Energy neutrality potential of wastewater treatment plants: A novel evaluation framework integrating energy efficiency and recovery

Abstract

Wastewater treatment plants (WWTPs) consume large amounts of energy and emit greenhouse gases to remove pollutants. This study proposes a framework for evaluating the energy neutrality potential (ENP) of WWTPs from an integrated perspective. Operational data of 970 WWTPs in the Yangtze River Economic Belt (YREB) were extracted from the China Urban Drainage Yearbook 2018. The potential chemical and thermal energies were estimated using combined heat and power (CHP) and water source heat pump, respectively. Two key performance indicators (KPIs) were then established: the energy self-sufficiency (ESS) indicator, which reflects the offset degree of energy recovery, and the comprehensive water-energy efficiency (CWEE) indicator, which characterizes the efficiency of water-energy conversion. For the qualitative results, 98 WWTPs became the benchmark (i.e., CWEE = 1.000), while 112 WWTPs were fully self-sufficient (i.e., ESS ⩾ 100%). Subsequently, four types of ENP were classified by setting the median values of the two KPIs as the critical value. The WWTPs with high ENP had high net thermal energy values and relatively loose discharge limits. The explanatory factor analysis of water quantity and quality verified the existence of scale economies. Sufficient carbon source and biodegradability condition were also significant factors. As the CWEE indicator was mostly sensitive to the input of CHP, future optimization shall focus on the moisture and organic content of sludge. This study proposes a novel framework for evaluating the ENP of WWTPs. The results can provide guidance for optimizing the energy efficiency and recovery of WWTPs.

Full text

RESEARCH ARTICLE
Energy neutrality potential of wastewater treatment plants: A
novel evaluation framework integrating energy efficiency
and recovery
Runyao Huang
1,2
, Jin Xu
1
, Li Xie
1,3
, Hongtao Wang (✉)
1,2,3
, Xiaohang Ni
1
1 Key Laboratory of Yangtze River Water Environment, Ministry of Education, State Key Laboratory of Pollution Control and Resource Reuse,
College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
2 UNEP-Tongji Institute of Environment for Sustainable Development, Tongji University, Shanghai 200092, China
3 Shanghai Institute of Pollution Control and Ecological Security, Tongji University, Shanghai 200092, China
1 Introduction
Wastewater treatment plants (WWTPs) consume high
amounts of energy and emit considerable amounts of
greenhouse gases (GHGs) (Wang et al., 2016). Owing to
the huge treatment scale and sewer length in China (Huang
et al., 2018; Lu et al., 2019), it is crucial to focus on
WWTPs to achieve the nationally determined contribution
to carbon neutrality in 2060. As the GHG emissions of
WWTPs are highly dependent on bioreactors and the input
of grid electricity, carbon neutrality is related to the energy
neutrality of WWTPs (Maktabifard et al., 2018). Thus,
studies on energy neutrality can provide information for
developing measures to achieve the carbon neutrality with
respect to WWTPs.
The main pathways for decarbonization of WWTPs can
be summarized as energy reduction, energy recovery, and
energy renewables (Nakkasunchi et al., 2021). To date,
some researchers have evaluated WWTPs in terms of the
✉Corresponding author
E-mail: hongtao@tongji.edu.cn
Front. Environ. Sci. Eng. 2022, 16(9): 117
https://doi.org/10.1007/s11783-022-1549-0
HIGHLIGHTS
•Framework of indicators was established based
on energy efficiency and recovery.
•Energy neutrality potential of 970 wastewater
treatment plants was evaluated.
•Analysis of characteristics and explanatory
factors was carried out.
•Pathways for improving the energy neutrality
potential were proposed.
ARTICLE INFO
Article history:
Received 21 October 2021
Revised 2 December 2021
Accepted 15 December 2021
Available online 30 January 2022
Keywords:
Wastewater treatment plants
Energy neutrality potential
Energy efficiency
Energy recovery
Evaluation framework
GRAPHIC ABSTRACT
ABSTRACT
Wastewater treatment plants (WWTPs) consume large amounts of energy and emit greenhouse gases
to remove pollutants. This study proposes a framework for evaluating the energy neutrality potential
(ENP) of WWTPs from an integrated perspective. Operational data of 970 WWTPs in the Yangtze
River Economic Belt (YREB) were extracted from the China Urban Drainage Yearbook 2018. The
potential chemical and thermal energies were estimated using combined heat and power (CHP) and
water source heat pump, respectively. Two key performance indicators (KPIs) were then established:
the energy self-sufficiency (ESS) indicator, which reflects the offset degree of energy recovery, and the
comprehensive water–energy efficiency (CWEE) indicator, which characterizes the efficiency of
water–energy conversion. For the qualitative results, 98 WWTPs became the benchmark (i.e., CWEE
= 1.000), while 112 WWTPs were fully self-sufficient (i.e., ESS≥100%). Subsequently, four types of
ENP were classified by setting the median values of the two KPIs as the critical value. The WWTPs
with high ENP had high net thermal energy values and relatively loose discharge limits. The
explanatory factor analysis of water quantity and quality verified the existence of scale economies.
