Dynamic-chance-constrained-based Fuzzy Programming Approach for Optimizing Wastewater Facultative Ponds for Multi-period Case

Abstract

In this article, a novel optimization model that was specifically designed as a dynamic-chance-constrained fuzzy uncertain programming framework is introduced. This model serves the purpose of optimizing the efficiency of facultative ponds utilized in domestic wastewater treatment. The primary focus of this study was maximizing the amount of the wastewater treated in the facility subject to quality requirements via the assessment of wastewater quality through the measurement of Biological Oxygen Demand (BOD). The model's development was grounded in a real-world scenario, where decision-makers encountered uncertainties in various parameters, such as the rate of BOD degradation and the incoming wastewater load, both characterized by fuzzy membership functions. In light of this uncertainty, the decision-maker aimed to maximize the wastewater treatment capacity while maintaining a suitable safety margin for both objective and constraint functions, employing policies founded on probability and chance. A case study was carried out at the Bantul domestic wastewater treatment plant, situated in Yogyakarta, Indonesia. The study successfully identified optimal decisions regarding wastewater flow rates and processing times. As a result, it can be concluded that the proposed model effectively resolved the problem at hand, making it a valuable tool for decision-makers in similar contexts.