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134 8 14 20 16 36 123 15 141 183 70 43 58 19 86 24

102 10 39 32 1,173

collected is displayed in Table 19.1. The table displays the sources, location, and number of waste records collected. These data sources were identi ed from previous research conducted (Franchetti, 1999), professional waste management contacts, information gathered during the literature review, and Internet searches. Much raw data existed on waste generation levels of individual companies, but little company information that quanti ed waste generation rates. Many state and local environmental agencies collected waste generation data to use for various aggregate reports. These reports examined waste generation for entire geographical regions, not individual companies. The individual data was stored in les and rarely used for other purposes. This research consolidated some of this data to validate the developed model. The accuracy of this data is considered high because it was collected by the U.S. government at the state and local levels. Different methodologies and survey techniques were utilized to collect the existing data, and may contribute to variation.

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FIGURE 10-2.

To demonstrate the validation process for the developed environmental model, the waste records were gathered from one existing data source (Greene County, Ohio). Greene County data was used due to its high accuracy and reliability. From these waste records, regression equations were computed for the waste groups with ve or more records (this included 16 of the 20 waste groups). Hypothesis tests were conducted to determine if the regression coef cients from the existing data were equal to the developed model s coef cients at the 95 percent con dence level. Table 19.2 displays the results for the 16 waste groups developed from Greene County data.

16

TABLE 19.2 MODEL VALIDATION TESTS (HYPOTHESIS TESTS* TO EXAMINE IF SIGNIFICANT DIFFERENCES EXIST BETWEEN THE MODELS DEVELOPED FROM SURVEY DATA VERSUS ADDITIONAL DATA COLLECTED )

0.51 7.14 3.49 140.40 0.43

Summary of key measures (in millions).

0.62 7.10 3.75 132.43 0.40

0.880 0.190 0.880 0.990 0.180

I would like to thank Dave Hrynkiw at Solarbotics Ltd for his help in preparing this chapter. Over the past century, we have seen a relentless march toward a world of automation and ease. Ever since the industrial revolution, increases in efficiency have been made by using automatic devices and robots to take over the menial tasks of man. As a result, we have given ourselves over to the machine, and our modern world is dependent upon the functioning of these devices for our continued development and growth. This presents us with a little bit of a dilemma. The machines and automation of the industrial revolution were fueled by coal. More recently, oil, natural gas, and other fossil fuels have powered the wheels of industry and automation. While automation requires less human input, it does require energy. Thus we have enslaved ourselves to previously plentiful fossil fuel energy, and created a world which would be much changed without it. In the field of energy and robotics, we could then go on to say that if we want to push the boundaries of what we know, into the deep uncharted realms of space, exploring other planets (Figure 16-1) (or closer to home, remote inaccessible areas like the sea), we need to provide energy for these distant ventures this is hard with conventional means.

2.262 3.182 3.182 3.182 2.052

0.408 0.861 0.444 0.395 0.858 (Continued )

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