Development of a real-time expert system for wastewater treatment plants control

Serra, P.; Lafuente, Javier; Moreno, R.; Prada, César de; Poch, Manel

doi:10.1016/0967-0661(93)91624-6

Cited by 23 publications

(5 citation statements)

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“…Software applications or programming environments are mandatory for testing a developed algorithm or implementing it in real time. In earlier days, most AI‐based control methodologies were developed using programming languages such as FORTRAN and C, and software such as LabVIEW, [ 51 ] ACSL, [ 57 ] ASPX, [ 59 ] and SPARC. [ 61 ] Later, after the development of MATLAB, [ 48 ] researchers found it more suitable because of its functional varieties, effectiveness in real‐time simulation through block diagram generation (SIMULINK), and easy mode of integration and code transfer with experimental interfaces such as LabVIEW, WinIso, [ 198 ] and VisiDAQ.…”

Section: Trends In Ai‐based Control Applicationsmentioning

confidence: 99%

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Dutta

Upreti

2021

Can J Chem Eng

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In the last three decades, artificial intelligence (AI) has been increasingly and vigorously utilized for process control in chemical, biochemical, and biomedical engineering. These disciplines frequently involve fairly sophisticated processes under risks of operational upsets, and thus have an ever increasing demand of superior control strategies. As the deployment of AI in process control pushes the limits in this regard, the research advances, which are multitudinous and varied, need to be assimilated and organized to help promote further utilization and progress in the field. To that end, we examine more than 280 relevant research publications, and systematically collate the information. The AI‐based technologies are classified, and their over‐arching control paradigm is presented. Common AI‐based control technologies are then presented, which are based on expert systems, fuzzy logic, artificial neural networks, nature‐inspired algorithms, and hybrid approaches. Their working principles, types, and implementations are summarized along with advantages, limitations, and comparisons, if available. Important applications in the above disciplines are included with the help of tables that capture important details. A discussion is also provided on advanced and newly emerging AI‐based control technologies with pertinent applications. Overall trends are analyzed, and future prospects are identified based on the survey.

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Section: Trends In Ai‐based Control Applicationsmentioning

confidence: 99%

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Dutta

Upreti

2021

Can J Chem Eng

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“…Besides the control of single treatment units (i.e., 'local control'), RTC can be applied to the whole WWTP to coordinate and optimise the control in the plant systematically (Duyy & Laboratorium, 1975;Serra et al, 1993). This is termed 'global control' as defined in Schütze et al (2002), referring to control where sensor information from within the same subsystem (i.e., sewer system, WWTP, receiving water) is used to determine the setting of a control device.…”

Section: Integrated Control Of Uwwsmentioning

confidence: 99%

Integrated modelling and control of urban wastewater systems

Meng

Saagi

2021

Water-Wise Cities and Sustainable Water Systems: Concepts, Technologies, and Applications

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“…Some KBS have been designed with the main purpose of on-line supervision (Verheijen et al, 1997), although no real-time supervisor control has been used. In previous work, our group focused on knowledge structure development (Serra et al, 1993(Serra et al, , 1994(Serra et al, , 1997Sánchez et al, 1996;R-Roda et al, 1999aR-Roda et al, , 1999b and control strategies (Moreno et al, 1992). The current work is primarily concerned with the real-time implementation of the data knowledge structure (Baeza, 1999;Baeza et al, 2000).…”

Section: Wwtp Control Systemsmentioning

confidence: 99%

A Distributed Control System Based on Agent Architecture for Wastewater Treatment

Baeza

Gabriel

Béjar

et al. 2002

Computer aided Civil Eng

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The development and implementation of an agent-based distributed control system in a wastewater treatment plant (WWTP) are shown. The hardware architecture contains different supervision levels, including two autonomous process computers, a programmable logic controller (PLC) and a knowledge-based system (KBS), acting at the top supervisory level. The knowledge is organized in several distributed agents, representing the available knowledge for every subprocess of the WWTP. In addition to these independent agents, a supervisor agent acts as its master. The distributed multi-agent architecture improves previous developments with monolithic KBS, allowing the development of independent and reusable agents. Different strategies have been included, adapted, and modified without a great programming effort. The developed system has involved the transformation of a classical control system into a system that enables a WWTP to respond to different usual problems. Finally, the system has been satisfactorily validated supervising a pilot WWTP for more than 3 years.

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Development of a real-time expert system for wastewater treatment plants control

Cited by 23 publications

References 1 publication

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Integrated modelling and control of urban wastewater systems

A Distributed Control System Based on Agent Architecture for Wastewater Treatment

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