Abstract

Wastewater treatment is often required before discharging treated water into rivers and lakes to comply with municipal water disposal regulations. Basic systems such as waste stabilization ponds, aerated lagoons, and activated sludge treatment plants are commonly selected by engineers. However, selecting the appropriate treatment system is challenging despite known parameters and variables such as flow rate, available area, and population density. Cost, including total investment and operational expenses, is often overlooked. Selecting an alternative treatment is challenging due to the lack of standardized rules and specialized software. Hierarchical and fuzzy scale methods can help identify the most significant variables, reducing uncertainty and improving correlation accuracy. A hierarchical scale (-1, 0, 1) was applied to develop a triangular mathematical model for comparison. Coefficients for four fuzzy scales were calculated for each wastewater treatment type. Results indicate that total investment and population density were consistently rated as 1, making them less reliable for decision-making compared to flow rate and area, which had varying fuzzy values (-1, 0, 1), indicating their significance. A linear correlation of R² = 0.65, p = 0.04 was observed between these variables. Additionally, total investment correlated with area, flow rate, and population density, making it a useful metric for budget estimation.

Keywords

Hierarchical analysis; Fuzzy method; Wastewater treatment plant; Stabilization pond; Aerated lagoon; Investment

Abbreviations

ΔA: Available Area Variation (ha); ΔQ: Flow Variation (L/s); ΔDP: Inhabitants Variation or Density Population; TI: Total Investment Variation US $; Qn: Q(maximun or minimum); A: Available Area (ha); Q: flow rate (L/s); DP: Density Population; TI: Total Investment; max v: maxime value for example: 0,9; p: probability occur a events (%); corr. Coef.: coefficient correlation (%); Distr. Hiper: Distribution Hypergeometric; F Distr: Fischer Normal Distribution; Chi Sq: Chi Square Distribution

Introduction

Wastewater treatment plants (WWTPs) are well-known in environmental engineering, and engineers commonly select activated sludge systems such as conventional (CAS), extended aeration (EAS), contact stabilization (CSAS), and oxidation ditches (ODAS). Waste stabilization ponds (WSP) and aerated lagoons (AL) are also options for municipal and domestic wastewater treatment. Engineers face uncertainty when selecting the best treatment system based on water characteristics, flow rate, available area, population density, total investment, and access to electricity, discharge locations, and road infrastructure.

Selecting the best wastewater treatment (WWT) process is a complex decision-making problem, and conventional methods are often inadequate [1]. WWTPs generate large amounts of sludge, making sludge management a key challenge in ensuring sustainable wastewater treatment [2]. Some engineers recommend WSPs when sufficient land is available, as they are economically viable due to lower maintenance and energy costs [3]. In contrast, CSAS and ODAS plants are preferred when land is limited but electrical power is available to handle high flow rates.

Previous studies have analyzed key parameters influencing WWT plant selection but have not identified a definitive method. Decisionmakers must evaluate various treatment options while considering client objectives, site constraints, regulatory requirements, and ethical responsibilities [4].

There is a lack of standardized equation-based methods and software for treatment selection. Hierarchical and fuzzy analysis methods can aid in resolving these uncertainties. Other proposed methods, such as Machine Learning (ML), analyze conventional WSP modifications, using algorithms to recognize patterns and improve prediction accuracy [5]. The Analytic Network Process (ANP) has been used for on-site wastewater treatment selection due to its ability to manage complex multi-criteria decisions. Studies have shown that hierarchical approaches combined with choosing-by-advantages analysis yield results comparable to traditional analytical hierarchical processes [6].

In this study, the Average Integration Method (AIM) was applied using a mathematical model for triangular analysis. An algorithm was developed, and was used to enhance decision-making using hypothetical IF-scenario analysis. Data from 34 wastewater treatment plants of six different types were analyzed [7]. Fuzzy variables including flow rate (Q), area (A), population density (PD), and total investment (TI) were evaluated. The main objective is not to determine the best WWTPs but to identify an optimal selection method using fuzzy performance criteria. The most common contaminants in domestic wastewater [8] include BOD₅, COD, TSS and SO₄ concentrations, yet their influence is not considered in this study.

