A multi-period and multi-criterion optimization model integrating multiple input configurations, reuse, and disposal options for a wastewater treatment facility

• Main Authors: Ang, Michael Vincent S., Duyag, Jensen, Tee, Kimberly Alyza C., Sy, Charlle L.
• Format: text
• Published: Animo Repository 2019
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1141; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2140/type/native/viewcontent
• Institution: De La Salle University

© 2019 Elsevier Ltd Rapid global industrial development has led to a significant increase in waste generation, including wastewater. Improper disposal of wastewater leads to the degradation of water bodies, endangering marine life and posing health hazards to the nearby communities. This study addresses the current lack of integration in the design of wastewater treatment plants and the challenge presented by the conflicting criteria of economic impact and environmental cost. A multi-period and multi-criterion non-linear programming model for a wastewater treatment plant that simultaneously considers economic and environmental tradeoffs, alternative plant configurations, disposal and reuse options is then developed. The study considers the variability of inputs in the form of water quality and quantity in order to demonstrate the natural variations presented by wastewater sources across a planning horizon. The proposed model is applied to a case study of an actual water utility company in the Philippines. It was seen that the integration of disposal and reuse options had facilitated the realization of improved economic and environmental benefits as it was able to match the effluent water quality to the best suited option. The model enabled the improvement of the treatment process of wastewater inputs by considering alternative methods of entry such as series and parallel configurations instead of just having a mixed input configuration. This significantly improved relevant metrics such as processing time and operational costs.