A nonlinear planning methodology for synthesizing operating procedures for continuous chemical processes is developed. The methodology makes use of the hierarchical, distributed, objected-oriented modelling framework ...
A nonlinear planning methodology for synthesizing operating procedures for continuous chemical processes is developed. The methodology makes use of the hierarchical, distributed, objected-oriented modelling framework described in the first paper of the two-part series. The important algorithms used in the various stages of planning are presented, and the properties of these algorithms, in terms of their correctness and completeness, are stated and proved.
Modelling issues associated with the synthesis of process operations on a plant-wide scale are discussed, and a hierarchical, distributed, object-oriented modelling framework described. The modelling approach is gener...
Modelling issues associated with the synthesis of process operations on a plant-wide scale are discussed, and a hierarchical, distributed, object-oriented modelling framework described. The modelling approach is generic, and is demonstrated to be suitable for computer-automation of the synthesis of operating procedures. In Part II, provably correct and complete nonlinear planning algorithms based on these models are presented. It is shown that domain-independent planning methodologies using the functional operators required for the synthesis of operating procedures are computationally intractable. Consequently, domain-specific knowledge is exploited within the modelling structure presented in this paper in order to define a tractable methodology for nonlinear planning of process operations. In this paper, we have discussed some of the issues involved in automating the synthesis of plant-wide operating procedures for continuous chemical process. In particular, we have focussed on the question of how to effectively model, on the computer, knowledge about the operation of chemical plants. Previous attempts at modelling have either relied on the restrictive operator- based modelling paradigm, or have employed extremely simplified or situation-specific models. A modelling technique based on a functional operator structure was also considered. This representation accounted for the fact that the outcome of applying an operator is dependent upon the state of the system before execution, and was shown to reduce to the conditional action scheme investigated by researchers in artificial intelligence. Planning with conditional operators involves a plan generation step which is NP-hard, and is therefore considered to be computationally intractable. This justifies the adoption of a domain-specific approach to the planning of process operations, and explains why no algorithmic solution has yet been developed. Realizing the limitations of the operator model, a hierarchical, distrib
The increasing complexity of chemical plants have caused the chemical industry to look towards automated and structured approaches for identifying and diagnosing process abnormalities during the normal course of a pla...
The increasing complexity of chemical plants have caused the chemical industry to look towards automated and structured approaches for identifying and diagnosing process abnormalities during the normal course of a plant's daily operation. One such approach is to make use of a knowledge-based expert system which can perform diagnostic analysis. Many of the recent attempts have focused on using compiled process knowledge, relating symptoms to causes represented as production rules in the knowledge base. Though this leads to real-time diagnostic efficiency, such expert systems lack flexibility to process changes and are incapable of diagnosing novel symptom combinations. The rule-based approaches also lead to knowledge bases that are difficult to develop and maintain as they lack structures that reflect higher-level organization of process knowledge. In this paper, we present a diagnostic methodology that provides the means to solve these problems. We advocate a diagnostic methodology that integrates compiled knowledge with deep-level knowledge, thus achieving diagnostic efficiency without sacrificing flexibility and reliability under novel circumstances. To formalize such an integration, we also propose an object-oriented two-tier knowledge base that houses process-specific compiled knowledge in the top-tier and process-general deep-level knowledge in the bottom-tier. The diagnostic reasoning effectively alternates between the two-tiers of knowledge for efficient and complete diagnosis. An important aspect of diagnostic reasoning is to be able to generate potential causes of the observed symptoms or faults as candidate malfunction hypotheses. We describe an agenda-based inference control algorithm that generates malfunction hypotheses by deriving them from structural and functional information of the process. We discuss the salient features of an expert system, called MODEX2, that has been implemented using these ideas.
The O(M) system is aimed at formalizing reasoning with approximate relations among quantities—relations like “much smaller than” or “slightly larger than.” O(M) is based on seven primitive relations among quantit...
