Advanced monitoring and control of anaerobic wastewater treatment plants: diagnosis and supervision by a fuzzy-based expert system

Abstract

A fuzzy-based expert system (ES) for the diagnosis and supervision for anaerobic digesters is presented. The system was developed in a Microsoft Windows support using fuzzy logic inference together with a rule base for the implementation of expert knowledge. The ES runs on-line through three main modules, which determine the state and trend of the process, and the best set points for the actuation on the final control elements of the plant. Two further modules run in parallel, when they are required by the operator, using off-line and on-line information for the detection of inhibition due to toxic compounds in the process and for the validation of the on-line diagnosis. The diagnosis and supervision ES was tuned up in order to adjust the membership functions describing the process, and lately tested, running on-line, to study the response of the rule base.

A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater

“…Among these methods, fuzzy-logic methodology has been successfully applied in a variety of ecological and environmental applications, ranging from mapping to modeling, evaluation and prediction tasks [30]. Fuzzy-logic-based models have also been conducted by many researchers as an established and promising method for modeling of various types of anaerobic processes [31][32][33][34][35][36][37][38][39]. However, there are no systematic papers in the literature specifically devoted to a study of an artificial intelligence-based modeling of biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater using the fuzzy-logic technique.…”

Monitoring and control of an anaerobic upflow fixed‐bed reactor for high‐loading‐rate operation and rejection of disturbances

“…It was assumed that the process remained in a stable state and avoided accumulation of volatile fatty acids (VFAs) under this condition. Puñal et al (2001Puñal et al ( , 2002 implemented a rule-based expert system based on fuzzy logic for the diagnosis and supervision of anaerobic digesters. Another notable approach reported in recent years is the probing control strategy based on analyzing the effect of disturbances added on purpose to the influent flow rate (Steyer et al, 1999).…”

Intelligent Models of the Quantitative Behavior of Microbial Systems

“…Their expert system, however, was combined with fuzzy rules to study the dynamic performance of a waste-water treatment system. So did Punal et al (2001). Their work also illustrates the uncertainty referred to above in choosing a unique membership function; Punal et al used their ES to adjust the membership functions while the process continued.…”

“…A fuzzy ES was also developed by Punal et al (2001) for supervision and diagnosis of a waste-water treatment plant. The ES combined a rule base constructed from expert knowledge with fuzzy logic inferences for variables that were difficult to quantify (Paraskevas et al 1999).…”