Industrial Engineering

Text: Climate Change and the Energy Problem David Goodstein and Michael Intriligator Copr. 2013 by World Scientific Publishing Co. ISBN: 978-981-4407-09-0
Q1 Analyze a drier that reduces the moisture content of bentonite from 11% (bone dry basis) to 4% bdb. Assume the plant processes 100 tons/day is a gas fired rotary kiln (8 ft ID x 120 ft long). The burners on high-fire contribute 12(106) Btu/hr to the system. Bentonite is a mineral mined in western USA. Refined bentonite has many industrial uses. The delivery contract for processed bentonite specifies 6% moisture (bdb) as acceptable maximum. Can the plant raise production moisture to 5%, or 5.5%. If that is physically possible, they might reduce fuel costs. Look into this idea. Your report (6 pages maximum) should include mass balances for water and bentonite. Include other energy usage reduction ideas that you think about.

Q2 Evaluate the transition from oil based world economy to an economy driven by alternative energy sources. Include economic and political analyses. Discuss new source transmission of energy (does this exist, even in the research phase?). Include an analysis of climate variation due to new source utilization. Limit 10 pages not including graphics.

Industrial Engineering

Regression
Assignment Objectives:
1.    Practice entering data into Minitab.
2.    Practice applying and interpreting data from a Regression
3.    Practice creating and interpreting graphs for Regression data
4.    Practice presenting data to an audience.
Step 1:
a.    Think of a relationship you are interested in learning more about, for instance, is one’s grade in a tutorial class a good predictor of his/her GPA?
b.    Then create a hypothesis statement to represent your relationship of interest (i.e. H0 and HA).
c.    Collect data to use in your analysis; or use data that we have already collected (e.g. Stroop data, Caliente Study data or Information Processing data); or you can find some other data that has already been gathered, on the Internet, for example.
d.    Enter collected data into a Minitab spreadsheet.
Step 2:
Complete a regression on the data to see if one variable (the IV) is a good predictor of another variable (the DV).
Your output should include:
a.    the ANOVA output with R and R-squared values
b.    Residual graphs
c.    Do you accept or reject your hypothesis?  Explain what this means in English.

Industrial Engineering

Regression
Assignment Objectives:
1.    Practice entering data into Minitab.
2.    Practice applying and interpreting data from a Regression
3.    Practice creating and interpreting graphs for Regression data
4.    Practice presenting data to an audience.
Step 1:
a.    Think of a relationship you are interested in learning more about, for instance, is one’s grade in a tutorial class a good predictor of his/her GPA?
b.    Then create a hypothesis statement to represent your relationship of interest (i.e. H0 and HA).
c.    Collect data to use in your analysis; or use data that we have already collected (e.g. Stroop data, Caliente Study data or Information Processing data); or you can find some other data that has already been gathered, on the Internet, for example.
d.    Enter collected data into a Minitab spreadsheet.
Step 2:
Complete a regression on the data to see if one variable (the IV) is a good predictor of another variable (the DV).
Your output should include:
a.    the ANOVA output with R and R-squared values
b.    Residual graphs
c.    Do you accept or reject your hypothesis?  Explain what this means in English.