Sufficient carbon source and biodegradability condition were also significant factors. As the CWEE
indicator was mostly sensitive to the input of CHP, future optimization shall focus on the moisture and
organic content of sludge. This study proposes a novel framework for evaluating the ENP of WWTPs.
The results can provide guidance for optimizing the energy efficiency and recovery of WWTPs.
© Higher Education Press 2022

three pathways above. For energy reduction, related
studies have mainly been conducted to evaluate and
improve the energy efficiency of WWTPs. To characterize
the efficiency, normalization is the initial method to relate
the energy input to the output of pollutant removal, such as
the specific energy consumption required to remove a
certain load of a given contaminant (Zou et al., 2019). In
addition, indicators of another dimension, such as the
removal rate of contaminants, can be added to enrich the
normalization outcome (Di Fraia et al., 2018). To consider
multiple types of pollutants, models of data envelopment
analysis have been used to evaluate the energy efficiency
of WWTPs from a multidimensional perspective. As a
result, indicators of energy efficiency became comprehen-
sive and applicable to large sample sets of WWTPs. The
evaluation results of energy efficiency could also be
extended to other functions, for example, identifying the
internal discrepancies within a certain region (Huang et al.,
2021). Regarding energy recovery, recent studies have
mostly concentrated on energy self-sufficiency (ESS). The
use of biogas from the anaerobic digestion of sludge for
digester heating and electricity generation is a feasible and
effective way to improve the ESS (Gu et al., 2017). Yan
et al. established the Net-Zero Energy model to utilize the
chemical energy of excessive sludge to offset operational
consumption (Yan et al., 2017; Yan et al., 2020). In
addition to the chemical energy, the thermal energy of the
WWTPs is promising in China (Hao et al., 2019b). The key
parameters to estimate the potential chemical and thermal
energy are sludge production and wastewater flow,
respectively (Yang et al., 2020). For the energy renew-
ables, the Photovoltaic (PV) has been commonly used to
utilize solar energy in previous studies of WWTPs. The
key parameter to estimate the potential solar energy is the
surface area of the biological reactor in a WWTP (Yang
et al., 2020). The input use of PV would further add to the
ESS of a WWTP, with the actual effect influenced by the
plant scale, presence of anaerobic digestion, and geogra-
phical location (Strazzabosco et al., 2019).
However, existing studies do not combine the pathways
for decarbonization. As energy efficiency and ESS are
indicators that reflect one aspect of energy neutrality, it is
necessary to comprehensively include multiple aspects of
the decarbonization pathways. Accordingly, this study
aims to integrate more pathways when evaluating a
WWTP. To reach this purpose, conversions of the
energy-to-water and water-to-energy in WWTPs are
considered concurrently. In addition to ESS, another key
performance indicator (KPI), defined as the water–energy
efficiency (CWEE) indicator, is set up to characterize the
efficiency of the bidirectional conversion of water–energy
in a WWTP. As a high ESS does not necessarily mean high
feedback, especially for economic investment (Liu et al.,
2021), this study integrates energy efficiency and energy
recovery. With more elements considered, it is necessary to
establish a framework and evaluate the energy neutrality
potential (ENP) of WWTPs. The results could provide a
scientific basis for assessing the energy neutrality of
WWTPs, which is a primary aspect of managing carbon
emission in wastewater sector. This framework may be
useful for other areas and countries.
2 Data and methodology
2.1 Data collection and study area
In this study, the data source was the China Urban
Drainage Yearbook 2018 (China Urban Water Association,
2019). Raw data included total electricity consumption
(kWh/a), pollutant concentrations of influent and effluent
(mg/L), volume of wastewater treated (10
4
m
3
/a), and wet
sludge production (10
3
kg/a). The designed capacity (10
4
m
3
/d) and the main technologies of the samples were
collected through the list of municipal wastewater treat-
ment facilities in China (Ministry of Ecology and
Environment, 2020). Due to limited data availability, the
usage of chemicals and agents was not considered in this
study.
