Data series name (including data period or year of publication)

RDF/SRF

type/

trademark

Type(s) of available statistic

Other

background

Information

Type of МВТ process

Case

number

Assumptions-

limitations

References

Country

Heering et al.^]

Herhof (TST) PR2 (1999 publication)

Herhof—Asslar plant

(2001 publication)

Nehlsen (Calobren; 1999-2003 data)

Germany Biodrying Germany Biodrying Germany Biodrying

Trocken Stabilat (TST) or Stabilat Trocken Stabilat (TST) or Stabilat Calobren

Data from

peer-reviewed publication (PR2)

Calorific value assumed net (ar) Cl assumed ar

Calorific value assumed net (ar) Cl assumed ar

Calorific values assumed net

Mean

(arithmetic)

Mean

(arithmetic)

Mean Median 80th-P

Heering²⁴⁵ cited by Gendebien et al.³² Juniper⁵

Data coverage: October 1999-June 2003—use in cement kiln

Nehlsen (Calobren; 2000 publication)

Germany Biodrying Calobren

Calorific value assumed net (ar) Cl assumed ar

Zeschmar-Lahl et al.²⁴⁶ cited by Gendebien et al.³² Juniper⁵

Mean

Eco-deco—Montanaso plant (2002-2004 data)

Italy

Biodrying

Eco-deco SRF

Mean Median Standard deviation

As-received (ar) net calorific values were transformed to dry (d) values following the CEN formula—leading to much higher d values that the ones originally reported Cu: soluble Pb: volatile

Herhof (TST) PR1 (2003 publication)

Germany Biodrying TST

Data from

peer-reviewed publication (PR1)

Niederdrank et al.²⁰³

Mean

Recofuel/SBS 2 Recofuel/SBS 1 TST

Eco-deco SRF

van Tubergen et al.¹⁸³

van Tubergen et al.¹⁸³

Juniper⁵

Juniper⁵

10

Cuperus et al.²¹³

The Biodrying

Netherlands

Mean Median 80th-P Standard deviation Median 80th-P

Mean Median 80th-P Standard deviation Median 80th-P

Van Tubergen et al.183 Cuperus et al.213

TST

Germany

The

Netherlands

Italy

Average of TST (Her-

hof/Fusina) Eco-deco SRF and Pirelli TST

van Tubergen et al.¹⁸³

Paoli et al.²²²

Italy

Mean

Remondis (SBS 2: for Germany cement kilns; 2003 publication)

Remondis (SBS 1: for Germany power plants; 2003 publication)

Herhof (TST) Renerod Germany plant (2003 data)

Eco- Italy

deco—Lacchiarella plant (2003-2004 data)

11. Biodrying—TAUW

data (2005 publication)

11. Herhof (TST)—(2005

publication)

11. МВТ—high-CV

fraction—TAUW data (2005 publication)

11. Italian SRF average

(2005 publication)

15 Herhof—Vesta Fusina plant (2006 data)

Biodrying

Mechanical sorting of high-CV fraction in МВТ

Average including cases of biodrying and mechanical sorting of high CV fraction

Biodrying

Mechanical sorting of high CV fraction in МВТ

Mechanical sorting of high CV fraction in МВТ

Biodrying

Biodrying

Median 80th-P More than 10 data points

Median 80th-P More than 10 data points

Median 80th-P Based on 70 samples

Mean Median Standard deviation

Independent

investigation

(Site D)

More than 10 data points Independent investigation

(Site A)

More than 10 data points

As-received (ar) net calorific values were transformed to dry (d) values following the CEN formula—leading to much higher d values that the ones originally reported Cu: soluble Pb: volatile Sampling plan limitations

Dry basis assumed for ash content

Cu: soluble Pb: volatile

analysis before final reporting of data in the literature restrict the potential for meaningful comparison of data by further statistical analysis. Additionally, not all the diversifying methodological details of measurement are clearly stated for each case. It has been proposed that the number of analyses per­formed for each data series can cautiously be used to guarantee a minimum reliability of the data.¹⁸³ The majority of the data sets used in our analysis are based on >10 samples/analyses (lower limit proposed by CEN).

