Intelligent Sequence Planning for Wastewater Treatment Systems

Authors:
Srinivas Krovvidy;William G. Wee;Makram Suidan;R. Scott Summers;John J. Coleman;Lewis Rossman
Affiliations:
-;-;-;-;-;-
Venue:
IEEE Expert: Intelligent Systems and Their Applications
Year:
1994

Citing 1
Cited 3

Wastewater Treatment Systems from Case–Based Reasoning

Machine Learning - Special issue on case-based reasoning

Artificial Intelligence and Environmental Decision Support Systems

Applied Intelligence
An emergent approach for the control of wastewater treatment plants by means of reinforcement learning techniques

Expert Systems with Applications: An International Journal
Application of a Multi-Criteria Decision Support Tool in Assessing the Feasibility of Implementing Treated Wastewater Reuse

International Journal of Decision Support System Technology

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Abstract

The article presents a system for intelligent sequence planning of wastewater treatment systems called Sowat, an approach that uses fuzzy sets to determine the best technologies for different compounds and heuristic search to generate optimal treatment trains. The Sequence Optimizer for Wastewater Treatment (Sowat) solves the wastewater treatment problem in two phases: analysis and synthesis. The system first analyzes the treatability database and develops fuzzy relationships between treatment technologies and waste stream contaminants. It couples these relationships with expert rules (for ordering the technologies in a treatment train), and with pretreatment conditions to be satisfied (for applying the treatment technologies). Then, in the synthesis phase, a heuristic search function uses these relationships to generate treatment trains in order of increasing cost. This phase includes a menu based user interface for performing "what if" analysis.