Automating operating procedure synthesis for batch processes

Viswanathan, Shankar; Johnsson, Charlotta; Srinivasan, Rajagopalan; Venkatasubramanian, Venkat; Årzén, Karl-Erik

doi:10.1016/s0098-1354(98)00228-2

Cited by 58 publications

(10 citation statements)

References 8 publications

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“…Purdue University introduced systematic approaches to describe operational knowledge on the basis of hazard and operability studies (HAZOP) (Srinivasan et al, 1998a(Srinivasan et al, , 1998bViswanathan et al, 1998aViswanathan et al, , 1998b. These approaches focused on describing operational behaviors.…”

Section: Discussionmentioning

confidence: 99%

Systems Approach to Process Lifecycle Engineering in Pharmaceutical Production

Kawai

Seki

Kitajima

et al. 2014

KAGAKU KOGAKU RONBUNSHU

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implementation, and change management of LCE. Applicability of this framework is shown using actual cases analysis of a pharmaceutical company. Chapter 4 presents a system to support information transactions between recipe scientists and process engineers; such transactions tend to fail without a suitable management system. In addition, a case-based reasoning approach is introduced as a cooperative method in the system to find relations between errors and deviations. Chapter 5 concludes this dissertation with future prospects. Structure of the Ph D thesis.

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Section: Discussionmentioning

confidence: 99%

Systems Approach to Process Lifecycle Engineering in Pharmaceutical Production

Kawai

Seki

Kitajima

et al. 2014

KAGAKU KOGAKU RONBUNSHU

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“…We will not provide a detailed review of HAZOP or the intelligent systems framework for it here as it is not the intent of this paper. This has been addressed elsewhere by the author [69][70][71][72][73].…”

Section: Intelligent Systems For Process Hazards Analysismentioning

confidence: 99%

Systemic risks management in complex process plants: Challenges, opportunities, and emerging trends

Venkatasubramanian

2010

2010 Conference on Control and Fault-Tolerant Systems (SysTol)

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The BP Deepwater Horizon oil spill disaster has reminded us, once again, the potential for systemic failures in complex engineered systems. But such systemic failures are not limited to the chemical and petrochemical industries alone. The 2003 Northeast electrical power blackout was a systemic failure. Financial disasters such as Enron, WorldCom, subprime derivatives market, and so on, also belong to the same class. Given the size, scope, and complexity of these modern engineered systems and their interactions, it is becoming increasingly difficult for people to anticipate, diagnose and control serious abnormal events in a timely manner. Practitioners in the process industries view this as the next major challenge in control systems research and application. There are two different, but related, components of the overall systemic risks management problem. One deals with the problem of process safety during real-time operations. The other deals with safety issues during the design and/or modifications of the plant or the processes. In this paper, I will present an overview of the challenges and the opportunities. Recent progress has promising implications on the use of intelligent systems for a variety of applications in the chemical, petrochemical, and pharmaceutical industries for inherently safer design, operator training, abnormal events management, and optimal process operations.

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“…There is one defined problem for each domain consisting of a high level goal that specifies a process to be performed. For example, the ITOPS problem [63] consists of five raw products, r 1 , r 2 , r 3 , r 4 and r 5 , initially in five different tanks. The goal is to obtain a final product, i 4 , by allowing a reaction to occur among them, according to the following scheme: mix r 1 , r 2 , r 3 to obtain i 1 ; filter i 1 to obtain i 3 and heat r 4 , r 5 and i 3 to obtain i 4 .…”

Section: Correctness Of Rulesmentioning

confidence: 99%

“…Artificial intelligence techniques are proving to be very useful in solving this task, allowing for an error-free, fast and low cost building process of these control programs [23,26,41,42,47,50,52]. In particular, it is becoming an area of growing interest for researchers from the field of AI planning systems [4,15,37,63]. The most commonly used planners in industry are based on hierarchical planning techniques [21,27,55] which are useful to represent the hierarchical structure of devices and their operation in manufacturing systems and are closer to the way control engineers represent a control program (modular and hierarchical).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Hybrid Hierarchical and Partial-Order Planners for Manufacturing Domains

Fernández

Aler

Borrajo

2005

Applied Artificial Intelligence

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The application of AI planning techniques to manufacturing systems is being widely deployed for all the tasks involved in the process, from product design to production planning and control. One of these problems is the automatic generation of control sequences for the entire manufacturing system in such a way that final plans can be directly used as the sequential control programs which drive the operation of manufacturing systems. Hybis is a hierarchical and nonlinear planner whose goal is to obtain partially ordered plans at such a level of detail that they can be used as sequential control programs for manufacturing systems. Currently, those sequential control programs are being generated by hand using modelling tools. This document describes a work whose aim is to improve the efficiency of solving problems with Hybis by using machine learning techniques. It implements a deductive learning method that is able to automatically acquire control knowledge (heuristics) by generating bounded explanations of the problem solving episodes. The learning approach builds on Hamlet, a system that learns control knowledge in the form of control rules.

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Automating operating procedure synthesis for batch processes

Cited by 58 publications

References 8 publications

Systems Approach to Process Lifecycle Engineering in Pharmaceutical Production

Systems Approach to Process Lifecycle Engineering in Pharmaceutical Production

Systemic risks management in complex process plants: Challenges, opportunities, and emerging trends

Machine Learning in Hybrid Hierarchical and Partial-Order Planners for Manufacturing Domains

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