The PROMETHEE method, developed by [9], was used for decisionmaking involving multiple alternatives. It compares different treatment options based on selected criteria, considering both criteria weights and decision-maker preferences. Additionally, a probabilistic-fuzzy model was developed to optimize WWTP’s selection by integrating uncertainty constraints [10].

Literature reviews show that selecting the most sustainable wastewater treatment (WWT) technology among alternatives is a very complex task because the choice must integrate economic, environmental, and social criteria. Traditionally, several multi-criteria decision-making approaches have been applied, with the most used being the analytical hierarchical process. Nevertheless, this method allows users to offset poor environmental and/or social performance at a lower cost. To overcome this limitation, a study examined a choosingby-advantages approach to rank WWT technologies; yielding results that aligned well with those obtained using the hierarchical approach [11].

Similarly, control structures in wastewater treatment intensification are developed to improve the plant’s performance. To date research has been proposed into two novel hybrid supervised hierarchical control structures for specifying the dissolved oxygen concentration in the last aerobic reactor of the WWTP based on the nitrification rate and the ammonia level in the reactor [12].

Strong enough hierarchical control structure in ocean engineering effective selection of offshore wind turbines requires a comprehensive evaluation of numerous factors, including but not limited to turbine specifications, economic performance, safety, and navigational considerations. Even this outgoing research pointing out traditional selection methods often focuses on specific aspects, leading to limitations in analysis and difficulty in accounting for the complex interrelationships between various factors. Therefore, there is a pressing need to employ advanced decision-making models and data analysis techniques to support the selection process [13].

Methodology

For each hierarchical variable, the significance was determined by ranking imbalances from minimum to maximum values. Coefficient calculations and average results were compared to logical proof using IF performance. A total of 136 fuzzy cases were analyzed for the 34 WWTPs and 4 variables, including various wastewater treatment facilities located in Zulia State as shown Appendices A, Venezuela [14].

• 11 Waste Stabilization Ponds (WSP)

• 3 Conventional Activated Sludge (CAS)

• 5 Extended Aeration Sludge (EAS)

• 8 Contact Stabilization Sludge (CSAS)

• 3 Oxidation Ditches (ODAS)

• 4 Aerated Lagoons (AL)

AIM Model equation

Equation for AIM is developed to continue:

\(\mathop {\lim }\limits_{n \to \infty } \frac{{\Delta Q}}{{{\rm{maximun value}}}} = c{\rm{onstant}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,{\rm{(1)}}\)

Integration equation 1, thus

\(\mathop \smallint \nolimits_{{x_1}}^{{x_2}} \frac{{\Delta Q}}{{\max v}} = {\rm{ }}constant\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,(2)\)

now evaluating the integral for a example value of max v=0.9

\( - 1 \le \frac{{\Delta Q}}{{0.9}}.\frac{{x2 - x1}}{2} > 0\,\,\,\,\,\,;Thus,{\rm{ }}\Delta Q{\rm{ }}compares{\rm{ }}with{\rm{ }}a{\rm{ }}matrix{\rm{ }}consisting{\rm{ }}of{\rm{ }}4{\rm{ }}fuzzy{\rm{ }}values,\,\)

\(\left[ {\begin{array}{*{20}{c}}{ - 1}&0\\0&1\end{array}} \right]\,\,solving{\rm{ }}the{\rm{ }}matrix\; - 1 < \Delta Q,\,\Delta Q = 0,\,\,\Delta Q > 0,\,\Delta Q < \;1\;\)

where ΔQ is Q variation from a n = Q maximum to n = Q minimum value it could be either ΔA, ΔDP and ΔTI;

The value x is used for hierarchical analysis of variables like Q, A, inhabitant, and TI. Coefficients were calculated using equation 2 for each ‘n,’ involving the inverse of the maximum variable value multiplied by the average difference between the maximum and minimum evaluation values [15]. This analysis covered 136 cases.

