Simplicity is prerequisite for reliability.
A system is a region where EEIS are intended to serve. On a spatial scale, a system can be classified as residential, community, city, country, or global level. Reliability is defined as the duration of an EEIS to function normally and provide the required service to protect human health and the environments under normal conditions. Reliability usually is measured by the time period during which the system will not fail. Therefore, it can be quantified as the time from starting service to failure of the system. EEIS should be designed in such a way that their reliability is quantified with known uncertainty and sensitivity on different spatial scales. To reduce probability of failure is to design EEIS as simple as possible because propagation of failure increases with increasing components.
Reliability on spatial scales must be quantified in designing EEIS such as WTPs, WWTPs, and GIs. Local geography, resource, economy, and culture could also be weighted to increase reliability of EEIS. For a household, if the land is large enough, on‐site WWT processes such as septic tanks and constructed wetlands could be the most economical way to achieve regenerative design. For communities of population greater than 1000 PE, energy‐neutral design may be more realistic. For large cities, if energy‐neutral design cannot be achieved, designers should target maximum reuse of materials and energy to reduce the FP on water, energy, and nutrients. In general, decentralized WWT is more suitable for rural areas than metropolitan cities. New urban communities and cities have the opportunity to design and build source‐separating systems optimized for cost‐effective resource recovery due to established technologies and design strategies. EEIS should be designed to support sustainable industries, agriculture, and social institutions. Engineers should design EEIS adapted to local geographical conditions, because local resources and geographical settings significantly influence the availability of water, energy, and nutrients. For example, in hilly settings, WTP and WRRF should be sited on a slope from 1 to 3% when gravity flow is used to save energy needed for pumping. For WRRF, reliability is affected by the variability of system performance under normal operation and impact of mechanical failure. For WTP, two components determining the system reliability are the probability of being online and the number of parallel treatment units (AWWA, 2012). To achieve reliability at different spatial scales, following design strategies are recommended:
In designing an EEIS, basic research on molecular reaction, applied research on materials and components, and the development of devices and products should consider the life cycle impact of the EEIS on the environments. Figure 5.1 shows that basic and applied research, product development, and manufacturing are corresponding to molecules, materials, components, and products. Life cycle assessment should be used to quantify and minimize the footprints on the environments at each stage. Since all the stages interact with environments, increasing reliability should not increase environmental impacts.
Sedlak (2014) considered that water and wastewater engineering have went through four stages: (i) transportation of water for drinking and irrigation in the Roman Empire 2000 years ago until 1800, (ii) water treatment and the collection of wastewater and discharge into nature in England until 1900, (iii) centralized wastewater treatment plants from 1900 to today, and (iv) decentralized wastewater treatment that may offer ecological and economic benefits from now on as the fourth stage. Indeed, aging EEIS such as WWTPs causes great problems due to increased population, infiltration, and inflow in sewer system. For example, from January 2007 to May 2013, Miami‐Dade County reported 211 sewer overflows totaling in excess of 51 million gallons, including at least 29 million gallons of raw sewage. In June 2013, the Environmental Protection Agency (EPA) and the state of Florida imposed a fine of $978 000 on the Miami‐Dade County for wastewater violations. The county has to sell a $13.6 billion bond to finance the required repairs of three WWTPs, ending ocean outfalls, upgrading pump stations, and constructing deep wells for the injection of treated wastewater. While the county has made progress toward improving operations since the penalty was issued, many proposed projects are not necessarily sustainable in terms of increasing system reliability. For example, $4 billion is proposed to design and construct two deep wells to dispose treated effluent after high level chlorination at the North and Central WWTPs. Many stakeholders and county commissioners are expressing their concerns for wasting tax payers’ money. Indeed, to increase reliability of the system, innovative technologies have to be assessed to reclaim the effluent for reuse and avoid the costly deep well injection.
