55.htm

14. Way around the Partial Derivatives This approach applies no matter how large the uncertainties (Lyons, 1991). (i) Set all x i equal to their measured values and calculate f. Call this f o. (ii) Find the n values of f defined by f i = f(x 1,x 2,...,x i +  i,...,x n ) (11) (iii) Obtain  f from (12) If the uncertainties are small this should give the same result as (10). If the uncertainties are large, this numerical approach will provide a more realistic estimate of the uncertainty in f. The numerical approach may also be used to estimate the upper and lower values for the uncertainty in f because the f i in (11) can also be calculated with x i +  replaced by x i - .

15. Now try the same calculation using the spread sheet method. The dimensionless form of (12) is (after taking the square root) (17) The propagated fractional uncertainties using (15) and (17) are compared in Table 4. A further advantage of the numerical approach is that it can be used with simulations. In other words, the function f in (12) could be a complex mathematical model of a distillation column and f might be the mole fraction or flow rate of the light component in the distillate. Table 4. Uncertainties in Gas Velocity Calculated from (15) and (17) Equation Used  f /f 0 (9)0.011968 (12) with +  0.011827 (12) with -  0.012113 See web page with sample calculation done with Excel

16. Fitting Data Linear Equation – linear regression Non-linear Equation Linearize the equation- linear regression Non-linear least squares

17. Rejection of Data Points Maximum Acceptable Deviations (Chauvenet’s Criterion)

18. Example x i -x M /  =  8.9-8.2  /0.3=2.33 x i -x M /  =  7.9-8.2  /0.3=1.0

19. Regression Linear Regression –good for linear equations only Linearize non-linear equation first –linearization leads to errors Non-linear Regression –most accurate for non- linear equations –See Mathcad example

20. Residence Time Measurements Time(min) T T T=(T o -T in )exp(-t/tau)+T in

21. Temperature(C) Time (min) Non-Linear Fit Linearized Eq. Fit Flow Calc.s

22. Results