Synthesis of Activated Sludge Wastewater Treatment Plants for Nitrogen Removal

N. P. ALASINO; M. C. MUSSATI; N. SCENNA

Buenos Aires, Argentina

Congreso; XXII Interamerican Congress of Chemical Engineering (XXII IACChE 2006); 2006

CIIQ Confederación Interamericana de Ingeniería Química / AAIQ Asociación Argentina de Ingenieros Químicos

The most used process for biological nitrogen removal from municipal and industrial wastewaters is the activated sludge process. Due to the importance of these systems, and the large number of existing facilities, a lot of research effort has been focused on optimizing the operation strategies or improving the individual plant designs but not on the systematic optimization of the process structure based on process models. The objective of this work is the simultaneous optimization of the process configuration and the operation conditions of activated sludge wastewater treatment plants for nitrogen removal based on a superstructure model. The model embeds conventional plants i.e. predenitrification, postdenitrification and pre-postdenitrification but allowing flow distribution of the main process streams i.e. nitrate and sludge recycle streams and fresh feed, along the reaction zone. The optimization model aims to minimize the total annual operation cost while predicting compliance with the effluent permitted limits. The cost function computes the cost for pumping and aeration energy demand, for dosage of an external carbon source, for fines for the polluting unit discharged, and for treating the excess sludge for disposal. Defined influent wastewater flow and composition are assumed, and the plant equipment size is given. The Activated Sludge Model No. 3 and the Tákacs model are used to model the biochemical processes and the secondary settler, respectively, resulting in a highly nonlinear system. The resulting MINLP problem with constrains was implemented and solved using General Algebraic Modeling System (GAMS). Results for two case studies are presented and discussed. Keywords: ASM3, Stream Flow Distribution, MINLP Model.