Real-Time Process Management for Materials Composition in Chemical Manufacturing

D'Ambrosio, Bruce; Fehling, Michael; Forrest, Stephanie; Raulefs, Peter; Wilber, B. Michael

doi:10.1109/mex.1987.4307067

Cited by 37 publications

(5 citation statements)

References 3 publications

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“…Adaptive control theory methods are based on control of current state and do not, themselves, define a path to a distant goal. Similar conclusions about the need for heuristic reasoning have been described for continuous materials processes (D'Ambrosio et al, 1987).…”

Section: Value Of Symbolic Reasoning To Process Controlsupporting

confidence: 67%

Intelligent control of complex materials processes

Pardee

Shaff

Hayes-Roth

1990

AIEDAM

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A blackboard based intelligent control system has been developed for a family of complex non-equilibrium materials processes. The system is being tested in the laboratory for control of a particular high risk, high value-added step in the manufacture of carbon-carbon composites. The system uses knowledge based methods in several fundamental ways to fill gaps left by control theory and process models. Most notable of these are (1) inferring from indirect measurements and history the process state at multiple, changing levels of abstraction, (2) anticipating problems and planning actions to reach goal (end of process) states, (3) selecting, executing and interpreting approximate models to predict process progression and (4) changing control objectives as the physical situation changes. The system has been demonstrated to substantially reduce processing time.

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Section: Value Of Symbolic Reasoning To Process Controlsupporting

confidence: 67%

Intelligent control of complex materials processes

Pardee

Shaff

Hayes-Roth

1990

AIEDAM

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“…Indeed, diffusion equations for momentum, heat and mass transfer all have similar form as Newton's equation, Fourier's conduction law and Fick's law, respectively. Material composition management (MCM) is the core problem in chemical and manufacturing processes [3]. Conservation of mass requires that all material entering the chemical process either accumulates or exits the process as product or waste (material balance).…”

Section: Lunar Industrial Ecologymentioning

confidence: 99%

Are There Biomimetic Lessons from Genetic Regulatory Networks for Developing a Lunar Industrial Ecology?

Ellery

2021

Biomimetics

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We examine the prospect for employing a bio-inspired architecture for a lunar industrial ecology based on genetic regulatory networks. The lunar industrial ecology resembles a metabolic system in that it comprises multiple chemical processes interlinked through waste recycling. Initially, we examine lessons from factory organisation which have evolved into a bio-inspired concept, the reconfigurable holonic architecture. We then examine genetic regulatory networks and their application in the biological cell cycle. There are numerous subtleties that would be challenging to implement in a lunar industrial ecology but much of the essence of biological circuitry (as implemented in synthetic biology, for example) is captured by traditional electrical engineering design with emphasis on feedforward and feedback loops to implement robustness.

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“…Sparse [35] is intended to process alarms of power systems. Other examples are MCM [36], DECA [3], IPCS [14], L*STAR [37], PISCES [38], DANTES [39] and AMPERES [40]). …”

Section: Expert Systems For Monitoring and Controlmentioning

confidence: 99%

Clinical communication and telemedicine

Coiera¹

2003

Guide to Health Informatics, 2Ed

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Human operators of monitoring and control systems typically perform knowledge intensive tasks requiring human intervention in diverse and sometimes critical situations. The cognitive overload to which they are submitted may motivate human errors which compromise safety and performance. Expert Systems offer the means to codify and automate the use of the knowledge of the best experts in a given domain, and may provide support to monitoring, diagnosis and control tasks. In this text we will make an overview of what expert systems are, what they may offer and what they don't. We will present their typical architecture and the most common knowledge representation formalisms and reasoning mechanisms, and discuss how they may deal with uncertainty. The lifecycle of an Expert System will also be presented, special attention being devoted to knowledge acquisition activities. An overview of some applications of expert systems to monitoring and control will be given, as well as some alternative approaches. INTRODUCTIONThe control needs of complex and expensive processes have motivated the development of sophisticated computational systems which allow the monitoring and control of complex processes by small crews of specialised human operators. Typically, these operators follow the process evolution through a set of instruments connected to a computer system which interacts with sensors, alarms and actuators. The control system is intended to minimize human intervention and to provide the operators with the maximum information on the controlled process. The operators' task essentially consists in trying to minimize failure risks while maximizing performance. The required degree of expertise depends on the complexity of the process and of the control system, on the importance, to people and equipment safety, of keeping the process in normal operation, and on the losses that an incorrect process control or its interruption may cause.In normal steady-state conditions, the control system takes charge of keeping the system in a given set point, and the operators confine themselves to monitoring the most important variables. Human intervention is required during system start-up and shut-down, and also to regulate the process to given resources and needs.Another, more critical, kind of situation occurs when equipment or process failures, or human errors, originate anomalous conditions. In such circumstances the control system presents a great quantity of information to the operators in a short time, originating in the most diverse sources, with the process conditions quickly changing and alarms escalating. In such a stressed situation, operators must discriminate actual faults from false alarm states, perform malfunction diagnosis, and plan and accomplish corrective actions 1 . This requires them to intensively resort to their knowledge on the process and on the control system. In particularly complex, stressed or time-critical situations, they 1 According to [1], "monitoring activities track process variables intelligent...

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Real-Time Process Management for Materials Composition in Chemical Manufacturing

Cited by 37 publications

References 3 publications

Intelligent control of complex materials processes

Intelligent control of complex materials processes

Are There Biomimetic Lessons from Genetic Regulatory Networks for Developing a Lunar Industrial Ecology?

Clinical communication and telemedicine

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