Expert Systems in Water Treatment Plant Operation

Decision‐tree theory may be useful in understanding complex biological systems. Decision‐tree construction and analysis indicate the quantitative occurrence of sulfate‐reducing bacteria in a groundwater basin. The decision‐tree analysis allowed determination of the most important measured variables (discriminators) associated with the occurrence of sulfate‐reducing bacteria. Those variables may be used to improve reliability and validity of the tree. Statistical validation of endpoint data (leaves) is described, and decision trees are presented that increase the understanding of individual wells and hypothesize causes for sulfate‐reducing bacteria.

Section: High-tech Toolsmentioning

confidence: 99%

Section: High-tech Toolsmentioning

confidence: 99%

Section: High-tech Toolsmentioning

confidence: 99%

See 1 more Smart Citation

Decision‐tree construction and analysis

Murphy¹,

Olson²

1996

Expert Systems Show Promise for Customer Inquiries

Males¹,

Coyle²,

Borchers³

et al. 1992

This article describes results of an agreement between the North Penn Water Authority in Lansdale, Pa., and the US Environmental Protection Agency, Drinking Water Research Division, Cincinnati, Ohio, to study use of expert systems technology in a water utility. The three‐year study has resulted in the development of computerized expert systems whose basic principles should be broadly applicable to other water utilities. The system described in this article, called a customer query expert system, is designed to assist administrative personnel in handling customers' inquiries and complaints about water quality. The system has been developed as a prototype and will be placed in field operation for further testing and refinement in the near future. Significant effort, however, is expected to be necessary to move from prototype “proof‐of‐concept” demonstrations to fully featured systems that are of practical use in the field.

A Knowledge‐Based Energy Management System for the Municipal Water Industry

Jamieson¹,

Foster²,

Sastri³

et al. 1993

Optimization of high‐service pump operation with respect to peak kilowatt consumption or total kilowatt hour consumption, depending on conditions, is the goal of KBEMS (knowledge‐based energy management system), the system described in this article. The system has high potential to save municipal water utilities significant amounts of money in electricity expenditures. The knowledge‐based system is designed to foster the incorporation of operators' knowledge and nonhistoric information into planning the operation of a water distribution facility. Flexibility is a prime consideration in the system's design because of the multitude of factors that can influence operating procedure. Constraints imposed by operators and by general pumping strategies of water plant managers act as “filters” placed in front of algorithmic procedures such as pump scheduling and demand forecasting. In this situation, pure optimality is admittedly sacrificed, but necessarily so, in favor of arriving at a good and efficient operating plan.