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*Hypothesis tests conducted at the 95 percent con dence level with H0: regression coef cients are equal and H1: regression coef cients are not equal for survey data versus Greene County data for each waste group. Waste generation data was available for 16 of the 21 waste groups from the Greene County Solid Waste District (external data source).
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All hypothesis tests resulted in a do not reject H0, the null hypothesis.
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As shown in the validation table, all the regression coef cients developed from the research data (national survey) and data gathered from other agencies were statistically equal at the 95 percent con dence level. This validated the research data and model ndings with an external source. The integrated model was also validated using an arti cial intelligence (AI) program (neural networks) to examine one typical waste group with a relatively large amount of research data. The commercial/government waste group was chosen with 124 company waste records. The results of the AI program yielded similar results to the multivariable regression model developed for this waste group (an average error estimate of 5.8 percent). The bene ts of AI are the ability for the program to learn as new data is entered to strengthen the predictions. One drawback of AI for this research is that larger amounts of data are required over that of regression modeling.
19.3 Demonstration of the Prediction of Solid Waste Generation Using the Developed Model
This section discusses a demonstration case study of the integrated environmental model s prediction capability and margin of error. An electronic manufacturing equipment company was randomly selected from the Greene County data set. Table 19.3 displays the actual data that was gathered from the company and the values predicted by the integrated environmental model. The company employed 60 people, paid $60 per ton to dispose of solid waste, and was not ISO 14001 certi ed. As shown in the table, the actual data was 13.3 tons or 5.4 percent more than the model prediction. At most, actual material composition tonnages were approximately 10 percent different than predictions with most approximately +/ 5 percent different. This random sample demonstration indicated the effectiveness and accuracy of the integrated environmental model. Figure 19.1 displays the graphical version of the data.
19.4 Performance Parameter Case Study
Two companies were selected from the electronic manufacturer s waste group to demonstrate the solid waste performance parameters. These companies waste records were provided by the Greene County (Ohio) Solid Waste District. One company
TABLE 19.3 MODEL PREDICTIONS FOR A SINGLE COMPANY IN THE ELECTRONIC MANUFACTURER S WASTE GROUP VERSUS ACTUAL DATA FROM A RANDOMLY SELECTED COMPANY IN THE WASTE GROUP
310 MODEL PREDICTIONS INDIVIDUAL COMPANY ACTUAL DATA* DIFFERENCE (TONS/YEAR) PERCENTAGE DIFFERENCE COMPOSITION PERCENTAGE DIFFERENCE
VARIABLE NAME
Number of employees
Average Comp. % 60 42 No 243.8 58.4 7.3 15.3 8.7 9.1 7.6 7.4 25.8 10.3 4.5 19.8 17.5 14.8 6.9 27.7 230.5 7.7 29.6 243.8 0.1 0.7 0.5 0.4 1.1 0.1 1.4 1.4 1.0 0.8 1.9 13.3 0.5 0.8 0.4 5.4 9.2% 5.3% 5.5% 6.0% 1.3% 9.0% 6.5% 1.7% 10.5% 2.5% 6.9% 7.8% 6.5% 10.2% 6.5% 5.4% 13.3 5.4%
x1 = 60 x2 = 42 x3 = No
Waste disposal cost per ton ($)
ISO 14001 certi cation
Total annual waste (tons) 23% 3% 7% 4% 4% 3% 3% 11% 4% 2% 8% 7% 6% 3% 12% 100% 13.8 16.1 18.4 4.6 9.2 25.4 6.9 6.9 9.2 9.2 16.1 6.9 53.0
MOP (tons/year)
1.0% 0.0% 0.7% 0.4% 0.3% 0.1% 0.0% 0.4% 0.2% 0.2% 0.1% 0.2% 0.1% 0.2% 0.1%
Newspaper
LDPE (tons/year)
PP (tons/year)
PS (tons/year)
HDPE (tons/year)
PET (tons/year)
Ferrous metals (tons/year)
Nonferrous metals (tons/year)
Aluminum cans (tons/year)
OCC (tons/year)
Wood (tons/year)
Food waste (tons/year)
Glass (tons/year)
Other (tons/year)
Totals
*Randomly selected from surveys provided by Greene County, Ohio.
From waste characterization (cluster analysis).
PERFORMANCE PARAMETER CASE STUDY
80 Annual solid waste tons Individual company actual data Model predictions
0 LDPE Glass NonHDPE Newpaper Ferrous Aluminum Wood Other MOP PP PS OCC PET Food
Material
Figure 19.1 Model predictions for a single company in the electronic manufacturer s waste group versus actual data from a randomly selected company in the waste group.
selected was evaluated as in control and one out of control. The contact from Greene County provided insights on each company and additional information. Table 19.4 and Fig. 19.2 provide an overview of the established performance parameter for each company, including data inputs for the integrated model.
TABLE 19.4 COMPARISON OF TWO SELECTED COMPANIES IN THE ELECTRONIC MANUFACTURER S WASTE GROUP DEMONSTRATING THE PERFORMANCE PARAMETERS VARIABLES AND PARAMETERS COMPANY 1 COMPANY 2
Number of employees Land ll disposal cost per ton ($) ISO 14001 Certi cation Actual annual solid waste generation (tons) Predicted annual solid waste generation (tons) Upper performance parameter* Lower performance parameter* Performance level
83 40 Yes 221 229 251 207 In control (within limits)
65 33 No 284 250 269 231 Out of control (beyond upper limit)
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