Optimal design and operation of activated sludge processes: State-of-the-art

• Published in: Chemical engineering journal (Lausanne, Switzerland : 1996) Vol. 281; pp. 900 - 920
• Main Authors: Hreiz, Rainier, Latifi, M.A., Roche, Nicolas
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2015; Elsevier
• Subjects: Activated sludge; Chemical and Process Engineering; Chemical engineering; Chemical Sciences; Dynamic optimization; Engineering Sciences; Environmental Engineering; Environmental Sciences; Modeling; Multi-objective optimization; Pareto front; Wastewater treatment; Activated sludge; Pareto front; Multi-objective optimization; Wastewater treatment; Modeling; Dynamic optimization
• ISSN: 1385-8947; 1873-3212
• DOI: 10.1016/j.cej.2015.06.125

•The paper deals with the optimal control and design of activated sludge processes.•Studies on the topic are critically reviewed.•Focus is set on the problem formulation that allows one getting reliable solutions.•The selection of appropriate cost functions and constraints is discussed.•Aspects related to dealing with model mismatch are presented. The activated sludge process (ASP) is the most commonly used process for wastewater treatment. Improving its performance is necessary from economic and environmental point of views. In this context, dynamic optimization is a powerful tool for assisting engineers in determining optimal operations and designs for ASPs. However, the real optimality of the solution strongly depends on the optimization problem statement, for which unfortunately, there is no standard or commonly accepted formulation. In a hopeful attempt to provide a guideline for future works on the topic, this paper reviews the literature devoted to optimal control and design of ASPs. The main issues to be addressed in order to get reliable solutions are discussed, among which: (1) Managing the inevitable mismatch between the model predictions and the real ASP operation. (2) Dealing with the unpredictable variations in the wastewater characteristics. (3) Accounting for the slowest dynamic processes occurring in ASPs. (4) Appropriately selecting the decision variables and the flowsheet structure in order to simplify the problem formulation from a mathematical perspective. (5) Conveniently choosing the cost functions expressions/correlations. (6) Successfully selecting the mathematical constraints in order to guarantee physically relevant operations.