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he effect of sampling plans on specific data series is anticipated to be critical and much higher than the uncertainties introduced by differences at the analytical stage. The CEN guidance for SRF sampling plans²²³ DD CEN/TS 15442:2006 incorporated elements of the latest developments in Gy’s theory of sampling^224-227developed over the last 50 years.²²⁸ Enhanced reliability can be expected for data series. Specifically for those that: (1) follow the sampling theory, such as the Tauw investigation²¹³; (2) comply with national quality assurance systems, such as the Italian Eco-deco data⁵; (3) constitute average measurements over long time periods (Nehlsen plant)⁵; (4) have been obtained independently, such as the Herhof-Stabilate SRF, investigated by Niederdrank et al.²⁰³

Publicly available data are statistically analyzed to produce the most rep­resentative and thorough overview of the achieved quality by MBT-derived RDF/SRF to date. Available statistics of input data on RDF/SRF sample prop­erties is largely limited to (1) measure of (central) location of the sample population (mean and/or median); (2) measure of the upper limit (80th per­centile and/or max) values; and (3) limited reported values for their spread (typically in the form of standard deviation).

The selection and validity of statistical tools for describing properties of waste is highly debatable. Certain aspects of the ongoing debate are addressed by van Tubergen et al.¹⁸³ and Pehlken et al.⁷⁷ Simplifying, it can be generally assumed that population distributions (or probability density function curves) for many of the properties of waste-derived products exhibit skewness and lengthy distribution tails (long-tailedness).⁷⁷ Typically, positive skewness is evident.

During the prenormative research for CEN SRF standards, TAUW in­vestigated the type of statistical distribution that is suitable to describe the properties of SRF. The study included outputs from biodrying plants and from mechanical sorting of the high-CV fraction of residual waste, analyzing 35 samples for each case. Results of the Kolmogorov-Smirnov test for Cl, Hg, Cu, and Cr have shown that for both plant cases the type of distribution that best fits these properties was log-normal rather than normal, with the exception of Hg for biodrying, which was normally distributed.²¹³ However, the CEN technical committee draft has opted for Cl to be classified by the mean rather than the median, implying that a normal distribution would suf­fice; indeed there is a certain degree of overlap between the two forms of distribution. Conversely, Hg has to be classified according to both median

and 80th percentile values, implying that a skewed long-tailed distribution is anticipated.

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ther data reported on SRF from Eco-deco and Nehlsen biodrying pro- cesses⁵ indirectly verify that many properties are not fully normally dis­tributed. Data series for which values for both mean and median are available show varying degrees of difference between these two statistics, establishing that their distributions are skewed and, hence, not normal. For instance, Pb and Cr show clear positive skewness in all three available data sets, whereas Hg shows mixed behavior, including one case of negative skewness.

The central trend and the spread of the values for each property are estimated by calculating and graphically presenting in box-plots the min­imum, lower quartile, median, upper quartile, and maximum value for both median and 80th percentile of input data. Calculations and graphics were made with Statistica 8,²²⁹ following typical box-plot convetions (see Figure 20). Preferably, statistical analysis should be performed only on me­dian and 80th percentile input SRF data statistics, but in practice means were also used to indicate the location. Median and 80th percentile are measures of location and upper values, respectively, independent of the form of distribution and robust to outliers. They are therefore suitable to describe properties of RDF/SRF that show log-normal distribution or skew­ness and long-tailedness in general. In the case of normally distributed property, median is identical to the arithmetic mean. High percentiles, like the 80th percentile, are particularly useful for estimating the upper values of concentrations of trace metals of concern (e.g., Cd), as demanded in practice. The alternative would be to employ data points that are statis­tics based on normally distributed populations (arithmetic mean, standard deviation, etc.); however, this assumption does not hold for many of the waste measurands, especially for analytes expected to be present at trace concentrations.

However, median values were not available for all the cases; as a com­promise mean values were also used to indicate the location of input data series. For properties that exhibit positive skewness, the overall average cen­tral trend of the median values is biased toward higher values when means are used instead of medians. However, the differences between medians and means, despite evidence for nonsymmetrically distributed properties, are not significant in all cases, as shown by the cases for which comparative data are available. The number of means used instead of medians for each property are shown in Table 19. For Pb only mean values were used, because there were very limited median values. Given that Pb exhibits positive skewness, the average location of Pb is then overestimated.