The Yangtze River Economic Belt (YREB) was selected
as the study area. The YREB consists of two municipalities
and nine provinces in China. It is also composed of three
subregions according to the geographical location of the
Yangtze River: upstream, midstream, and downstream.
The population and gross domestic product (GDP) of the
YREB both account for more than 40%of the country’s
total population and GDP, respectively (Pan et al., 2020).
Hence, the YREB is a vital area of China’s economy and
an important support for sustainable development. Nowa-
days, the ecological protection has been added to
development strategies, which is a huge challenge for
regional WWTPs. Thus, this study aims to evaluate the
ENP of WWTPs with the YREB set as the demonstration
to provide national guidance.
To ensure reliability, data screening was performed to
refine the raw data. As a result, data from 970 WWTPs in
the YREB were used in this study. Most of these WWTPs
were designed with a treatment capacity <510
4
m
3
/d
and were mostly equipped with technologies related to
activated sludge, such as anaerobic-anoxic-oxic (AAO),
anaerobic-oxic (AO), and oxidation ditch (OD). A flow
diagram of the data screening process and descriptions of
the selected 970 WWTPs were provided in the Supporting
Information.
2.2 Evaluating the energy neutrality potential of wastewater
treatment plants
2.2.1 Element flows inside wastewater treatment plants
WWTPs are municipal facilities that consume energy to
remove pollutants conventionally. Electricity from the grid
2 Front. Environ. Sci. Eng. 2022, 16(9): 117

is converted to work of pumps and reactors. In this aspect,
the energy flows into the wastewater. Conceptually, a
WWTP should also function with the recycled resources
(Qu et al., 2022). The element flows of wastewater, sludge,
and energy in a WWTP are seen in Fig. 1(a). Through the
multi-direction of the flows, the chemical and thermal
energies can also be recovered to offset the operational
consumption of a WWTP (Yan et al., 2017; Hao et al.,
2019b). Combined heat and power (CHP) and water source
heat pump (WSHP) are the common technologies for
recovering the chemical and thermal energies of WWTPs,
respectively (Hao et al., 2019b).
Based on the flows in Fig. 1(a), a four-step framework
was proposed for evaluating the ENP of a WWTP
(Fig. 1(b)). The details of each step are provided in
following sections.
2.2.2 Definition and calculation on variables
As shown in Fig. 1(b), the step 1 is to define and calculate
the initial variables. All variables are summarized from the
element flows inside the WWTP and reflect the conversion
situation of the water–energy nexus. Energy consumption
and pollutant removal are the primary variables that reveal
the energy efficiency of the wastewater treatment system.
Except for the energy efficiency, the variables of the energy
recovery system are also crucial for determining the ENP.
The energy consumed and recovered by CHP and WSHP
should be included to characterize the ENP. On the one
hand, the net energy recovered offsets the energy
consumption for basic operation; on the other hand, the
energy consumed and recovered could better enrich the
conversion relationship between water and energy. The
variables are listed in Table 1.
In Table 1, C
operation
is the total electricity consumption
(kWh/a), which was obtained from the China Urban
Drainage Yearbook 2018. The energy consumed and
recovered by these technologies should be estimated using
appropriate methods. The relevant equations and para-
meters are detailed in the Supporting Information.
2.2.3 Estimation of two key performance indicators
After defining and calculating all variables, the following
two KPIs were estimated using steps 2 and 3 in Fig. 1(b):
ESS, which characterizes the offset degree of the recovered
energy, and CWEE, which reveals the conversion
efficiency of water–energy.
(1) KPI I: Energy self-sufficiency
The ESS (%, labelled asη
recovery
) of each WWTP, was
obtained using a normalization method. The WWTP
samples with ESS values of ≥100%were defined as
fully self-sufficient, whereas those with ESS values
of <100%were defined as non-self-sufficient.
(2) KPI II: Comprehensive water–energy efficiency
The CWEE indicator, denoted asθ, reveals the
conversion efficiency of water–energy. In this study, we
used slack-based measure model and selected the condi-
tions of the non-oriented type and variable returns to scale.
WWTPs with CWEE values equal to 1 were taken as the
benchmark sites, while those with CWEE values of <1
were taken as the normal WWTP samples.
2.2.4 Classification of energy neutrality potential
The step 4 in Fig. 1(b) classifies the ENP of a WWTP. After
calculating both KPIs, the ENP of each WWTP can be
classified. To distinguish the relative differences, the
Fig. 1 Evaluation framework for energy neutrality potential based on element flows inside a model wastewater treatment plant.