Algorithms have been considered when logical proof IF for Excel tools is implemented.

Algorithms

Start Calculation for each 4 variable coefficient or weight significance

=((Qn/Qmax*(Qmax-Qmin)/2);

Logical proof IF for each fuzzy analysis

=+IF(Qn*coef<Qn;0)+IF(Qn*coef=Qn;-1;0)+IF(Qn*coef=Qn;0)+IF(Qn*coef>Qn;1);

Fuzzy result -1; End;

The mathematical model incorporates fuzzy solution values within a triangular area, as depicted in figure 1.

Figure 1: AIM math model approach using 4 triangular fuzzy values: (-1, 0, 0, 1).

The minimum and maximum values for each WWTP variable are shown in Appendix A. Six cases were evaluated for the four variables in this study.

Evaluating hierarchical variation values for the model across different cases

Case 1: Waste Stabilization Ponds (WSP) consists of an anaerobic lagoon, a facultative lagoon, and a maturation lagoon [16].

Flow rate variation interval (ΔQ) = 0, 38 - 45, 73 L/s

Area variation interval ( ΔA) = 0, 06 - 3, 98 ha

Population density interval ( ΔDP) = 130 – 50.000 inhabitants

Total investment interval ( ΔTI) = 50.000 - 500.000 US$

Case 2: WWTP Conventional Activated Sludge (CAS) includes gritter separation, aerator tank, settlement tank, and drying bed.

ΔQ = 13,80 - 25,00 L/s

ΔA = 0,27 - 1,80 ha

ΔDP = 4.286 -17.280 inhabitants

ΔTI = 260.000 US $ - 390.000 US $

Case 3: WWTP Extended Aeration Activated Sludge (EAS) includes gritter separation, aerator tank, settlement tank, and drying bed

ΔA = 0,38 - 26,06 L/s

ΔA = 0,01 - 1,10 ha

ΔDP = 130 – 10.500 inhabitants

ΔTI = 10.000 – 425.000 US$

Case 4: WWTP Contact Stabilization Activated Sludge (CSAS) includes grit separation, aerator tank, sludge aerator tank, recirculation, settlement tank, and drying bed.

ΔQ =13,59 - 52,08 L/s

ΔA = 0.27- 1,70 ha

ΔDP = 9.000 – 20.000 inhabitants

ΔTI = 401.000 – 930.233 US $

Case 5: WWTP Oxidation Ditches (ODAS) consist of gritter separation, oxidation ditch with aerator brush, settlement tank, and drying bed.

ΔQ = 2.89 - 60.0 L/s

ΔA = 0.4 - 1.30 ha

ΔDP = 1.000 – 50.000 inhabitants

ΔTI = 400.000 – 930.000 US $

Case 6: Aerated Lagoon (AL) with gritter separation, aerator tank or embankment lagoon, settlement sludge tank, and drying bed.

ΔQ = 9,84 - 40,73 L/s

ΔA = 0,26 – 3,98 ha

ΔDP = 5.000 - 31.000 inhabitants

ΔTI = 180.000 - 598.000 US $

Results and Discussion

The coefficients calculated using Equation 2 is shown in table 1. Additionally, four hierarchical groups were determined to use Excel’s IF logic method for weighting significance in decision-making.

