Hazard and Operability (HAZOP) analysis is the study of systematically identifying every conceivable deviation, all the possible abnormal causes for such deviation, and the adverse hazardous consequences of that devia...
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Hazard and Operability (HAZOP) analysis is the study of systematically identifying every conceivable deviation, all the possible abnormal causes for such deviation, and the adverse hazardous consequences of that deviation in a chemical plant. HAZOP analysis is often carried out by a group of experts poring over the process flowsheets for weeks or months. Thus, it is a labor- and time-intensive process that would gain by automation. Previous work has addressed the issue of automating HAZOP analysis for continuous chemical plants. However, the HAZOP methodology of continuous processes cannot be applied to batch and semi-continuous plants as such because they have two additional sources of complexity. One is the role of operating procedures and operator actions in plant operation, and the other is the discrete-event character of batch processes. To represent these characteristics of batch operation, high-level Petri nets with timed transitions and colored tokens are used. The causal relationships between process variables are represented using subtask digraphs. This Petri net-Digraph model based framework has been implemented in G2 for a pharmaceutical batch process case study. The salient aspects of this framework are discussed with the aid of this case-study.
Synthesis of plant-wide control structures involves identifying control objectives that are consistent with the overall production goals and formulating control strategies in a multivariate environment. This is a comp...
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Synthesis of plant-wide control structures involves identifying control objectives that are consistent with the overall production goals and formulating control strategies in a multivariate environment. This is a complex task. A hierarchical framework is proposed in which the plant is vertically decomposed into a set of representations of different degrees of abstraction. Starting from the input-output level, we develop a control structure for the overall plant. Then, we move onto the next level where the model, the control objectives and the control strategies are being refined. This procedure is repeated until all levels of the plant have been analyzed. The hierarchy of control strategies can be integrated to form a multi-horizon control system. In this paper, the key conceptual steps of the methodology are being demonstrated.
Designing competitive production processes for the manufacturing of new chemicals requires meeting conflicting objectives such as economic performance, health and safety requirements and compliance with environmental ...
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Designing competitive production processes for the manufacturing of new chemicals requires meeting conflicting objectives such as economic performance, health and safety requirements and compliance with environmental regulations. While new conceptual processing schemes can be effectively generated through the BDK synthesis tools, Simulator_Evaluator and process_Synthesizer, the ecological soundness of these candidates has to be assessed with respect to their overall performance in order to discriminate between promising candidates and inferior designs. The methodology of the process_Assessor with its subsystems Material_Assessor and Treatment_Selector, supports evaluation of the quality for each of those designs in terms of their economic and ecological impact. Its features include impact assessment and compliance with regulations caused by all its process streams. The Treatment_Selector expert system suggest feasible treatment technologies for all waste streams and calculates the associated treatment costs. The final validation of the processing schemes includes cost estimates for the appropriate waste treatment strategy. The assessment of materials as well as consideration of the effort for waste treatment allows for identification of improvement potential for promising candidates and the elimination of inferior designs.
Diagnostic problem solving forms a major part of the operator's activity in complex chemical processes. It cannot be overemphasized that training operators on fault diagnosis and developing appropriate corrective ...
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Diagnostic problem solving forms a major part of the operator's activity in complex chemical processes. It cannot be overemphasized that training operators on fault diagnosis and developing appropriate corrective strategies will help minimize process upsets, and thereby improve the reliability and safety of the plant. An intelligent operator training framework has been proposed and implemented to improve training effectiveness for diagnostic problem solving. A dynamic simulator coupled with an intelligent computer-based tutor constitutes the intelligent training system (ITS). It helps operators to organize system knowledge and operational information including symptom-cause relationship, and may enhance their performance. Our learning environment is composed of an interactive, real-time simulation of a chemical process plant, a computer model of expert operators, components that allow for automatic evaluation and coaching of the trainees in order to improve their diagnostic skills, and graphical user interfaces. To make our intelligent training system more effective, the process-related information/explanations including digraph-based symptom-cause relation are supplied through intelligent hypermedia interface. This system can also be used as an on-line assistant of process operators and for the training of remedial actions, and they are part of further enhancements being planned.
Designing new molecules possessing desired properties is an important and difficult problem in the chemical, material, and pharmaceutical industries. The standard approach to this problem consists of an iterative form...
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Designing new molecules possessing desired properties is an important and difficult problem in the chemical, material, and pharmaceutical industries. The standard approach to this problem consists of an iterative formulation, synthesis, and evaluation cycle that is long, time-consuming, and expensive. Current computer-aided design approaches include heuristic and exhaustive searches, mathematical programming, and knowledge-based systems methods. While all these methods have a certain degree of appeal, they suffer from drawbacks in handling combinatorially large, nonlinear search spaces. Recently, a genetic algorithm-based approach was shown to be quite promising in handling these difficulties. In this paper, we investigate the performance of the basic genetic design framework for larger search spaces. We also present an extension to the basic genetic design framework by incorporating higher-level chemical knowledge to handle constraints such as chemical feasibility, stability, and complexity better. These advances are demonstrated with the aid of a polymer design case study.