In developing countries, decentralized wastewater treatment appears to be even more important. Rudimentary sanitation facilities such as stand‐alone septic tanks, composting or pit toilets, may be one of the best solutions for low‐income areas. However, the potential of contaminating the ground or surface water makes it necessary to employ decentralized WWTP, because conventional on‐site treatment has proved to be of low community acceptance while conventional sewage is too costly. Oxidation ponds, constructed wetlands, sand filters, and biological disks could be effectively applied in small communities as decentralized treatment technologies. A decentralized wastewater management system is defined as a wastewater treatment system that collects wastewater from the source, conveys to a treatment system, and disperses the water near the point of origin. It includes treatment facilities in rural, satellite, cluster, on‐site, and small WWTPs. Small WWTPs refer plants with flow rates ranging from 1 000 to 50 000 GPD. There are also privately owned WWTPs such as mobile home parks, camp sites, recreational facilities, apartments, schools, and privately owned public facilities such as condominiums, shopping centers, and parks. Major advantages of these systems include the protection of public health and the environment, cost effectiveness, recharging of local aquifers, and the provision of water close to wastewater generation. Therefore, they are appropriate for low‐density communities with varying site conditions and are especially suitable for ecologically sensitive areas. Small WWTP systems can be optimized to achieve low cost and high performance with several criteria. They should be easy to be constructed using low‐precision construction techniques, minimize moving parts, operate without electricity, provide feedback on the performance of every step of the treatment process, operate without complicated calculations, and use chemical dosages that can be set directly by a few persons.
US EPA recommends the following procedure as the best practice to increase the reliability of small WWTPs:
Financing assets for rehabilitation, repair, and replacement through borrowing or other financial assistance should be analyzed in asset management plan (long term). The asset management program should assess performance, evaluate progress, adopt new best practices, and take action based on review results to ensure that assets are managed to ensure reliable EEISs.
There are many tools available to facilitate the decision‐making process of different treatment options. Table 5.1 lists analysis tools that the US EPA provided to design an integrated system by selecting.
Table 5.1 Analysis tools.
Tools | Description |
Check Up Program for Small Systems (CUPSS) | This EPA‐developed software provides a simple comprehensive approach to asset management for small water and wastewater utilities in an easy‐to‐use, no‐cost package |
Financing Alternatives Comparison Tool (FACT) | A financial analysis tool developed by EPA to identify the most cost‐effective method to fund a wastewater or drinking water management project |
The Wastewater Information System Tool (TWIST) | This computer‐based tool allows state and local health departments to inventory and manage small wastewater treatment systems by tracking information on residences and facilities served, permits, site evaluations, types of systems, inspections, and complaints in their jurisdictions |
Water and Wastewater Treatment Technologies Appropriate for Reuse (WAWTTAR) | This software identifies appropriate water and wastewater treatment options. It estimates local performance and costs associated with each system based on available material and manpower resources |
Operations and maintenance (O&M), personnel, and the capital budget account for an estimated 85% of a typical water system’s expenses. Asset management enables a system to determine the lowest cost options for providing the highest level of reliable service over time. The work by O&M crew should be optimized on what are they doing, where they are doing it, and why the tasks have to be performed as listed by the US EPA in Table 5.2.
Table 5.2 Five core questions for SWWTPs.
Step | Action |
1 | What is the current state of my system’s assets? |
2 | What is my required “sustainable” level of service? |
3 | Which assets are critical to sustained performance? |
4 | What are my minimum life cycle costs? |
5 | What is my best long‐term funding strategy? |
Table 5.3 Daily water consumption for standard domestic appliances. Table 5.4 Daily water consumption for water‐efficient domestic appliances. Table 5.5 Comparing the costs and water consumption of standard and water‐efficient appliances. Table 5.6 Comparing the capital and running costs of the water treatment and reuse systems. Table 5.7 Comparing the total cost of conventional and IED systems over 20 years.
