| Literature DB >> 29510560 |
Xudong Chen1, Zhongwen Xu2, Liming Yao3, Ning Ma4.
Abstract
This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.Entities:
Keywords: multi-objective decision; municipal sewage treatment; processing technology selection
Mesh:
Substances:
Year: 2018 PMID: 29510560 PMCID: PMC5876993 DOI: 10.3390/ijerph15030448
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1Uncertainty of urban sewage treatment problem.
Figure 2Economic-environment trade-off problem.
Cost schedule of all schemes.
| Cost Items | Scheme I | Scheme II | Scheme III |
|---|---|---|---|
| Occupied land (10,000 m2) | 2.4 | 4.6 | 3.5 |
| Land expropriation cost (10,000 USD) | 28.57 | 38.10 | 34.92 |
| Demolition cost (10,000 USD) | 14.29 | 23.81 | 20.63 |
| Initial capital cost (10,000 USD) | 50.79 | 47.62 | 57.14 |
| Sewage pipeline construction cost (10,000 USD) | 15.87 | 31.75 | 23.81 |
| Sewage pump station construction cost (10,000 USD) | 23.81 | 7.94 | 15.87 |
| pipeline maintenance cost (10,000 USD) | 0.63 | 0.95 | 0.63 |
| Pump station operation cost (10,000 USD) | 4.13 | 2.86 | 3.81 |
| Other operation cost (10,000 USD) | 2.54 | 1.59 | 1.90 |
| Double circuit return pipe installation cost (10,000 USD) | 19.05 | 15.87 | 14.29 |
The designed influent and effluent quality and pollutant removal rate of the sewage plant.
| Item | pH | COD | NH3-N |
|---|---|---|---|
| Influent quality (mg/L) | 6–9 | 252 | 35 |
| Effluent quality (mg/L) | 6–9 | 60 | 15 |
| Process rate (%) | — | 76 | 57 |
COD: Chemical Oxygen Demand.
Figure 3Three types of sewage treatment process flow charts.
Comparison between three types of sewage treatment process structures.
| Treatment Process | Oxidation Ditch | ICEAS * | A/A/O |
|---|---|---|---|
| Same structures | Coarse screen wells and pumping station, fine screen and grit chamber, blower room, sludge tank, dewatering room, instruments and center control room | ||
| Different structures | Oxidation ditch biological reaction tank, return sludge pump room | ICEAS reaction tank | A/A/O biological reaction tank, secondary sedimentation tank, return sludge pump room |
* ICEAS: Intermittent Cycle Extended Aeration System.
Comparison between three types of sewage treatment process equipment.
| Treatment Process | Oxidation Ditch | ICEAS | A/A/O |
|---|---|---|---|
| Equipment | Surface aerator, rotating disc aerator, underwater agitator, submersible axial pump | Micro porous aeration device, plug-flow agitator, water decanter, ICEAS submersible sewage pump | Underwater agitator, underwater propeller, aerator, rotating door, submersible sewage pump, mud scraper, electric hoist, excess sludge pump |
Initial investment cost of three types of sewage treatment processes (lv 2009 [37]).
| Treatment Process | Oxidation Ditch | ICEAS | A/A/O |
|---|---|---|---|
| Initial Investment Cost | |||
Operation costs of three types of sewage treatment process (lv 2009 [37]).
| Treatment Process | Oxidation Ditch | ICEAS | A/A/O |
|---|---|---|---|
| Operation cost |
Figure 4Basic Steps of the Genetic Algorithm.
The GA algorithm for the sewage treatment selection.
| Procedure: The GA Algorithm for the Sewage Treatment Selection |
|---|
* The mutation operator maintains the diversity of the population and increases the possibility of not losing any potential solution while finding the global optimal solution. The crossover operator is a technique used for rapid exploration of the search space.
Optimal decision results under different weight conditions (variable unit in the table is the same as that in this paper).
| 0.7 | 0.1 | 0.1 | 0.1 | 0.42 | 0.22 | 0.15 | 0.01 | 3.7 | 35.63 | 2.39 | ||
| 0.4 | 0.4 | 0.1 | 0.1 | 0.46 | 0.29 | 0.14 | 0.01 | 4.2 | 29.58 | 2.31 | ||
| 0.4 | 0.1 | 0.4 | 0.1 | 0.42 | 0.21 | 0.12 | 0.01 | 3.9 | 27.39 | 2.44 | ||
| 0.3 | 0.1 | 0.2 | 0.4 | 0.43 | 0.22 | 0.14 | 0.01 | 3.7 | 33.22 | 2.31 |