The O(M) system is aimed at formalizing reasoning with approximate relations among quantities—relations like “much smaller than” or “slightly larger than.” O(M) is based on seven primitive relations among quantities, and compound relations formed as implicit disjunctions of consecutive primitives. In the interpretation of the relations, strict interpretation allows exact conservative inferences, while heuristic interpretation allows inferences more aggressive and human-like, by permitting some slack at each inference step. Inference strategies within O(M) are based on propagation of order-of-magnitude relations through properties of the relations, solved or unsolved algebraic constraints and rules. Assumption-based truth-maintenance is used, and the physical dimensions of quantities efficiently constrain the inferences. Statement of goals allows more effective employment of the constraints and focuses the system's opportunistic forward reasoning. O(M) relations permit order-of-magnitude analysis in processengineering. The O(M) system is suitable for many processengineering activities, such as preliminary design of process flowsheets, planning of process operations, design of control structures for chemical plants, fault simulation and diagnosis, process trend analysis and analysis of biochemical pathways.
A nonlinear dynamic model of the Czochralski process, valid throughout the batch growth cycle is derived for use in designing an improved process controller. The model is a lumped element representation of the major s...
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A nonlinear dynamic model of the Czochralski process, valid throughout the batch growth cycle is derived for use in designing an improved process controller. The model is a lumped element representation of the major system components, and simulates the dynamic system response to disturbances and system inputs. The linearized model is used to determine the system eigenstructure, revealing the system stability, transient response constants, and coupling. Significant results for control design include the identification of the basic time varying nature of the eigenstructure and the disturbances acting on the system, and identification of mechanisms that affect the transient system characteristics. In addition, the growth dynamic effects of liquid encapsulation, low thermal gradient schemes, and magnetic fields are discussed.
This paper outlines the structure and implementational features of the DESIGN-KIT, a software support environment developed to aid processengineering activities such as: synthesis of process flowsheets, configuration...
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This paper outlines the structure and implementational features of the DESIGN-KIT, a software support environment developed to aid processengineering activities such as: synthesis of process flowsheets, configuration of control loops for complete plants, planning and scheduling of plant-wide operations and operational analysis. Based on object-oriented and data-driven programming styles, the paper discusses how the DESIGN-KIT is constructed to provide a rich repertory of declarative and procedural knowledge for the development of analytic- or design-oriented knowledge-based expert systems. A series of illustrations describe the construction of knowledge bases, graphic interface support, equation-oriented simulation and design, order-of-magnitude analysis, reasoning strategies and other facilities of the DESIGN-KIT.
This article outlines the use of artificial intelligence in three areas of biotechnology: (a) exploration of new production routes for various bioproducts; (b) design of mammalian cell biofermentors; and (c) synthesis...
This article outlines the use of artificial intelligence in three areas of biotechnology: (a) exploration of new production routes for various bioproducts; (b) design of mammalian cell biofermentors; and (c) synthesis of downstream processing schemes for the separation and purification of proteins. Until recently, all of these areas have been ‘knowledge intensive’, driven by the incisive expertise of scientists and engineers, and quite resistant to analytic and rigorous mathematical formulations and solutions. Here we describe the ‘prototype intelligent system’ used in the above three areas, and attempt simple projections on the use of artificial intelligence in biotechnology.
Soft robots’ flexibility and compliance give them the potential to outperform traditional rigid-bodied robots while performing multiple tasks in unexpectedly changing environments and conditions. However, soft robots...
Soft robots’ flexibility and compliance give them the potential to outperform traditional rigid-bodied robots while performing multiple tasks in unexpectedly changing environments and conditions. However, soft robots are yet to reveal their full potential; nature is still far more advanced in several areas, such as locomotion and manipulation. To understand what limits their performance and hinders their transition from laboratory to real-world conditions, future studies should focus on understanding the principles behind the design and operation of soft robots. Such studies should also consider the major challenges with regard to complex materials, accurate modeling, advanced control, and intelligent behaviors. As a starting point for such studies, this review provides a current overview of the field by examining the working mechanisms of advanced actuation and sensing modalities, modeling techniques, control strategies, and learning architectures for soft robots. Next, we summarize how these approaches can be applied to create sophisticated soft robots and examine their application areas. Finally, we provide future perspectives on what key challenges should be tackled first to advance soft robotics to truly add value to our society.
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