Waste source
Allowance (l/person/day)
Toilet
50
Bath and shower
50
Handbasin tap
10
Kitchen
10
Tap
7
Dishwasher
3
Laundry
30
Tap
5
Washing machine
25
Total
150
Waste source
Allowance (l/person/day)
Toilet
33
Bath and shower
19
Handbasin tap
1
Kitchen
3
Tap
1
Dishwasher
2
Laundry
11
Tap
1
Washing machine
10
Total
64
Water‐consuming appliances
Capital costs (not installed)
Water consumption
Running costs (water only)
Standard appliances
$2650
273 kl/year
$129/year
Toilet
$300
Shower head
$50
Taps
$800
Dishwasher
$700
Washing machine
$800
Water‐efficient appliances
$3380
116 kl/year
$55/year
Toilet
$300
Shower head
$80
Taps
$1000
Dishwasher
$1000
Washing machine
$1000
Water treatment and reuse system
Capital costs (installed)
Running costs
Conventional solution
$10 836
$640/year
Septic system
$6 500
$240/year
Slow sand filter system
$1 500
$400/year
Subsurface drip irrigation system
$2 836
WDS solution
$10 701
$367/year
Biolytix system
$8 829
$367/year
Subsurface drip irrigation system
$1 872
Solution
Capital costs
O/M costs
20‐year cost
20‐year water consumption
Conventional
$13 486 + $3 630 in 10 years
$769/year
$25 741
5460 kl
Whole system design
$14 081
$422/year
$18 311
2320 kl
Innovative technologies in EEIS design start from laboratory research. Therefore, laboratory data are the major source for scaling up to pilot and full scale. For example, to ensure the reliability of a UV disinfection system to achieve a specific inactivation level of log inactivation (log I), the US EPA requires utilities to validate their UV processes so that the credit of a specific log I can be granted.
Reliability must be proven according to the US EPA protocol of UV disinfection from laboratory data to the full‐scale reactor. In addition, uncertainty associated with the scale‐up must be quantified so that the system can perform as expected to earn the US EPA disinfection credit under different flow rate or water quality such as the transmittance of water. Although UV sensors can measure the intensity of UV light, they cannot measure the dose delivered to the microorganisms as they pass through the reactor at different trajectories. Therefore, to receive treatment credit for inactivating Cryptosporidium, Giardia, or viruses using UV light, the LT2ESWTR requires water systems to use UV reactors that have undergone validation testing. Table 5.8 summarizes the regulatory requirements for all LT2ESWTR requirements related to UV disinfection, including minimum dose, validation, monitoring, and reporting requirements.
Table 5.8 Summary of LT2ESWTR validation requirements.
Requirement | Conditions |
Validated operating conditions must include |
|
Validation testing must include |
|
Validation testing must account for |
|
Collimated beam test typically has the following steps (US EPA, 2006):
US EPA (2006) recommends test procedure using a challenge microbe such as MS2 for health, safety, and repeatability. To validate the full‐scale UV reactor, biodosimetric‐tested RED dose results from the real‐life tests with actual microbes should be used to measure the reactor performance using the following three steps:
Bench‐scale testing using a collimated beam apparatus. Collimated beam testing characterizes the UV dose–response relationship of the challenge microorganism. UV light is directed through a collimating tube to dose a sample of challenge microorganisms of a known concentration. After a specified exposure time, the sample is analyzed to determine the log inactivation (where log inactivation in this situation equals the log concentration prior to UV light exposure minus the log concentration after UV light exposure) as a function of UV dose. The UV dose delivered to the microorganisms is calculated based on the UV intensity, exposure time, and other experimental factors. Collimated beam tests are performed at a range of doses to generate a UV dose–response curve for the specific challenge microorganism. The functional forms of the equations for UV dose–response curves can vary depending on the results. EPA provided guidance on developing UV dose–response curves (US EPA, 2006). A quadratic UV dose–response equation can be used as follows:
The coefficients A and B would be solved for using the collimated beam testing data by Example 5.2 using Matlab. UVT is typically reported at 254 nm because UV manufacturers, WTPs and WWTPs widely use A254.