The significance of the results is also restricted by the fact that data series of different reliability are given equal, nonweighed treatment (e.g., values resulting from long and short time series results) and by the low number of available data series on RDF/SRF properties, ranging from 14

Input data property and statistic	Designation of relevant property	Units	Max	Q3 (Upper quartile)	Qi (Lower Median quartile)		Min	Number of data seriesa	Medians- Meansb	CV (Coefficient of variation,%)	Equivalent, hypothetical CEN classification
Location of moisture	MC	% w/w ar	24.4	14.2	13.4	12.0	11.7	8	5-3	28.8
content
Upper limit (80th	MC	% w/w ar	31.5	n.a.	19.8	n.a.	15.1	4	n.a.	28.8
percentile) of moisture content
Location of ash content Location of net calorific	Qp,net	% w/w ar MJ kg-1 ar	28.3 19.9	25.1 16.8	21.1 16.3	18.2 15.4	17.6 13.0	7 8	2 3	25.3 13.1	CEN class for hypothetic
value ar Location of net calorific	Qp,net	MJ kg^1 d	28.3	24.33	22.60	20.27	16.50	8	6-2	18.8	SRF with same median value: 3
value d
Upper limit (80th	Qp,net	% w/w ar	37.22	n.a.	26.47	n.a.	18.62	5	n.a.	25.7
percentile) of net calorific value d
Location of chlorine	[Cl]	% w/w d	1.05	0.72	0.50	0.42	0.29	13	9-4	40.0	CEN class for hypothetic
Upper limit (80th	[Cl]	% w/w d	1.11	1.00	0.98	0.53	0.39	7	n.a.	36.9	SRF with same mean value: 2
percentile) of chlorine
Location of suflur	[S]	% w/w d	0.40	0.33	0.24	0.20	0.18	6	3-3	32.9
Location of arsenic	[As]	mg kg-1 d	4.0	3.4	1.1	0.7	0.5	8	4-4	78.6
Location of cadmium	[Cd]	mg kg-1 d	2.6	2.2	1.8	1.2	0.4	8	4-4	47.3
Location of chromium	[Cr]	mg kg-1 d	192	90	78	60	40	11	6-5	49.8

TABLE 19. Results on statistical analysis of МВТ-derived RDF/SRF data-series

Output statistic

Data quality

Location of copper	[Cu]	mg kg-1 d	694	448	198	73	14	10	6-4	95.9
Location of lead	[Pb]	mg kg-1 d	230	208	152	88	71	8	0-8	48.3
Location of mercury (mass basis)	[Hg]	mg kg-1 d	1.50	0.75	0.43	0.33	0.15	12	9-3	71.4
Upper limit (80th percentile) of mercury (mass basis)	[Hg]	mg kg-1 d	1.11	0.80	0.53	0.38	0.22	7	n.a.	51.1
Location of mercury (energy basis)	[Hg]	mg MJ-1 ar	0.040	0.037	0.023	0.014	0.009	6	5-1	53.6
Upper limit (80th percentile) of mercury (energy basis)	[Hg]	mg MJ-1 ar	0.045	n.a.	0.024	n.a.	0.009	4	n.a.	47.7
Location of nickel	[Ni]	mg kg-1 d	97	40	28	24	18	9	4-5	66.4
Upper limit (80th	[£(Sb, As, Cd,	mg kg-1 ar	1,025	n.a.	795	n.a.	581	4	n.a.	23.5

percentile) of CEN SRF sum of “heavy metals”

Cr, Co, Cu, Pb, Mn, Hg, Ni, Tl, V)]

CEN class for hypothetic SRF with same median and 80^th P value: 2

“Number of data entries that were used to calculate the related statistics affecting their reliability.

^bTo estimate the location, median values were used if available; otherwise, arithmetic means: this column provides the exact numbers. Depending on the skewness of the initial statistic of a certain property, use of means instead of medians could mean overestimation on the median output location (if the property is positively skewed, as is typical for waste) or vice versa, n.a.: not available.