WSP
Q coef	A coef	DP coef	TIcoef	Q	Area	DP	TI
7,44	1,44	9974	237500	1	1	1	1
13,09	0,25	4987	166250	1	0	1	1
20,66	0,98	2493,51	183825	1	0	1	1
22,68	1,95	15459,71	189525	1	0	1	1
5,86	0,49	2992,22	284050	1	0	1	1
6,26	0,0294	2493,52	95000	1	0	1	1
1,61	0,14	822,86	85500	1	0	1	1
0,19	0,0294	64,83	23750	0	0	1	1
4,88	0,13	24935	85500	1	0	1	1
3,16	0,1	916,61	11875	1	1	1	1
8,03	1,22	3490,91	35625	1	1	1	1
CAS
3,086358	0,11475	1611,47	63333,33	1	-1	1	1
5,555	0,459	4331,33	65000	1	-1	1	1
5,555	0,765	6497	43333,33	1	-1	1	1
EAS
1,42	0,0034	98,76	39058,82	1	-1	1	1
3,62	0,01807	1357,98	4882,35	1	-1	1	1
0,66	0,0049	246,91	24411,76	-1	-1	1	1
12,34	0,53	5185	207500	1	-1	1	1
0,18	0,0289	64,2	24411,76	-1	-1	1	1
CSAS
19,1	0,63	5250	114069,5	1	-1	1	1
19,1	0,63	5250	114353,97	1	-1	1	1
13,58	0,34	3500	120896,61	1	-1	1	1
13,58	0,34	3150	264616,5	1	-1	1	1
25,46	0,72	7000	177220,2	1	-1	1	1
8,49	0,21	2450	198462,16	1	-1	1	1
19,1	0,63	5250	136542,05	1	-1	1	1
5,09	0,11	2100	173806,65	1	-1	1	1
ODAS
28,56	0,45	14700	113978,49	1	-1	1	1
1,38	0,14	490	113978,49	1	-1	1	1
8,26	0,35	24500	265000	1	-1	1	1
AL
16,35	0,93	2096,77	135255,85	1	-1	1	1
17,95	1,86	13000	139449,83	1	1	1	1
3,86	0,12	2096,77	62909,70	1	-1	1	1
4,63	0,47	2516,13	209000	1	-1	1	1

Table 1: Coefficients calculated using equation 2 and resulted hierarchical method Q, A, DP and TI variables.

A hierarchical scale (-1, 0, 1) was applied to develop a triangular mathematical model for comparison. Coefficients for four fuzzy scales were calculated for each wastewater treatment type. Results indicate that total investment and population density were consistently rated as 1 as shown in table 1, making them less reliable for decisionmaking compared to flow rate and area, which had varying fuzzy values (-1, 0, 1), indicating their significance. Statistics [17] show on the four variables more significance tends on the area A & Q as shown in table 2.

Area & Q	Area & Q	Area & Q	Area	Area
Corr C (-1,0,1,-1)	Corr C(-1, 1,-1)	Corr Coef(1,1,-1)	Chi Square (-1) true	F Disrib (-1) true
0,0365419	0,0158103	0	0,2419707	0,5
Area	Area	Flow Q	Flow Q	Flow Q
DistrHiper. true	DistHiper false	Distr. Hiper false	Distr. Hiper true	Inhabitant DP&TI
0,0051904	0,0684321	0,75	0,00036879	0,00018085

Table 2: Statistical performance assessment A vs. Q.

The finest mean covariance correlation coefficient is p = 0.04 when considering four fuzzy cases and 34 WWTPs for the A & Q matrix. This reduces slightly to p = 0.02 with a performance fuzzy scale of (-1, 1, -1), indicating a fuzzy negative for Q and a sole fuzzy negative for A. This analysis highlights the uncertainty in the design and construction areas of aeration and sediment tanks. More advanced control strategies, such as model-based control or adaptive control, can also be employed in complex CSTR systems to achieve better control performance [18]. Further research will focus on this topic. The reactor tank for WWTP standard design employs the Completely Stirred Tank Reactor (CSTR) methodology. However, analysis of both ideal and nonideal CSTR models underscores the significance of input flow rate and system characteristics. Models with dead zones demonstrate reduced efficiency [19], indicating a need for further investigation into geometry design and hydrodynamic tank considerations. Additionally, there is an imbalance between aeration (Dissolved Oxygen, DO) [12] and the operation of sediment clarification tanks with recirculation.