The BatchDesign-Kit is a software system that is aimed to support the development and design of batch processes for the manufacturing of pharmaceuticals and specialty chemicals, by integrating economic and ecological ...
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The BatchDesign-Kit is a software system that is aimed to support the development and design of batch processes for the manufacturing of pharmaceuticals and specialty chemicals, by integrating economic and ecological considerations. One of its three subsystems, the process Assessor deploys techniques and methodologies which allow the rapid generation and evaluation of alternative batch processing schemes. Using a state-transition network as the basic model of the batch process, the process Assessor allows the simulation of batch process, assessment of materials from the ecological point of view, and the selection of the most appropriate treatment options for the process wastes. This paper gives an overview of the character, structure and facilities of the process Assessor.
Designing new molecules possessing desired properties is an important activity in the chemical and pharmaceutical industries. Much of this design involves an elaborate and expensive trial-and-error process that is dif...
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Designing new molecules possessing desired properties is an important activity in the chemical and pharmaceutical industries. Much of this design involves an elaborate and expensive trial-and-error process that is difficult to automate. The present study describes a new computer-aided molecular design approach using genetic algorithms. Unlike traditional search and optimization techniques, genetic algorithms perform a guided stochastic search where improved solutions are achieved by sampling areas of the parameter space that have a higher probability for good solutions. Moreover, genetic algorithms allow for the direct incorporation of higher level chemical knowledge and reasoning strategies to make the search more efficient. The utility of genetic algorithms for molecular design is demonstrated with some case studies in polymer design. The merits and potential deficiencies of this approach are also discussed.
A methodology for pattern-based supervisory control and fault diagonsis is presented, based on the multi-scale extraction of trends from process data described in Part III of this series (Bakshi and Stephanopoulos, Co...
A methodology for pattern-based supervisory control and fault diagonsis is presented, based on the multi-scale extraction of trends from process data described in Part III of this series (Bakshi and Stephanopoulos, Computers Chem. Engng 17, 1993). An explicit mapping is learned between the features extracted at multiple scales, and the corresponding process conditions, using the technique of induction by decision trees. Simple rules may be derived from the induced decision tree, to relate the relevant qualitative or quantitative features in the measured process data to process conditions. These rules are often physically interpretable and provide physical insight into the process. Industrial case studies from fine chemicals manufacturing, reactive crystallization and fed-batch fermentation are used to illustrate the characteristics of the pattern-based learning methodology and its application to process supervision and diagnosis.
To overcome the limitations of the black-box character of the standard neural network approaches, a network with ellipsoidal units has recently been proposed. This novel approach addresses three main issues: (a) to un...
To overcome the limitations of the black-box character of the standard neural network approaches, a network with ellipsoidal units has recently been proposed. This novel approach addresses three main issues: (a) to understand better and represent the nature of fault classification boundaries;(b) to determine the network structure without the usual trial and error schemes;and (c) to avoid erroneous generalizations. In this paper, we develop the ellipsoidal units approach further by addressing the problem of real-time large-scale fault diagnosis. For such applications neural networks become very large and complex, making the training and interpretation tasks time consuming and difficult. Networks with ellipsoidal units naturally lend themselves to the development of decomposition techniques that result in the training of smaller networks with fewer training patterns. Three decomposition strategies, namely, network decomposition, training set decomposition, and input space decomposition, have been developed for large-scale industrial processes. The results for the real-time diagnosis of an Amoco model IV FCCU simulation case study are discussed. Network size and diagnostic performance are compared with alternative approaches, such as backpropagation networks and radial basis function networks.
This paper presents a formal methodology for the analysis of process signals and the automatic extraction of temporal features contained in a record of measured data. It is based on the multiscale analysis of the meas...
This paper presents a formal methodology for the analysis of process signals and the automatic extraction of temporal features contained in a record of measured data. It is based on the multiscale analysis of the measured signals using wavelets, which allows the extraction of significant temporal features that are localized in the frequency domain, from segments of the record of measured data (i.e. localized in the time domain). The paper provides a concise framework for the multiscale extraction and description of temporal process trends. The resulting algorithms are analytically sound, computationally very efficient and can be easily integrated with a large variety of methods for the interpretation of process trends and the automatic learning of relationships between causes and symptoms in a dynamic environment. A series of examples illustrate the characteristics of the approach and outline its use in various settings for the solution of industrial problems.
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