Full‐scale reactor testing. The challenge microorganisms are injected upstream of the UV reactor. Samples are analyzed to determine the log inactivation at the test conditions of flow rate, UVT, lamp status, and UV intensity as measured by UV sensors. Full‐scale reactor testing can be performed on‐site at a water or a wastewater treatment plant or off‐site at a validation test center.
Male‐specific‐2 bacteriophage (MS2) and Bacillus subtilis spores have historically been used for validation testing to receive treatment credit for Cryptosporidium and Giardia. Of importance is how much the UV sensitivity of each microorganism is related to the target microorganisms. The UV sensitivity of some commonly used and some candidate bioassay microorganisms is shown in Table 5.9.
Table 5.9 UV sensitivity of challenge microorganisms (US EPA, 2006).
Microorganism | Reported delivered UV dose(mJ/cm2) to achieve indicated log inactivation | Reference | |||
1‐log | 2‐log | 3‐log | 4‐log | ||
Bacillus subtilis | 28 | 39 | 50 | 62 | Sommer et al. (1998a) |
MS2 phage | 16 | 34 | 52 | 71 | Wilson et al. (1992 |
Qß phage | 10.9 | 22.5 | 34.6 | 47.6 | Mackey et al.(2006) |
PRD‐1 phage | 9.9 | 17 | 24 | 30 | Mengand Gerba (1996) |
B40‐8 phage | 12 | 18 | 23 | 28 | Sommer et al. (1998b) |
φx174 phage | 2.2 | 5.3 | 7.3 | 11 | Sommer et al. (1998b) |
E. coli | 3.0 | 4.8 | 6.7 | 8.4 | Chang et al. (1985) |
T7 | 3.6 | 7.5 | 11.8 | 16.6 | Mackey et al. (2006) |
T1 | ~5 | ~10 | ~15 | ~20 | Wright (2006) |
Since the UVT of water has to be monitored during operations, it is critical to validate the UV reactor to confirm dose delivery.
The sampling points for microorganisms should be placed far enough from the UV reactor that the germicidal UV intensity at the point of sampling is less than 0.1% of the germicidal intensity within the UV reactor. If the outlet sample port is located downstream of a 90° bend (or the inlet sample port is upstream of a 90° bend), incident light is not a concern. To estimate intensity at a certain distance from the reactor, the following equation can be used: where
US EPA (2006) provided ample examples in validating UV disinfection technology and process. Matlab codes were developed for EEIS designers to comply with the EPA requirements.
The calculated dose approach uses a dose‐monitoring equation to estimate the UV dose based on the parameters measured during reactor operations. The most common operational parameters in dose‐monitoring equation are (i) flow rate, (ii) UV intensity, and (iii) UVT. Since different configurations of reactor have different UV distributions, the RED calculation becomes very complicated. As a result, Matlab codes will greatly facilitate the process.5.3.2.1 Sampling Ports
The RED should be calculated for all full‐scale reactor test conditions, individually for each replicate. For each test condition replicate (i.e. influent and effluent sample pairs), calculate the log inactivation (log I) using the following equation:
where
Determine the RED in mJ/cm2 for each test condition replicate pair using the measured log inactivation (log I) and the UV dose–response curve developed through collimated beam testing. If individual UV dose–response curves cannot be combined, the curve for a given day of testing should be used to determine RED for full‐scale reactor testing data collected that day. If individual dose–response curves developed on the same day of testing cannot be combined, the curve resulting in the most conservative (lowest) RED values should be used. Replicates are evaluated separately to develop the UV dose‐monitoring equation.