The Monod equation is a mathematical model that describes how microorganisms grow. It’s named after French biochemist Jacques Monod (1910-1976). The microbial growth process considered involved two consecutive steps, i.e., (i) the formation of a substratecell “complex”, and (ii) the metabolism of the substrate for cell reproduction [20].

μ = μmax S / (KS + S)                  (3)

μ: The specific growth rate of microorganisms

μmax: The maximum specific growth rate of microorganisms

S: The substrate concentration

X: The biomass concentration

KS: The half-saturation or half-velocity constant

The Monod equation is used in biotechnology, bioengineering, environmental engineering, such as in the activated sludge model for sewage treatment. The Monod equation is empirical, meaning it lacks systematic grounds.

The equivalent result of covariance without correlation (p = 0) for a specific case (fuzzy 1) on an aerated lagoon indicates that, for the fuzzy scale performance (1;1; -1), flow is considered valid when only variable A is fuzzy positive, while all other variables are fuzzy negative, as seen in table 2. Additional statistical tests, such as the Chi-Square test and Fisher’s Distribution, confirm that the value for variable A is fuzzy performance. The false fuzzy value is not significant according to both tests for this dataset.

In this study, WSP showed a significant difference in fuzzy Q true (1) and fuzzy false (0) A, with a covariance p-value of 0.02 not shown. This indicates that only one case of fuzzy false has both Q and A as doble-fuzzy false (0;0). The WSP design is highly empirical, involving hydraulic processes and microorganism growth [21] show that the majority of ponds have higher flow rates than those assumed by designers, and as a result organic loading rates are higher than anticipated and hydraulic detention times lower. Consideration of other variables such as climate condition temperature [22], solar radiation [23], endogenous dead constants [24], equal width/depth ratios and wind direction [25] are necessary. Hierarchic methods [12] and Machine Learning (ML) [5] are also relevant. Future research will explore new findings. Only two WSP lagoons, 1 and 11, designed with superficial loading and statistical methods [26] achieved AIM performance.

Another statistical proof, such as the Hypergeometric Distribution, [18] is common when results like true and false hold relative significance. Fuzzy scales of 1 (true) for Q and A were highly significant with p < 5.1×10^-3. Notably, this is corroborated for DP and TI variables with p < 1.8×10^-4, supporting the demonstration by AIM.

A plot shows the behavior of significant variables for decisionmaking using the AIM hierarchical method, as seen in figure 2. Flow is sufficient for WWTP performance in this study, while available area is not significant for WSP. The results are more reliable for decisionmaking with this variable’s performance but show fuzzy negative (-1) outcomes for CAS, EAS, SCAS, ODAS, and AL.

Figure 2: Values significance making decision to Q vs. available Area for WWTP.

Flow rate (Q) was identified as a key variable in selecting wastewater treatment plants (WWTPs). For waste stabilization ponds (WSPs), numerical integration analysis indicated that flow rates should range from 3.24 L/s to 45.73 L/s. One case, Restaurant La Maroma, had a flow rate of 0.38 L/s, which was considered impractical for WSPs.

Hierarchical clustering with fuzzy negative values was decisive, but physical interpretation is needed to decide if a variable’s value should be increased or decreased for the treatment area. As shown in table 2, analysis of covariance indicated significant differences in fuzzy performance at values of 1 and -1.

Literature lacks clarity on how fuzzy performance differs. One investigation, unrelated to this study’s focus on religion and depression, [27] indicates that the second fuzzy performance best explains the relationship between these variables. Results showed a negative relationship between belief in God and depression, with greater belief associated with lower depression levels, B = -0.164, p < 0.001.

Available area (A) was a key variable. For WSPs, 9 cases scored 0 (not performing) and 2 cases scored 1 (performing). The AIM method confirmed that area is crucial for all treatment systems. However, CAS, EAS, CSAS, ODAS, and AL had fuzzy scale negative results, indicating potential overestimation of required land area. In contrast, the LUZ lagoon (Maracaibo) and San José lagoon (Perijá) showed positive values with A = 2.50 ha and 3.98 ha, respectively, confirming WSPs need between 2.50 and 4 ha.

