A Methodological Approach to Knowledge-Based Control and Its Application to a Municipal Solid Waste Incineration Plant

Carrasco, F.; Llauró, X.; Poch, M.

doi:10.1080/00102200500248243

“…To achieve a particular outcome, people are required to make multiple decisions that have to accommodate multiple elements of the system, each of which can change in real time, and with different forms of feedback (i.e., random, positive, negative, delayed). The next three examples (Brandouy, 2001; Carrasco, Llauró, & Poch, 2008; Sarter, Mumaw, & Wickens, 2007, respectively), like the first, involve controlling variables as they change in real time. However, these tasks examine the impact of experience on controlling real as well as simulated control systems.…”

Section: What Do the Above Examples Have In Common?mentioning

confidence: 99%

Controlling uncertainty: A review of human behavior in complex dynamic environments.

Osman

¹

2010

Psychological Bulletin

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Complex dynamic control tasks (CDC tasks) are a type of problem-solving environment used for examining many cognitive activities (e.g., attention, control, decision making, hypothesis testing, implicit learning, memory, monitoring, planning, and problem solving). Because of their popularity, there have been many findings from diverse domains of research (Economics, Engineering, Ergonomics, Human Computer Interaction (HCI), Management, Psychology), which remain largely disconnected from each other. The objective of this article is to review theoretical developments and empirical work on CDC tasks, and to introduce a novel framework (Monitoring and Control framework) as a tool for integrating theory and findings. The main thesis of the Monitoring and Control framework is that CDC tasks are characteristically uncertain environments, and subjective judgments of uncertainty guide the way in which monitoring and control behaviors attempt to reduce it. The article concludes by discussing new insights into continuing debates and future directions for research on CDC tasks. Controlling Uncertainty 3Key words: Monitoring, Control, Uncertainty, Dynamic, Complex tasks, Causality, Agency Controlling Uncertainty 4Controlling Uncertainty: A Review of Human Behavior in Complex Dynamic Environments Problem solving is regarded by many (Anderson & Lebiere, 1998;Laird, Newell, & Rosenbloom, 1987;Newell & Simon, 1972;Sweller, 2003) as the most important cognitive activity in everyday and professional contexts, especially problem solving of the kind needed to control complex dynamic environments. As the complexity of the systems that we interact with in our daily lives grows (e.g., phones, computers, automated driving systems) so too does the importance of research on how we learn to control dynamic environments. Before detailing the issues surrounding the various research domains that investigate control behaviors, the illustrations below provide a general flavor of the kind of real world and laboratory tasks in which control behaviors have been studied. Illustration 1: Ecosystem Control System. In a laboratory-based task, people are presented with a simulated environment of an African landscape with different flora and fauna. The goal is to manage the environment in order to improve the living conditions of the native population.This involves controlling the system over several weeks by managing many interconnected variables that have a complex feedback process, both negative and positive, and with time delays.Illustration 2: Automated Pilot System. Trained pilots are required to take part in flight scenarios (including take-off and landing) in a flight simulator, in which they control the system while responding to a variety problems, including delays between their commands and their actual execution in the system, failings in the system, and miscued problems from the control panel. Controlling Uncertainty 5Illustration 3: Solid Incineration Plant. A municipal solid waste incineration plant is a largescale industrial syste...

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“…Shen et al [65] summarized expert experience as fuzzy control rules and implemented them in an MSWI plant in Shenzhen. Carrasco et al [145] developed a combustion control system based on expert rules for an MSWI plant in Spain. However, rule-based control systems face challenges in maintaining stable operation in the presence of frequent fluctuations in operating conditions.…”

Section: Research Of Non-acc Systemmentioning

confidence: 99%

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Tang,

Wang,

Xia

et al. 2024

Sustainability

4

0

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Artificial intelligence (AI) has found widespread application across diverse domains, including residential life and product manufacturing. Municipal solid waste incineration (MSWI) represents a significant avenue for realizing waste-to-energy (WTE) objectives, emphasizing resource reuse and sustainability. Theoretically, AI holds the potential to facilitate optimal control of the MSWI process in terms of achieving minimal pollution emissions and maximal energy efficiency. However, a noticeable shortage exists in the current research of the review literature concerning AI in the field of WTE, particularly MSWI, hindering a focused understanding of future development directions. Consequently, this study conducts an exhaustive survey of AI applications for optimal control, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance. Timeline diagrams depicting the evolution of AI technologies in the MSWI process are presented to offer an intuitive visual representation. Each category undergoes meticulous classification and description, elucidating the shortcomings and challenges inherent in current research. Furthermore, the study articulates the future development trajectory of AI applications within the four fundamental categories, underscoring the contribution it makes to the field of MSWI and WTE.

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“…random, positive, negative, delayed). The next three examples (Brandouy, 2001;Carrasco, Llauró, & Poch, 2008;Sarter, Mumar, & Wickerns, 2007), like the first, involve controlling variables as they change in real time. However, these tasks examine how experts interact with genuine or simulated control systems.…”

Section: What Do the Above Examples Have In Common?mentioning

confidence: 99%

Controlling Uncertainty

Osman¹

2010

View full text Add to dashboard Cite

Complex dynamic control tasks (CDC tasks) are a type of problem-solving environment used for examining many cognitive activities (e.g., attention, control, decision making, hypothesis testing, implicit learning, memory, monitoring, planning, and problem solving). Because of their popularity, there have been many findings from diverse domains of research (Economics, Engineering, Ergonomics, Human Computer Interaction (HCI), Management, Psychology), which remain largely disconnected from each other. The objective of this article is to review theoretical developments and empirical work on CDC tasks, and to introduce a novel framework (Monitoring and Control framework) as a tool for integrating theory and findings. The main thesis of the Monitoring and Control framework is that CDC tasks are characteristically uncertain environments, and subjective judgments of uncertainty guide the way in which monitoring and control behaviors attempt to reduce it. The article concludes by discussing new insights into continuing debates and future directions for research on CDC tasks.

show abstract

A Methodological Approach to Knowledge-Based Control and Its Application to a Municipal Solid Waste Incineration Plant

Cited by 7 publications

References 11 publications

Controlling uncertainty: A review of human behavior in complex dynamic environments.

Controlling uncertainty: A review of human behavior in complex dynamic environments.

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Controlling Uncertainty

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