Table 5.10 Dose‐monitoring approaches: Key characteristics (US EPA 2006). Table 5.11 Log N0 and log N. Table 5.12 Calculated RED according to the full‐scale tests. The water system’s maximum and minimum flow rates, as well as one or more intermediate flow rates, are recommended by the US EPA to be selected as test conditions. To select intermediate flow rates, geometric progression in the following equation should be used because the relationship between UV dose and flow rate is nonlinear: where
The value of β should be sufficient to obtain at least three measured data points for developing the dose‐monitoring equation. The value of n should be selected to span the range of flow rates.
Table 5.13 Flow rates for the validation test. Table 5.14
RED (mW/cm2) vs. flow rates. Table 5.15 UV dose (in mJ/cm2) required by LT2ESWTR to inactivate target pathogens (US EPA, 2006). Table 5.16 Results of validation testing.
Dose‐monitoring strategy
Parameter used as the operational set point
Parameters monitored during operations to confirm dose delivery
UV intensity set point approach
UV intensity
Flow rate lamp status UV intensity
Calculated dose approach
Calculated or validated dose
Flow rate lamp status UV intensity UVT
Test condition
Replicate
N0 (pfu/ml)
N (pfu/ml)
1
1
5.94
4.57
1
2
6.00
4.54
1
3
5.84
4.56
2
1
6.01
4.10
2
2
5.99
4.09
2
3
6.04
4.06
Test condition
Replicate
N0 (pfu/ml)
N (pfu/ml)
log I
RED (mJ/cm2)
1
1
5.94
4.57
1.37
25.1
1
2
6.00
4.54
1.46
27.0
1
3
5.84
4.56
1.28
23.2
2
1
6.01
4.10
1.91
37.2
2
2
5.99
4.09
1.9
36.9
2
3
6.04
4.06
1.98
38.8
5.3.3.1 Flow Rate for Validation
N
1
2
3
4
Q (MGD)
20
12.5
7.8
4.9
Flow rate (US gallon per minute (GPM))
RED (mW/cm2)
RED5
RED8
RED11
RED15
RED20
3400
4.8
5.7
6.44
7.3
8
3150
5
6
6.8
7.7
8.5
2810
5.5
6.6
7.5
8.5
9.5
2460
6
7.3
8.2
9.5
10.5
2110
6.8
8.2
9.3
10.6
11.8
1760
7.9
9.5
10.8
12
13.4
1210
10.7
12.7
14.2
16
17.7
710
15.9
18.7
21.1
23.5
26
315
28.7
33.5
37.6
41.5
45.5
175
42.2
49
55
60.5
66.5
Log inactivation
0.5
1
1.5
2
2.5
3
3.5
4
Cryptosporidium
1.6
2.5
3.9
5.8
8.5
12
15
22
Giardia
1.5
2.1
3.0
5.2
7.7
11
15
22
Virus
39
58
79
100
121
143
163
186
Challenge microorganism
Influent conc. (pfu/ml)
Effluent conc. (pfu/ml)
UV sensitivity (mJ/cm2 per log I)1
logI2
RED (mJ/cm2)3
MS2
1 × 106
1 × 104
20
2.0
40
φ × 174
1 × 104
0
2
≥4.0
≥8.0
The UV intensity set point approach has intrinsic uncertainty due to systematic or random errors. The uncertainty in the set point value is based on a prediction interval at a 95% confidence level using the following procedure:
SDRED = standard deviation of the RED values measured for each test condition
t = t‐statistic for a 95% confidence level defined as a function of the number of replicate samples by using the following data (Table 5.17).