Aerated Lagoons (AL) were assessed based on AIM integration results for flow rate and area. The analysis indicated potential overestimation in design due to negative area fuzzy values. However, the Pueblo Nuevo lagoon (Baralt) showed positive fuzzy results, with Q = 40.73 L/s and A = 4 ha being essential parameters for AL systems. Require much less land than facultative ponds, depending on the design conditions [28]. This does not correspond well with a similar flow of 41.67 L/s for WSP El Mene, which requires 2 hectares as shown in appendices A.

Inhabitants were found to be irrelevant for WSPs, WWTPs, and ALs, as AIM integration results were consistent across all cases. In whatever way, climate affects the natural landscape, the economic productivity of societies, and the lifestyles of its inhabitants. Probably these one of the reasons in this study inhabitants DP and TI remained stable, unlike climate condition [29]. However, total investment showed a strong correlation with Q, A, and population density variables, making it useful for budget estimation. The Q vs. A correlation yielded R² = 0.33, but eliminating less significant values reduced it to R² = 0.26. When only fuzzy negative values (-1) were considered, R² improved to 0.65, aligning the model more closely with real-world data as shown in figure 3.

Figure 3: Linear correlation significance variable Q vs. A.

CSAS and EAS treatments exhibited better agreement between Q and A, while WSPs and ALs required more area than flow capacity. Conversely, CSAS and ODAS require less area, making them suitable for high-flows cenarios as shown in figure 4. Alternatively, combined WWTPs can be used.

Figure 4: Harmony claims portrayal of WWTPs between Q and A variables.

An example occurs in Canada, Alberta city after extensive testing of the City’s wastewater and the downstream receiving environment, in 2012, the City received confirmation from the provincial and federal regulators regarding the treatment standards that would be applied to the upgraded WWTP. In addition to considering more traditional mechanical wastewater treatment options, in 2015, the City received approval from the regulators to consider “hybrid” systems, which could leverage the investment of the existing aerated lagoons while minimizing the complexity and operator requirements of the upgraded WWTP [30].

Figure 5 displays a plot “box and whisker” from Microsoft Excel showing the fuzzy membership of preference between Area and Flow. This study highlights that variable A significantly influences decisionmaking, with the box plot illustrating the high weight of this variable close to 0 (10% positive and until 30% negative probability) fuzzy value enclose 1. The Q variable is notably distant from the ceremonial value, reaching 40% probability proficiency and a dispersion to 70%, which aligns with the AIM mathematical method.

Figure 5: Fuzzy encourage of the two variables Area and Flow.

The box plot displaying the data set variable-width box plot illustrates the size of each group whose data is being plotted by making the width of the box proportional to the size of the group. The advantage of the box plot technique is that it enables a quick graphical examination of one or more data sets. It is a useful technique for comparing distributions between several groups or sets of data in parallel and also helps to compare the box plot against the probability and observe their characteristics directly [31].

Attempts to compare these results using the PROMETHEE method were unsuccessful due to copyright costs, despite all data being submitted via webinar for a free trial, which went unanswered.

Conclusions

This research shows that flow rate and available area are crucial in treatment selection. CSAS and EAS performed better with these variables, while WSPs and Als needed more area than flow. CASs and ODASs worked well with high flow rates and less area.

This insight should aid WWTP criteria selection. Hierarchical clustering methods effectively identify suitable treatments. With fuzzy variables set at 4 and sufficient data (n>30), the average integration method (AIM) supports quick and clear decision-making for WWTPs criteria.

Population density or inhabitants’ number is not decisive in selection. Perhaps unlike climate conditions in this study. Total investment, however, correlates with key parameters like area, flow rate, and population density, helping in budget estimation.