Runyao Huang et al. Evaluating the energy neutrality potential of wastewater treatment plants 3

median of each KPI was selected as the critical value. In
addition, the median divided all selected WWTPs into
equivalent clusters to avoid errors in the statistical analysis
arising from the sample size. The WWTPs with ESS and
CWEE values that were equal to or greater than the median
of the ESS and CWEE values were classified as having
high ENP, while those with ESS and CWEE below the
median ESS and CWEE values were classified as having
low ENP. For WWTPs with medium ENP, there were two
cluster types (medium I and medium II) based on the
characteristics of the two KPIs. The details are listed in
Table 2.
2.3 Statistical analyses
2.3.1 Kappa index
The Kappa index can be used to assess the consistency of
two diagnostic results. Theoretically, the kappa value
ranges from –1 to 1, but more often ranges from 0 to 1.
Empirically, a kappa index of ≥0.7 indicates high
consistency, whereas a value of <0.7 but >0.4 indicates
moderate consistency, and <0.4 indicates low consistency.
In this study, we used Kappa index to assess the
consistency of the qualitative results of the ESS (fully self-
sufficient and non-self-sufficient) and CWEE (benchmark
and normal) indicators.
2.3.2 Chi
2
test
The Chi
2
test is widely used to make statistical inferences
based on the deviation between the actual observed value
and the theoretical value, whereby the deviation deter-
mines the Chi
2
value. In other words, the larger the Chi
2
value, the greater the deviation.
In this study, we used Chi
2
test to determine whether
statistical disparities existed among subregions of the
YREB in terms of the proportion of selected WWTPs with
different ENP.
2.3.3 Kruskal–Wallis Htest
The Kruskal–Wallis Htest is used to verify the statistical
significance of the differences among several clusters. In
addition, as a non-parametric one-way variance analysis
method, it can also diagnose the consistency hypothesis of
the overall function distribution and the normality and
homoscedasticity assumptions. A p-value of >0.05
indicates no significant difference among the tested
samples, whereas a p-value of <0.05 indicates a
significant difference.
In this study, we set the groups according to different
explanatory factors and used Kruskal–Wallis Htest to
diagnose the statistical difference based on the graded
clusters of the ENP of the studied WWTPs.
3 Results and discussion
3.1 Assessment of key performance indicators
3.1.1 Frequency distribution
The initial difference between the ESS and CWEE
indicators appeared in the basic statistics. The ESS values
of the studied 970 WWTPs ranged from 9.94%to
181.36%, while the CWEE values ranged from 0.123 to
1.000. The median and mean ESS values were 67.26%and
69.33%, respectively, while the median and mean values
were 0.544 and 0.576, respectively. Although the mean
and median values of each KPI were similar, the ESS had a
relatively high standard deviation of 27.09%. The other
difference existed in the qualitative results. As shown in
Fig. 2, 121 WWTPs were fully self-sufficient (ESS≥
100%), while 98 WWTPs were considered benchmarks
(CWEE = 1.000). The intervals with upper bounds of
<0.5 included most of the WWTPs.
The WWTP with ESS of 181.36%is situated in the
midstream of YREB. The technology configured in this
WWTP was OD in its initial project phase and, after the
reconstruction, the technology has been modified to AAO.
Nowadays, this WWTP has a designed capacity of 12 
10
4
m
3
/d. According to the China Urban Drainage
Yearbook 2018, this WWTP loaded 5 292 10
4
m
3
of
wastewater and consumed 5 179 120 kWh of electricity in
2017, generating 16 733 10
3
kg of wet sludge. Hence,
Table 1 Initial variables of the framework to evaluate the energy
neutrality potential of a wastewater treatment plant
Variable Label Unit
Total electricity consumption for basic operation C
operation
kWh
Energy consumed by combined heat and power C
CHP
kWh
Energy consumed by water source heat pump C
WSHP
kWh
Energy recovered by combined heat and power E
CHP
kWh
Energy recovered by water source heat pump E
WSHP
kWh
Pollutant removal R
pollutant*
10
3
kg
Note:
*
Pollutant removal includes reductions in the concentration of chemical
oxygen demand (COD), 5-day biochemical oxygen demand (BOD
5
), total
nitrogen (TN), ammonia nitrogen (NH
4+
-N), and total phosphorus (TP).
Table 2 Classification of the energy neutrality potential of wastewater
treatment plants
Cluster of energy neutrality potential Description
High Relatively high ESS and CWEE
Medium I Relatively high ESS but low CWEE
Medium II Relatively high CWEE but low ESS
Low Relatively low ESS and CWEE
4 Front. Environ. Sci. Eng. 2022, 16(9): 117

the specific energy intensity was approximately
0.10 kWh/m
3
, which indicates a high energy efficiency.
Meanwhile, the recovered chemical and thermal energies
were estimated to be 286 130 and 9 106 646 kWh. Thus,
the high ESS resulted from the low electricity cost for
wastewater treatment and the large amount of energy
recovered.
The degree of consistency between the two KPIs was
low, with a Kappa index of 0.203. This indicates that the
emphasis of ESS and CWEE was different. The intersec-
tion of these two KPIs are the energy-related variable; ESS
equals the sum of the net energy recovered divided by the
total operational energy, while CWEE is a unitless
indicator that is composed of several inputs and outputs.
The variables for pollutant removal were also included in
the CWEE indicator. Thus, the calculated ESS directly
reflects the offset degree through chemical and thermal
energy recovery in the studied WWTP samples, whereas
the calculated CWEE includes the components that
characterize the energy efficiency of a certain production
procedure. Considering the different calculation methods
of the KPIs, the classification results of the ENP of the
WWTPs are reasonable to represent the condition from
both aspects of energy efficiency and energy recovery.
3.1.2 Comparative analysis on variables
To gain further insight into the determined ESS and
CWEE, the statistics of the input and output variables were
analyzed. The mean values of the variable were compared
using 50%of the sample set as the baseline. The variables
were analyzed in terms of both water and energy in
WWTPs. The following figure shows the percentage
proportion of fully/non-self-sufficient and benchmark/
normal WWTPs. The variables for the studied WWTPs
differed with respect to the calculated ESS and CWEE.
For ESS, the WWTPs that recovered more energy did
not necessarily have more input. The fully self-sufficient
WWTPs tended to consume less for operational functions
(C
operation
) and cogeneration (C
CHP
). The consumption
(C
WSHP
) and production (E
WSHP
) of the WSHP were the
only two variables that exceeded the baseline for the fully
self-sufficient WWTPs. Meanwhile, the amount of pollu-
tant removal of fully self-sufficient WWTPs was also less
than that of the non-self-sufficient WWTPs (see Fig. 3). As
C
operation
is the direct outcome of pollutant removal, the
fully self-sufficient WWTPs may have had lower inputs in
operational functions of the biochemical process. Mean-
while, the high amount of thermal energy recovery offset
the total electricity consumption. Thus, the 121 WWTPs
that achieved fully self-sufficiency may have done so
owing to both water quantity and quality. Less pollutant
removal leads to less electricity and the sufficient recovery
of thermal energy offsets the electricity consumption.
For the CWEE indicator, both the input and output
variables of the benchmark WWTPs were considerably
higher than the normal ones. All the related variables
surpassed the baseline of 50%(see Fig. 3). Thus, for
CWEE, the benchmark WWTPs consumed more and
generated more at the same time. For normal WWTPs, an
output shortfall was observed. Based on this, we inferred
that CWEE was also influenced by scale economies, which
are commonly associated with wastewater treatment
facilities (Hernández-Chover et al., 2018). Thus, for the
98 benchmark WWTPs, the condition of a high CWEE
score could be attributed to their relatively large treatment
capacities.
3.2 Evaluation on energy neutrality potential
3.2.1 Energy neutrality potential classification
To classify the ENP of the studied WWTPs, the median
values of the two KPIs were set as critical values (67.26%
Fig. 2 Frequency of WWTP distribution in intervals of energy self-sufficiency and comprehensive water–energy efficiency.
Runyao Huang et al. Evaluating the energy neutrality potential of wastewater treatment plants 5

for ESS and 0.544 for CWEE). Although the dimensions of
the ESS and CWEE values differ, they shared the
intersection of energy recovery and operational energy
for pollutant removal. As proved in the previous sections,
the classification of ENP based on these KPIs was
reasonable. The WWTPs with ESS and CWEE values
equal to or exceeding their respective median values were
defined as having high ENP. A scatterplot of the studied
WWTP samples is shown in Fig. 4.
The overall distribution trend was very discrete, with
many WWTPs scattered in the 2
nd
and 4
th
quadrants.
Furthermore, many benchmark WWTPs distributed in the
bottom-right corner. This result corresponds to the out-
come of the Kappa index (see Fig. 2). The WWTPs that
plotted near the upper righthand corner had higher ENP.
The CWEE values of WWTPs in the upper left part were
not high (2
nd
quadrant, medium potential I), but the ESS
values exceeded 67.26%(i.e., the median). Although these
WWTPs had a relatively poor water–energy conversion
efficiency, the offset degree of energy recovered to the
basic operation was high. In contrast, the WWTPs in the 4
th
quadrant exhibited high CWEE but had a certain
deficiency on ESS. Therefore, distinct disparities existed
among the WWTPs in each quadrant, representing
different ENP. The determined characteristics in terms of
region and water–energy features are introduced in the
following section.
A list evaluated by several institutions with authority
was used for the validation. The list contains the WWTPs
given honorary titles for excellent achievements with
respect to low carbon and eco-friendliness (Zhao 2021).
Among the titled WWTPs, there are two involved in this
study, and both were found to have a high ENP. The first is
Yixing Urban WWTP in Wuxi, Jiangsu Province (AAO
process with a designed capacity of 7.5 10
4
m
3
/d in
2017) (Ministry of Ecology and Environment, 2020). The
WWTP was given an honorary title for plant–network
integration. The second is the Taziba WWTP in Mianyang,
Sichuan province (AO process with a designed capacity of
20 10
4
m
3
/d in 2017) (Ministry of Ecology and
Environment, 2020). In the list, the Taziba WWTP was
classified into the group with an excellent operation of
power saving and environmental education. This corre-
sponds well with the high ENP determined in this study.
The external validation of these two WWTPs supports the
results of the ENP evaluation in this study.
3.2.2 Characteristic analysis of different clusters of energy
neutrality potential
The characteristic analysis was carried out based on the
aspects of region and water–energy conditions. Table 3
Fig. 3 Comparative analysis of variables for fully/non-self-sufficient and benchmark/normal wastewater treatment plants.
Fig. 4 Classification of the energy neutrality potential of 970
wastewater treatment plants.
6 Front. Environ. Sci. Eng. 2022, 16(9): 117

represents the number and proportion rate of WWTP
samples with different types of ENP in each subregion.
The Chi
2
test was used to determine whether there were
significant differences among the clusters in terms of
proportion. The null hypothesis of the Chi
2
test indicates
that the proportion of WWTPs with different types of ENP
was the same in the various subregions of the YREB. The
statistical results for the sample number and proportion rate
are shown in Table 3.
According to the results displayed in Table 3, the p-value
of the Chi
2
test was <0.001, indicating the proportion of
WWTPs within each ENP cluster differed significantly
between the subregions of the YREB. As demonstrated by
current research on YREB, the conditions of population
density, economic development level, and water resource
endowment may influence the efficiencies of wastewater
discharge and treatment (Wang et al., 2020). Meanwhile,
the efficiency characterized by the urban sewer length and
designed capacity also differed between the subregions of
YREB (Pan et al., 2020). Factors relating to population
density and the level of economic development can also
result in different net-zero energy conditions in WWTPs in
China (Xiong et al. 2021). As the economic development
and population situation are distinct among the subregions
of the YREB, the reasons for the observed differences may
include the socio-economy factors that influence the ENP
of WWTPs.
As shown in Fig. 5(a), there was a monotonous trend in
the net thermal energy via the WSHP. The WWTPs with
high ENP exhibited the highest thermal energy recovery,
while the cluster with low ENP had the lowest thermal
energy recovery. The proportion of chemical energy
recovered was much smaller for the WWTPs with high
ENP as shown in Fig. 5(a). This result corresponds to the
phenomenon of heat over organics (Hao et al., 2019b),
which means that there is a much higher potential to
recover thermal energy than chemical energy in China.
Regarding the discharge conditions, except for COD, all
contaminants were discharged under the least strict
conditions in the WWTPs with high ENP, whereby the
mean effluent concentration of BOD
5
, TN, NH
4
+
-N, and
TP were 6.85, 9.77, 2.03, and 0.77 mg/L, respectively.
Hence, the less strict discharge limits may have contributed
to the high ENP of these WWTPs. Enhancing pollutant
removal is still the main target of WWTPs, and strict
effluent standards are increasingly implemented in
WWTPs nationwide (Qu et al., 2019). However, stricter
effluent limits tend to have negative effects on WWTPs.
For instance, upgrading a WWTP to stricter effluent limits
can increase the energy use and carbon footprint (Smith
et al., 2019), whereas less strict effluent standards may
improve resource recovery performance (Zhang et al.,
2020b). Thus, reasonable discharge limits are recom-
mended for WWTPs.
