Probabilistic-fuzzy programming model with chance-constrained to optimize wastewater treatment plants: A case study with the Bantul wastewater treatment plant layout
This study introduces a novel optimization approach, employing chance-constrained probabilistic-fuzzy uncertain programming, to enhance the efficiency of facultative ponds in wastewater management systems. Unlike traditional deterministic or stochastic models, this approach integrates both probabili...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
|
| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/05/e3sconf_icenis2024_03036.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832098473461153792 |
|---|---|
| author | Armanda Adiqya May Dwi Sutrisno Sunarsih Widowati Kartono |
| author_facet | Armanda Adiqya May Dwi Sutrisno Sunarsih Widowati Kartono |
| author_sort | Armanda Adiqya May Dwi |
| collection | DOAJ |
| description | This study introduces a novel optimization approach, employing chance-constrained probabilistic-fuzzy uncertain programming, to enhance the efficiency of facultative ponds in wastewater management systems. Unlike traditional deterministic or stochastic models, this approach integrates both probabilistic and fuzzy uncertainties capturing real-world variations in Biological Oxygen Demand (BOD) degradation rates and wastewater loads. The model addresses the decision-making scenario where some uncertain parameters, like the rate of BOD reduction, are probabilistic with some probability density functions, and some other uncertain parameters, like wastewater load, are represented as fuzzy variables with membership functions determined by the decision-maker. Amid this uncertainty, the goal is to maximize the volume of wastewater treated while maintaining adequate safety margins via chance-based rules is implemented to the objective and the constraints. Using the layout of the Bantul residential wastewater treatment plant located in Yogyakarta, Indonesia, this research succeeded in determining optimal decisions regarding processing time and wastewater flow rate. Consequently, it is inferred that the developed model effectively resolves the problem at hand, rendering it applicable for decision-makers in similar contexts. |
| format | Article |
| id | doaj-art-02e709411ace4e6d93f357489de16c04 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-02e709411ace4e6d93f357489de16c042025-02-05T10:49:10ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016050303610.1051/e3sconf/202560503036e3sconf_icenis2024_03036Probabilistic-fuzzy programming model with chance-constrained to optimize wastewater treatment plants: A case study with the Bantul wastewater treatment plant layoutArmanda Adiqya May Dwi0Sutrisno1Sunarsih2Widowati3Kartono4Department of Mathematics, Diponegoro UniversityDepartment of Mathematics, Diponegoro UniversityDepartment of Mathematics, Diponegoro UniversityDepartment of Mathematics, Diponegoro UniversityDepartment of Mathematics, Diponegoro UniversityThis study introduces a novel optimization approach, employing chance-constrained probabilistic-fuzzy uncertain programming, to enhance the efficiency of facultative ponds in wastewater management systems. Unlike traditional deterministic or stochastic models, this approach integrates both probabilistic and fuzzy uncertainties capturing real-world variations in Biological Oxygen Demand (BOD) degradation rates and wastewater loads. The model addresses the decision-making scenario where some uncertain parameters, like the rate of BOD reduction, are probabilistic with some probability density functions, and some other uncertain parameters, like wastewater load, are represented as fuzzy variables with membership functions determined by the decision-maker. Amid this uncertainty, the goal is to maximize the volume of wastewater treated while maintaining adequate safety margins via chance-based rules is implemented to the objective and the constraints. Using the layout of the Bantul residential wastewater treatment plant located in Yogyakarta, Indonesia, this research succeeded in determining optimal decisions regarding processing time and wastewater flow rate. Consequently, it is inferred that the developed model effectively resolves the problem at hand, rendering it applicable for decision-makers in similar contexts.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/05/e3sconf_icenis2024_03036.pdf |
| spellingShingle | Armanda Adiqya May Dwi Sutrisno Sunarsih Widowati Kartono Probabilistic-fuzzy programming model with chance-constrained to optimize wastewater treatment plants: A case study with the Bantul wastewater treatment plant layout E3S Web of Conferences |
| title | Probabilistic-fuzzy programming model with chance-constrained to optimize wastewater treatment plants: A case study with the Bantul wastewater treatment plant layout |
| title_full | Probabilistic-fuzzy programming model with chance-constrained to optimize wastewater treatment plants: A case study with the Bantul wastewater treatment plant layout |
| title_fullStr | Probabilistic-fuzzy programming model with chance-constrained to optimize wastewater treatment plants: A case study with the Bantul wastewater treatment plant layout |
| title_full_unstemmed | Probabilistic-fuzzy programming model with chance-constrained to optimize wastewater treatment plants: A case study with the Bantul wastewater treatment plant layout |
| title_short | Probabilistic-fuzzy programming model with chance-constrained to optimize wastewater treatment plants: A case study with the Bantul wastewater treatment plant layout |
| title_sort | probabilistic fuzzy programming model with chance constrained to optimize wastewater treatment plants a case study with the bantul wastewater treatment plant layout |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/05/e3sconf_icenis2024_03036.pdf |
| work_keys_str_mv | AT armandaadiqyamaydwi probabilisticfuzzyprogrammingmodelwithchanceconstrainedtooptimizewastewatertreatmentplantsacasestudywiththebantulwastewatertreatmentplantlayout AT sutrisno probabilisticfuzzyprogrammingmodelwithchanceconstrainedtooptimizewastewatertreatmentplantsacasestudywiththebantulwastewatertreatmentplantlayout AT sunarsih probabilisticfuzzyprogrammingmodelwithchanceconstrainedtooptimizewastewatertreatmentplantsacasestudywiththebantulwastewatertreatmentplantlayout AT widowati probabilisticfuzzyprogrammingmodelwithchanceconstrainedtooptimizewastewatertreatmentplantsacasestudywiththebantulwastewatertreatmentplantlayout AT kartono probabilisticfuzzyprogrammingmodelwithchanceconstrainedtooptimizewastewatertreatmentplantsacasestudywiththebantulwastewatertreatmentplantlayout |