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o 4 (properties with fewer data points available are not analyzed). As- received (ar) values were converted to dry basis (d) for the properties nec­essary to do so using the median(/mean) moisture content values, where available. As-received net calorific values were transformed into dry ba­sis by applying the proposed CEN formula²³⁰ for the calculation of anal­yses to different bases.²³¹ This has rendered certain NCV (d) to be much higher than values reported in the literature (e.g., Eco-deco values⁵), pos­sibly calculated by a different conversion formula. Nevertheless, given the limitations, calculation of medians and use of box-plots can provide the most appropriate, accurate, and clearly presented account for the RDF/SRF properties.

FIGURE 20. Results of descriptive statistical analysis on MBT-derived SRF data: comparison of the location values of net calorific value Q_p>net, expressed on an as-received (ar) and dry (d) basis. Box-plot conventions: (1): lower and upper lines of the boxes denote the 25th and 75th percentiles; (2) lower outlier and upper outlier limits, denoted by whiskers define the nonoutlier range; that is, range of values defined as those that do not differ from the median more than the 25th or 75th percentile plus 1.5 times the interquartile range (height of box); (3) extreme values, presented as asterisks, exceed the 75th percentile plus three times the interquartile range.

FIGURE 21. Results of descriptive statistical analysis on MBT-derived SRF data: (1) location values of moisture content MC expressed on an as received basis (ar); and (2) location values of ash content expressed on a dry basis (d). For box-plot conventions see Figure 20.

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esults and Discussion of Statistical Analysis on MBT-Derived RDF/SRF Properties

Results related to key technical, environmental, and economic aspects of RDF/SRF quality are summarized in Table 19. Box-plots are used to graphi­cally represent the main findings (Figures 20-26).

Results were generally within the expected range and are logically con­sistent. The 80th percentile values are higher than the median values, with certain exceptions, such as the maximum values of Hg reported on a mass basis. Coefficients of variation (CV;%) are calculated for each statistic to es­timate the scale-free dispersion of the analysed populations. The CV ranged from 13.1% for the location of NCV ar to 95.9% for the location of Cu.

The location of median NCV ar (Figure 20) falls within the range of Class 3 of the proposed CEN classes, with the lower and upper quartiles being at 15.4 and 16.8 MJ kg^-1 ar respectively, showing a narrow spread. The location of MC medians (Figure 21) is at 13.4% w/w ar, with the maximum value being more than three times the interquartile range higher than the upper quartile (Q3 = 14.2% w/w ar) and hence depicted as an extreme value (star) in

FIGURE 22. Results of descriptive statistical analysis on MBT-derived SRF data: comparison of location and 80th percentile values of chlorine concentration [Cl], expressed on a dry basis (d). For box-plot conventions see Figure 20.

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he box-plot. The ash content location of medians (Figure 21) is at 21.1% w/w, but the upper quartile reaches as high as 25.1% w/w, which can be uninviting for the most demanding end uses of RDF/SRF.

The Cl content quality achieved by MBT-derived SRF appears generally good (Figure 22), but this result should be cautiously interpreted. The central trend of Cl content median values is at 0.50% w/w d; a hypothetical SRF with the same value would be classified as Cl class 2. The maximum reported lo­cation value is at 1.05% w/w d. However, the data series included RDF/SRF with unexpectedly low location values for Cl, especially for measurements reported on a dry basis and stemming from plants that have not yet incorpo­rated sophisticated sorting (e.g., NIR) to screen out high-Cl components: the minimum being at 0.29% w/w d. Detailed calculations by Schirmer et al.²³² established that the average total Cl content (TCC) in MBT-derived RDF/SRF produced from residual MSW can be anticipated to fall in the range of

1. 6-0.8% w/w d. Anticipated values for RDF/SRF produced from biodrying plant configurations are ca. 0.1% w/w d higher than values for MBT plants that just separate mechanically RDF/SRF before biological treatment.²³² We

FIGURE 23. Results of descriptive statistical analysis on MBT-derived SRF data: comparison of location of concentration of trace elements, expressed on a dry basis (d). The As, Cd, and Hg are further compared in Figure 24 using a suitable axis scale. For box-plot conventions see Figure 20.

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peculate that the unexpectedly low values reported in the literature could re­flect difficulties in the sampling methods and analytical measurements used. Schirmer et al.¹⁵⁹ have pointed out the particular challenge of determining total chlorine. Importantly, they stressed that combustion digestion methods underestimate the inorganic portion of Cl in samples with high ash content, which is problematic for QA/QC purposes.