Table 5.17 t‐Statistic for a 95% confidence level. For reactors using the calculated dose approach, the uncertainty of interpolation (UIN) is calculated as the lower boundary of the 95% prediction interval for the dose‐monitoring equation. This prediction interval reflects the noise in the data about that fit. In nonstatistical terms, the UIN represents the difference between (i) the RED value as derived using measured log inactivation and the UV dose–response curve and (ii) the RED value as calculated using the dose‐monitoring equation. The value of UIN depends on the calculated RED (or calculated dose), increasing at low calculated RED values. EPA recommends that one UIN be selected that represents the most conservative (largest) uncertainty value calculated for the validated dose‐operating range (for the lowest calculated RED). Alternatively, UIN can be expressed as a function of the calculated RED. UIN is calculated using the following equation: where
Table 5.18 t‐Statistic at a 95% confidence level for a sample size equal to the number of test conditions. The last step is to adjust the RED results by the VF to determine the validated dose for the UV reactor using the following equation:
UV intensity set point approach can be used to validate the validated dose through the following example:
Table 5.19 Design specifications. Table 5.20 Characteristics of UV reactor. Table 5.21 Challenge microorganism UV dose–response measured using a collimated beam apparatus. Table 5.22 Flow rate, UVT, lamp power, and UV sensor data measured during validation testing. Table 5.23 Measured influent and effluent challenge microorganism concentrations. Table 5.24 Sensor 1 measurements with lamp 2 operated at 100% ballast power. Table 5.25 Reference UV sensor checks. Table 5.26 Challenge microorganism UV dose–response defined as UV dose versus [log(No/N)] (i.e. log I).
Number of samples
T
3
3.18
4
2.78
5
2.57
5.3.4.1 Calculating UIN for the Calculated Dose Approach
Number of data points used to develop the dose‐monitoring equation
T
Number of data points used to develop the dose‐monitoring equation
t
3
3.18
14
2.14
4
2.78
15
2.13
5
2.57
16
2.12
6
2.45
17
2.11
7
2.36
18
2.10
8
2.31
19–20
2.09
9
2.26
21
2.08
10
2.23
22–23
2.07
11
2.20
24–26
2.06
12
2.18
27–29
2.05
13
2.16
≥30
2.04
5.3.4.2 Determining the Validated Dose and Validated Operating Conditions
Design flow rate
400 gpm
Minimum UVT
90%
Lamp aging factor
80%
Fouling factor
85%
Fouling/aging factor
68% (80% × 85%)
Disinfection goal
2.5‐log Cryptosporidium inactivation credit
UV reactor
UV dose‐monitoring approach
UV intensity set point approach with one alarm set point
90% UVT
97% UVT
UV dose (mJ/cm2)
Replicate 1
Replicate 2
UV dose (mJ/cm2)
Replicate 1
Replicate 2
N (pfu/ml)
Log N
N (pfu/ml)
Log N
N (pfu/ml)
Log N
N (pfu/ml)
Log N
0
882 329
5.95
944 980
5.98
0
1 148 154
6.06
1 300 460
6.11
10
180 120
5.26
198 394
5.30
10
316 328
5.50
257 749
5.41
20
64 217
4.81
69 438
4.84
20
113 644
5.06
74 396
4.87
30
20 622
4.31
20 100
4.30
30
34 679
4.54
25 189
4.40
40
7 257
3.86
8 145
3.91
40
12 624
4.10
9 226
3.97
60
1 274
3.11
1 399
3.15
60
1 980
3.30
1 722
3.24
80
188
2.27
261
2.42
80
387
2.59
211
2.32
100
80
1.90
90
1.95
100
80
1.90
100
2.00
Test ID
Banks on
Flow rate (gpm)
UVT (%)
Relative lamp output (%)
Sduty, 1 (mW/cm2)
Sduty, 2 (mW/cm2)
1
1, 2
394
89.9
100
11.7
11.7
2
1, 2
403
97.0
66
11.6
11.7
Test ID
Influent challenge microorganism log concentration
Effluent challenge microorganism log concentration
Replicate
Replicate
1
2
3
1
2
3
1
5.94
6.00
5.84
4.57
4.54
4.56
2
6.01
5.99
6.04