3.3 Analysis of explanatory factors and optimization path-
ways
3.3.1 Quantity and quality of influent wastewater
The Kruskal–Wallis Htest was used to analyze the impacts
of different factors on the quantity and quality of influent
Table 3 Number and proportion of wastewater treatment plants with different energy neutrality potential in the subregions
Cluster of energy neutrality potential Sample number (Proportion rate) Chi
2
test
Upstream Midstream Downstream χ2p-value
High 78 (33.1%) 87 (31.8%) 111 (24.1%)
101.601 <0.001
Medium I 36 (15.3%) 105 (38.3%) 68 (14.8%)
Medium II 67 (28.4%) 22 (8.0%) 121 (26.3%)
Low 55 (23.3%) 60 (21.9%) 160 (34.8%)
Fig. 5 Characteristics of energy neutrality potential: (a) net energy recovery and (b) discharge condition.
Runyao Huang et al. Evaluating the energy neutrality potential of wastewater treatment plants 7

wastewater. The mean rank reflects the value of a factor,
whereby the value of a factor increases with an increase in
the mean rank. The treatment capacity (10
4
m
3
/d) and
sludge production (kg/a) are the key factors determining
the potential energy recovery. The factors that characterize
the influent water quality are the COD concentration (mg/
L) and the ratios of BOD
5
/COD, COD/TN, and BOD
5
/TP.
In this section, the ENP types of medium I and II were
integrated as one cluster. The results are listed in Table 4.
Table 4 shows that the treatment capacity, sludge
production, influent COD concentration, and influent
BOD
5
/COD ratio significantly affected the ENP (p-
value <0.001), whereas the influent COD/TN and Influent
BOD
5
/TP ratios did not significantly affect the ENP (p-
value >0.05). Regarding the mean rank, the WWTPs with
high ENP generally had relatively high treatment capa-
cities, amounts of sludge production, influent COD
concentrations, and influent BOD
5
/COD ratios. These
results imply that the probability of a WWTP having a high
ENP increases with increases in the values of the factors
above.
The treatment capacity and amount of sludge production
reflect the scale of a WWTP. The treatment capacity
determines the amount of wastewater flow used to estimate
the thermal energy. As the WWTPs with higher thermal
energy values tended to have better performance both in
terms of ESS and CWEE (Fig. 3), the treatment capacity
was taken as the initial factor influencing most the ENP.
This result indicates the effect of scale economies on ENP
and corresponds to the findings in Fig. 3. For sludge
production, this factor determines the chemical energy of a
WWTP. As it is the outcome of the biochemical process,
the value of this factor may depend considerably on the
removal of contaminants with an oxygen-demand. Thus,
there exists some internal correlation between sludge
production and the factors of water quality that affect
biochemical treatment. For water quality factors, the
influent COD is the carbon source of wastewater. As
reported in a related study, the COD concentration has a
significant effect on potential of energy neutrality and
energy positivity at the plant-level (Sarpong et al., 2020).
The influent BOD
5
/COD ratio is a factor of biodegrad-
ability, and a higher value is helpful for increasing the
efficiency of pollutant removal (Zhang et al., 2020a). In
addition, the influent COD/TN and BOD
5
/TP ratios reflect
the feasibility of using biotechnology to remove nitrogen
and phosphorus (Zou et al., 2019). A low COD/TN ratio
may indicate the need for an additional carbon source to
attain a high TN removal efficiency (Quan et al., 2018).
However, in the present study, the features of these two
parameters did not significantly impact the ENP. The
effects of the influent COD/TN and BOD
5
/TP ratios may
only be evident when the conditions of scale, carbon
source, and biodegradability are similar for WWTPs.
3.3.2 Optimization pathway analysis
The pathway towards optimizing the ENP of WWTPs shall
consider ESS and CWEE. According to the relevant
equations, ESS greatly depends on the operational energy
consumption and total energy recovery. The CWEE
indicator arises from the linear programming of the input
and output variables. The input variables involve three
types of energy consumption, while the output variables
consist of energy recovery and pollutant removal. In terms
of performance improvement, it is evident that a lower
operational energy consumption and higher energy
recovery would optimize ESS. However, the relationship
between the variable and CWEE was not that obvious.
Thus, a sensitivity analysis was conducted by excluding
certain variable from the CWEE calculation. The fitting
line was used to compare the baseline values (y= x). The
smaller the slope of the fitted line, the greater the
sensitivity of the CWEE value to the variable excluded.
The results are presented in Fig. 6.
Regardless of the excluded variable, a descending
tendency was evident, which was the same as that reported
in a previous study that applied this method (Liu et al.,
2021). According to Figs. 6(a) and 6(b), the fitted lines
deviated significantly from the original baseline. The most
sensitive variables were the amounts of energy consumed
and recovered by CHP, deviating by 0.7532 (R
2
= 0.8945)
and 0.9050 (R
2
= 0.9914), respectively. Regarding WSHP,
the fitted lines were very closed to the baseline with slopes
of 0.9919 and 0.9633 for the amounts of energy consumed
and recovered, respectively (Figs. 6(c) and 6(d)). Thus,
Table 4 Analysis of explanatory factors with different types of energy neutrality potential
Explanatory factor Mean rank in each cluster Kruskal–Wallis Htest
Low Medium High χ2p-value
Treatment capacity (10
4
m
3
/d) 351.01 510.76 581.15 98.956 <0.001
Sludge production (kg/a) 393.53 516.31 530.37 41.780 <0.001
Influent COD concentration (mg/L) 420.12 491.18 542.01 26.382 <0.001
Influent BOD
5
/COD 430.65 491.69 530.75 17.949 <0.001
Influent COD/TN 486.05 504.51 456.09 4.973 0.083
Influent BOD
5
/TP 488.36 479.35 491.98 0.378 0.828
8 Front. Environ. Sci. Eng. 2022, 16(9): 117

CWEE was highly sensitive to CHP, while it was less
sensitive to the WSHP, indicating that CWEE should be
optimized based on the polish of CHP, especially for
reducing the input. This is particularly true for WWTPs
with ENP categorized as medium I (i.e., a high ESS but
low CWEE).
In this study, the water content (%) was a crucial
parameter reflecting the properties of excessive sludge.
The water content can refer to that after mechanical
dewatering or after drying. The water content after
mechanical dewatering determines the energy consump-
tion before combustion. The traditional sludge belt filter
press can reduce the sludge water content by approxi-
mately 80%(Yang et al., 2020). Under these circum-
stances, further steps of the dryer should reduce the water
content for self-sustaining combustion (Hao et al., 2019a).
The water content after drying determines the energy
requirements for combustion. In addition, the organic
content is also important. Empirically, the amount of
energy generated through incineration increases with an
increase in the organic content of the excessive sludge (Wu
et al., 2021). For anaerobic digestion, improvements in the
efficiency of anaerobic digestion and substrate allocation
between catabolism and anabolism were also recom-
mended (Yan et al., 2020). From the perspective of
wastewater treatment, it is promising to combine COD
capture and anaerobic ammonium oxidation to convert
carbon compounds into high-value organic materials.
Moreover, upgrading effluent standards usually results in
an increased energy input and a higher carbon footprint
(Smith et al., 2019). Hence, moderate discharge limits
should be implemented to avoid the latent factors that
burden energy neutrality.
Under the framework for evaluating ENP in this study,
optimization pathways should concentrate both on the
energy recovery system and the wastewater treatment
system. Detailed strategies for optimizing energy neutrality
should be investigated and formulated for specific
WWTPs, such as reducing the water content of excessive
sludge, making use of organic matters, and determining the
most suitable discharge limits.
4 Conclusions
This study established a novel framework for evaluating
the ENP of 970 WWTPs in the YREB. Two KPIs, ESS and
CWEE, were evaluated, and the median values were set as
the critical values. The results showed that these KPIs
characterized energy recovery differently. For ESS, fully
Fig. 6 Sensitivity analysis on variables of energy consumed and recovered.
Runyao Huang et al. Evaluating the energy neutrality potential of wastewater treatment plants 9

self-sufficient WWTPs consumed less energy and removed
less pollutants, whereas scale economies affected CWEE.
The studied WWTPs with high ENP had higher thermal
energy recoveries and relatively loose discharge limit. In
addition, an analysis of explanatory factors demonstrated
that the treatment capacity, amount of sludge production,
influent COD concentration, and influent BOD
5
/COD
ratios significantly affected the ENP. Optimization meth-
ods should focus on reducing the input of CHP. To achieve
this, it is crucial to emphasize organic storage and the
reduction on the water content of the sludge. The selected
discharge limits for wastewater treatment should consider
the ENP. The evaluation framework proposed in this study
could also be applied to WWTPs in other regions. The
results provide guidance for managing WWTPs to
optimize energy neutrality both in China and abroad.
Acknowledgements This work was supported by the Foundation of Key
Laboratory of Yangtze River Water Environment, Ministry of Education
(Tongji University), China (No. YRWEF 202007). The research was partially
supported by the Science and Technology Commission of Shanghai
Municipality Foundation (Nos. 17DZ1202100 and 21230712200). We are
also grateful to editors and the anonymous reviewers for their efforts and
insightful suggestions.
Electronic Supplementary Material Supplementary material is available
in the online version of this article at https://doi.org/10.1007/s11783-022-
1549-0 and is accessible for authorized users.
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