The data indicate that the content of MBT-derived RDF/SRF in trace elements of concern (often called heavy metals) is generally acceptable in most cases (Figures 23-26). This can be illustrated by comparing the upper quartile of the location data for trace elements (range that includes the 75% of the population values, from low to high) with, for example, the German limits for SRF¹⁷⁹): As: Q3[As] = 3.4 mg kg^-1 d < German limit = 5 mg kg^-1 d; Cd: Q3[Cd] = 2.2 mg kg^-1 d < German limit = 4 mg kg^-1 d; Cr: Q3[Cr] = 90 mg kg^-1 d < German limit = 125 mg kg^-1 d; Ni: Q3[Ni] = 40 mg kg^-1 d < German limit = 80 mg kg^-1 d. From the elements for which sufficient data series existed for statistical analysis, only Cu (Q3[Cu] = 448 mg kg^-1 d < German limit = 350 mg kg^-1 d) and Pb (Q3[Pb] = 208 mg kg^-1 d < German

FIGURE 24. Results of descriptive statistical analysis on MBT-derived SRF data: comparison of location of concentration of certain trace elements, expressed on a dry basis (d). See Figure 23 for comparison with more elements. For box-plot conventions see Figure 20.

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imit = 190 mg kg^-1 d) upper quartiles of location exceed the limits. The spread of the Cu values reported in the literature is much higher than that of Cr, as can be seen from their interquartile ranges in Figure 23. For data regarding each plant the 80th percentile values are considerably higher than the median in many cases. This could verify the need to resort to statistical description and classification of trace element content with an indication of both location (median) and upper values (80th percentile).

However, this is slightly less evident for Hg. Reported on a mass basis (Figure 25), the location of medians is at 0.46 mg kg^-1 d and location of 80th percentile at 0.53 mg kg^-1 d. Reported on an energy basis (Figure 26), in agreement with the CEN classification system, the location of Hg medians is at 0.023 mg MJ^-1 ar and the location of 80th percentiles is at 0.024 mg MJ^-1 ar, showing unexpectedly similar values. This may be due to fewer data points for the 80th percentile. Hence, an equivalent imaginary SRF with the same median and 80th percentile values would be cautiously classified as CEN Hg Class 2, which is toward the high end of possible qualities.

FIGURE 25. Results of descriptive statistical analysis on MBT-derived SRF data: comparison of location and 80th percentile values of mass-based mercury concentration [Hg], expressed on a dry basis (d). For box-plot conventions see Figure 20.

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ONCLUSIONS AND RECOMMENDATIONS

A significant objective for MBT is to achieve effective material flow man­agement of residual waste that involves extracting homogeneous fractions of known biochemical composition. The benefits of this are twofold: to concen­trate contaminants separately and direct them toward appropriate onward disposal or treatment mechanisms and to produce recyclates to a desired quality, prolonging their residence time in the anthroposphere.

Appropriate Descriptors for Evaluating Unit Process Operations

Because MBT is a generic process that can be broken down into many pro­cess unit operations, it is important to understand, through characterization studies, the relative contribution that each unit makes by using appropriate descriptors. Conventional descriptors such as yield and purity of waste com­ponents have typically have been applied. PSD, if used appropriately, can be useful in describing comminution results and for modeling size-dependent mechanical processing. The move from a predominantly disposal-led waste

FIGURE 26. Results of descriptive statistical analysis on MBT-derived SRF data: comparison of location and 80th percentile values of energy-based mercury concentration [Hg], expressed on an as-received basis (ar). For box-plot conventions see Figure 20.

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ector to one that is more resource based demands the use of analytical tools such as MFA. MFA, via TCs and MECs, can provide the ability to map flows of preserved properties of waste, such as trace elements, into the output fractions. This can enable the optimization of MBT processes to effectively separate waste fractions into outputs of desired quality; for example, known chemical composition. This is of value to waste companies because it pro­vides the basis for a targeted reduction in pollution load of MBT outputs, which potentially can have a positive effect on end-user emissions.

Performance of Process Units and Implications for Output Quality and MBT Design

MFA has recently been employed to accurately map and predict behavior of MBT plants. Despite some very promising experimental results reported in recent studies, most of the data come from theoretical investigations. There is a need for additional experimental MFA research on a test scale and full commercial basis.

MFA data have so far demonstrated the difficulty in achieving effective chemical separation solely by mechanical means. The recent application of additional processing technology combined with mechanical methods in MBT plants, such as NIR sorting, three-stage ballistic separators, and X-ray sorting, offers a significant opportunity to improve this situation.

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here is invariably a trade-off between achieving a high quality of recov­erable outputs and the properties of reject material; for example, RDF/SRF with a low Cl content can result in a significant yield of rejects that require subsequent treatment or disposal. The selection of intermediate and final sinks of materials diverted away from RDF/SRF production therefore needs careful consideration. Adittionally, attempting to produce SRF of higher spec­ifications and more consistent quality may result in more energy-demanding plant configurations, adversely affecting the sustainability of such choices.

Recent studies have provided results for particular aspects of process unit operations. Biodrying systems appear to offer the advantage of opti­mally preparing the waste for mechanical treatment. Latest developments on biodrying are covered by the authors elsewhere.²⁴⁷ Promising results for selective comminution and fast biodegradation were achieved by ball mill pretreatment. Zn and Cl in particular are difficult to dilute in RDF/SRF pro­duced from residual MSW because of their highly diffused distribution in waste components. To achieve higher quality recoverable outputs there is need for careful choices regarding selection of unit operations and their ar­rangement in the plant flowsheet, along with improvements in the design of existing processing equipment and use of novel but proven solutions. For example, with the objective of high-grade SRF production, it is not sufficient to separate a comminuted coarse fraction just on a PSD basis.

Quality Management for SRF Production

The importance of implementing quality management schemes to the SRF production line of MBT plants is gaining recognition. The CEN/TC 343 ini­tiative for harmonization across Europe is significant in setting a new bench­mark but is still under validation.

When dealing with highly heterogeneous material streams such as resid­ual waste or SRF, the adoption of robust standardized sampling plans based on established sampling theory which takes account of the inherent hetero­geneity of waste materials is vital.

Differentiation of SRF production to meet specific end-user require­ments is vital for the future marketability of SRF due to the different needs of end-users; for example, certain SRF quality criteria for cocombustion in power plants are higher than those applying to SRF-dedicated thermal re­covery facilities such as gasification plants. A satisfactory understanding of the exact behavior of MBT-derived SRF during the many available thermal recovery processes is still needed. Sufficient characterization and long-term

operational data at higher degrees of SRF substitution as an alternative fuel in cocombustion outlets are necessary in order to estimate the technical feasibil­ity for SRF. Such results could inform the debate surrounding specifications.

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iogenic content is becoming an increasingly important descriptor, re­flecting the increased recognition of the potential global warming effect of waste. There is a need to standardize the measurement methodology be­fore biogenic content can be determined accurately. To this, the authors recently presented new insights.²⁴⁸ Data produced from validated method­ologies could support scientifically sound regulation.

Statistical Analysis for MBT-Derived SRF

Results from literature data on MBT-derived RDF/SRF were generally within the expected range and logically consistent. SRF achieved quality could po­tentially prove able to respond to legislative and market needs if improve­ments are achieved; for example, reduction of the Cl content. Coefficient of variation ranged from 13 to 96%, which reasonably reflects the variability in SRF production, properties, and measurement conditions.

The location of median net calorific values (ar) falls within the range of Class 3 of the proposed CEN classes, showing a narrow spread. The ash content location of medians is at 21% w/w, but the upper quartile reaches as high as 25% w/w, which is less appealing to the most demanding end-users of RDF/SRF. The Cl content appears generally good; the central trend of Cl content median values is at 0.5% w/w d. However, we speculate that the unexpectedly low values reported in the literature could reflect unresolved issues related to sampling plans and the analytical methods used. Reported data indicate that the content of trace elements in MBT-derived RDF/SRF is generally acceptable in most cases (within end-user specifications or regu­latory limits). From the elements for which sufficient data series existed to enable statistical analysis, only Cu and Pb showed a high potential to exceed the German specification limits.

General Evaluation

Most of the unit operations currently used in MBT plants have an established track record. The waste input materials, specific MBT plant objectives, and output requirements have evolved considerably since the earlier RDF plants and associated dirty composting plants that relied on mechanical processing. However, insufficient scientifically derived data are available in the public domain on the performance of individual process unit operations that would inform the design of MBT plants to meet the needs of the modern sustainable resource management agenda.

Mixed MSW is a challenging waste stream for waste treatment processes such as MBT. However, through improved upstream source separation (e.g.,

removal of dry recyclates, food waste, or green waste for composting), segre­gation, and recognition of changing waste streams, the properties of residual MSW are changing significantly. The biodegradable content, for example, may be expected to significantly reduce during the next 10 years. This will have an impact on the performance of MBT systems that are yet to demon­strate an ability to adapt.

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his review relates to material flow management. However, there are important wider considerations to be made. MBT is generally a highly mech­anized process that is energy intensive. A wider sustainability appraisal of MBT performance, compared with alternative technologies such as anaer­obic digestion, therefore warrants investigation to consider issues such as energy consumption, emissions, and value in materials recovery.

Additional data on specific material properties (e.g., physical properties) are needed to build confidence on MBT-derived SRF as a viable alternative to fossil fuels. Further research is needed to enhance our understanding of what constitutes appropriate data at the operational and regulatory level and suitable statistical analysis for MBT-derived SRF and other waste-derived products to enable appropriate and harmonized reporting in the future. The performance of MBT systems requires continued scrutiny to establish a vi­able waste treatment technology for improved handling of material flows in accordance with sustainable resource management.

ACKNOWLEDGMENTS

The funding for this project is made possible by donations from Waste Recy­cling Group Limited through the Landfill Communities Fund of Grantscape (Project No. URN 158/327). Additional donations have been made by the following organizations for which the project team is grateful: Biffa Waste Services Ltd., Cambridgeshire County Council, Cemex, Defra, Donarbon Ltd., Dorset County Council, Enviros, Luton Borough Council, Norfolk Environ­mental Waste Services, SITA, Summerleaze Ltd., Sustainable Development Research Centre, Viridor, and Wasteology Systems Ltd. We acknowledge the members of the project steering group, Gareth Davies for helping with spe­cific figures, and Rebecca Roy for assisting with formatting of the manuscript.

NOMENCLATURE ar As-received (or wet) basis of reporting*

С Purity: mass fraction of waste component (or collection of compo­

nents); i.e., С(S) or С(B)

CM Component (or collection of components) in a stream; e.g., S or B CV Coefficient of variation

c Concentration of substance; i.e., cm(s) mass-based concentration

of substance (s) d Dry basis of reporting*

d Particle size

daf Dry, ash-free basis of reporting^*

de Effective particle size

dx Cumulative fraction finer than d: x percentage passing through the

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creen/sieve (undersize fraction, underflow) with aperture size d d6₃.₂ Characteristic particle size (Y(d) = 0.632) d9₀ Nominal product size (Y(d) = 0.9) d95 Nominal top size (Y(d) = 0.95)

E Total effectiveness

G Grade efficiency (or selectivity)

I Input stream

k Sharpness of cut (of a grade efficiency curve)

LHV Lower heating value MC Moisture content MCair Moisture content of air MCwaste Moisture content of waste matrix Mg Mega gram (or ton)

Mg a^-1 Mega gram per year (or tpa: ton per annum)

MEC Material enrichment coefficient

m Mass or mass flow rate

NCM Set of components other than CM

NF Narrow range fraction (portion of waste particulates that fall within

a defined size range) n Measure of uniformity (breadth) of PSD

1. Diameter

P Product (output stream)

R Recovery

S Waste component (or collection of components) containing sub­

stances of concern

s Substance (according to the definition of MFA, an element or

chemical compound that is preserved through a process)

TC Transfer coefficient (or transfer factor)

w/w Weight fraction or percentage Y Yield

Y(d) Particle size distribution (PSD) function Greek Symbols

^_dmesh Size reduction ratio measured for screen/sieve with aperture d т Mean residence time

Subscripts

dmesh Screen/sieve with aperture d

e Energy basis of ratios

I Input to unit operation

i Running index of product stream

initial Initial plant or process input values

j Running index of input stream