4.10
4.09
4.06
Lamp ID
Sduty 1 (mW/cm2)
Lamp ID
Sduty 1 (mW/cm2)
1
13.6
5
13.9
2
14.6
6
13.3
3
14.2
7
14.5
4
13.4
8
14.3
Before/after validation testing
UVT(%)
Relative lamp power (%)
Sensor ID
Sduty (mW/cm2)
Sref, 1 (mW/cm2)
Sref, 2 (mW/cm2)
Sref, 3 (mW/cm2)
Before
97
100
1
11.3
11.7
12.1
11.4
Before
97
68
1
5.1
5.5
5.7
5.3
Before
90
100
2
3.7
4.0
4.1
3.8
Before
90
68
2
2.0
1.9
1.9
1.8
After
97
100
1
11.6
11.8
12.2
11.4
After
97
68
1
5.1
5.4
5.6
5.3
After
90
100
2
3.9
4.0
4.1
3.9
After
90
68
2
1.9
1.8
2.0
1.8
90% UVT
97% UVT
UV dose (mJ/cm2)
Replicate 1
UV dose (mJ/cm2)
Replicate 1
Replicate 2
5.92 − log N
Replicate 2
6.05 − log N
0
−0.03
−0.06
0
−0.01
−0.06
10
0.66
0.62
10
0.55
0.64
20
1.11
1.08
20
0.99
1.18
30
1.61
1.62
30
1.51
1.65
40
2.06
2.01
40
1.95
2.08
60
2.81
2.77
60
2.75
2.81
80
3.65
3.5
80
3.46
3.73
100
4.02
3.97
100
4.15
4.05
The uncertainty in the UV dose calculation using the Equation (5.15):
where
In this case, SD = 2.2 at 1‐log inactivation and t = 2.04 for 32 test condition replicate as shown in Table 5.26. Equation (5.15) can then be used to determine UDR at various log inactivation values from Table 5.21. The graph in the succeeding text shows the relationship between log inactivation and UDR. The value of UDR should not exceed 30% at the UV dose corresponding to 1‐log inactivation of the challenge organism. In this case, UDR = 25% at 1.0‐log inactivation, which is less than the recommended limit of 30% (Figure 5.13).
The electrical energy (in kilowatt hours) required to reduce the concentration of a pollutant by one order of magnitude for 1000 US gallons of of water is defined as electrical energy per order (EE/O). For batch reactor, the equation is
where
For a continuous flow reactor, the equation is
where
Power requirements can be estimated from manufacturer‐supplied information regarding the number of lamps in a given system, the kilowatt draw of each lamp, the warranty power setting, and the average number of UV reactors needed. The total kilowatt draw from each manufacturer is then determined, and the average power consumption (kW) is calculated. The average power consumption can be used to calculate the total power costs by multiplying the total power requirements by the assumed power rate of 0.076$/kWh. One of the most important concepts to comparing power consumptions for different UV disinfection systems is electrical energy per order (EE/O). Usually, plug flow reactor will require less EE/O than batch reactors because the distribution of UV intensity in the plug flow reactor (PFR) is more uniform than batch reactor. In addition, the equations may not hold at extreme high inactivation orders such as greater than 6 or 7 because most UV dose will be wasted when Giardia concentration is extremely low.
Table 5.27 Reliability of a WWTP (Eisenberg et al., 2001).
Unit processes
ETBF (days)
Fraction of all components are operating
Headworks
26
0.9953
Primary
41
0.9985
Secondary
9
0.9757
Tertiary
13
0.9994
UV
212
0.9991
Reverse osmosis
10
0.9990
Xiongan is located in the heart of Hebei province and next to Baiyangdian Lake. The Chinese government is relocating many nongovernmental related businesses and some government entities from Beijing to Xiongan such as science and technology, education, manufacturing, and certain corporations. It could require 350 billion USD in fixed asset investment over the next 20 years. Xiongan district is to expand to 10, 100, and 1000 km2 in 5, 10, and 20 years, respectively. According to this plan, please do the following:
The following topics may be considered: