Knowledge-based expert system applications in waste treatment operation and control

Barnett, Michael W.

doi:10.1016/0019-0578(92)90009-8

Cited by 8 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many reasons favor this choice (Barnett 1992;Gall and Patry 1989): (1) complexity of the process, which has several unit operations, (2) many parameters, some of which cannot be controlled, (3) strongly dynamic process characteristics, (4) the presence of many components and a consortium of many microorganisms, not all of which are known exactly, and (5) uncertainty or fuzziness of some properties such as color and odor. Recognizing these difficulties, Paraskevas et al (1999) designed an integrated and distributed ES that comprised five modules, analogous to that shown in Fig.…”

Section: Expert Systems For Microbial Reactorsmentioning

confidence: 97%

Intelligent Models of the Quantitative Behavior of Microbial Systems

Patnaik

2008

Food Bioprocess Technol

View full text Add to dashboard Cite

Under different environmental conditions, microbial systems display complex behavioral patterns that are difficult to express quantitatively by mechanistic methods. Therefore, two alternate approaches based on different forms of intelligence have emerged. One approach uses methods of artificial intelligence (AI) such as neural networks, expert systems, and genetic algorithms to describe cellular behavior. The second methodology, which leads to the class of cybernetic models, relies on intelligence postulated to be possessed by the cells themselves. While both AI and cybernetic methods have been effective in many applications where mechanistic models are inadequate, all three methods have strengths and weaknesses. This recognition has led to the development of hybrid systems that combine two or more approaches for different aspects of a microbial system. However, the optimum design of hybrid systems still remains heuristic. The rationale and the developments from mechanistic to hybrid models are discussed here, and it is suggested that eventually a truly intelligent system should be self-evolving to maintain itself at the optimum configuration at all times.

show abstract

Section: Expert Systems For Microbial Reactorsmentioning

confidence: 97%

Intelligent Models of the Quantitative Behavior of Microbial Systems

Patnaik

2008

Food Bioprocess Technol

View full text Add to dashboard Cite

show abstract

“…Barnett has developed a rule-based expert system for the identification of abnormal conditions in anaerobic sludge digestion processes. With the help of the process data analysis, sludge concentration, suspended solid concentration, dissolved oxygen concentration, and pH value, which characterize the state in the secondary sedimentation tank, were selected as auxiliary variables, and abnormal conditions were identified as the output [6]. In order to maintain the stability of the abnormal condition identification, Traore et al used fuzzy rules to identify the sludge height of the secondary sedimentation tank with variables such as sludge concentration, sludge volume, and suspended matter concentration and used the identification results to evaluate the settling performance of the sludgewater mixture in the secondary sedimentation tank to determine whether the abnormal condition occurred [7].…”

Section: Introductionmentioning

confidence: 99%

Research on Wastewater Treatment Monitoring Algorithms Based on Deep Convolutional Neural Networks

Zhang

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Around the problems of data loss, noise, and different temporal and spatial scale features of urban wastewater treatment process data, a method of monitoring and predicting wastewater treatment process data based on deep convolutional neural networks is proposed in the paper. Firstly, to address the problem that urban wastewater treatment process data has multiple spatial and temporal scale characteristics, which makes it difficult for the data to be used effectively, a spatial and temporal data fusion model based on fuzzy neural network (FNN) is proposed. Fuzzy neural networks have strong generalization ability and robustness. Secondly, to enable accurate and real-time monitoring of the content of the monitored components in the effluent of the urban wastewater treatment process, an intelligent prediction model based on SDF-FNN is established for the effluent. Finally, in order to verify the effectiveness of this intelligent prediction model, the model is tested using data collected from actual municipal wastewater treatment plants. The experimental results show that the wastewater treatment intelligent monitoring model is effective and can predict the content of the effluent monitoring index with high accuracy.

show abstract

Systems Analysis for the Future of Solid Waste Management: Challenges and Perspectives

2015

Sustainable Solid Waste Management

View full text Add to dashboard Cite

Knowledge-based expert system applications in waste treatment operation and control

Cited by 8 publications

References 15 publications

Intelligent Models of the Quantitative Behavior of Microbial Systems

Intelligent Models of the Quantitative Behavior of Microbial Systems

Research on Wastewater Treatment Monitoring Algorithms Based on Deep Convolutional Neural Networks

Systems Analysis for the Future of Solid Waste Management: Challenges and Perspectives

Contact Info

Product

Resources

About