The study concludes that combined treatment systems offer optimal efficiency. Engineers should use these findings to systematize criteria for WWTPs.

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Appendices A

Waste Stabilization Pond WSP
Flux L/s	Name	Area ha	Inhabitants	TotalInv $
15	Lag LUZ	2,94	20000	500000
26,39	Lag Carrasquero	0,5	10000	350000
41,67	Lag El Mene	2	5000	387000
45,73	Lag Nva Barbara	3,98	31000	399000
11,81	Lag Caja Seca	1	6000	598000
12,62	Lag Sta Maria	0,06	5000	200000
3,24	Lag El Moralito	0,28	1650	180000
0,38	rest La Maroma	0,06	130	50000
9,84	Lag. El Guayabo	0,26	50000	180000
6,37	Lag. Las Piedras	0,21	1838	25000
16,2	Lag. Sn Jose	2,5	7000	75000
WWTP Conventional Activated Sludge CAS
Flux L/s	Name	Area ha	Inhabitants TotalInv $
13,89	Barrio R5 &R10	0,27	4286	380000
25	La Salina Lagunillas	1,08	11520	390000
25	UrbTamareLagunillas	1,8	17280	260000
WWTP Extended Aeration EAS
Flux L/s	Name	Area ha	Inhabitants Total Inv $
3	Granja Alegria	0,01	200	80000
7,64	Isla Dorada	0,04	2750	10000
1,39	BateriaDefensa	0,01	500	50000
26,06	Estac. ULE	1,10	10500	425000
0,38	rest La Maroma	0,06	130	50000
WWTP Contact Stabilization Activated Sludge CSAS
Flux L/s	Name	Area ha	Inhabitants Total Inv $
52,08	Puertos Altagracia	1,5	15000	401000
52,08	Santa Rita	1,5	15000	402000
37,04	Bchaquero MRV	0,8	10000	425000
37,04	Tia Juana LGV MRV	0,80	9000	930233
69,44	Lagunilla Norte	1,7	20000	623000
23,15	Lagunilla Sur	0,5	7000	697674
52,08	Bachaquero	1,5	15000	480000
13,89	Urb NvoPalmarejo	0,27	6000	611000
WWTP Oxidation Dicthes ODAS
Flux L/s	Name	Area ha	Inhabitants Total Inv $
60	Machiques	1,3	30000	400000
2,89	Cuartel J. E. A.             0,40	1000	400000
17,36	Sn Rafael El Mojan	1	50000	930000
Aerated Lagoon AL
Flux L/s	Name	Area ha	Inhabitants TotalInv $
41,67	Laguna El Mene	2	5000	387000
40,73	Lag Pueblo NvoBaralt	3,98	31000	399000
9,84	Lag. El Guayabo	0,26	5000	180000
10,81	Lag base agricola	1,25	8500	698000
Note: (1) Values for TI were estimated for 1993, not cost-status update.
(2) Repeatable values for some WWTP isnot means equal, there were similar treatments and constructed later.

Appendices A: Full date to study located at Zulia’state, Venezuela.

Highlights

• The hierarchical fuzzy method should identify significant variables in wastewater treatment plants (WWTP’s).

• There is a linear correlation between flow and available area variables, which indicates their worth and proficiency.

• Total inversion aligns well with flow and available area and density population variables for budget management.

• WSPs and ALs require more area than flow. Conversely, CSAS and ODAS require less area, for high-flow scenarios.

• Combined WWTPs should assist municipal treatment by reducing costs and improving performance efficiency.

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Article Information

Article Type: RESEARCH ARTICLE

Citation: Aldana-Villasmil G (2025) Hierarchical and Fuzzy Analysis for Wastewater Treatment Criteria Selection. Int J Water Wastewater Treat 10(2): dx.doi.org/10.16966/2381-5299.198

Copyright: ©2025 Aldana-Villasmil G. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publication history: 

Received date: 05 Mar, 2025

Accepted date: 16 Jun, 2025

Published date: 26 Jun, 2025