Using Simulation for Scheduling and Rescheduling of Batch Processes

The problem of scheduling multiproduct and multipurpose batch processes has been studied for more than 30 years using math programming and heuristics. In most formulations, the manufacturing recipes are represented by simplified models using state task network (STN) or resource task network (RTN), transfers of materials are assumed to be instantaneous, constraints due to shared utilities are often ignored, and scheduling horizons are kept small due to the limits on the problem size that can be handled by the solvers. These limitations often result in schedules that are not actionable. A simulation model, on the other hand, can represent a manufacturing recipe to the smallest level of detail. In addition, a simulator can provide a variety of built-in capabilities that model the assignment decisions, coordination logic and plant operation rules. The simulation based schedules are more realistic, verifiable, easy to adapt for changing plant conditions and can be generated in a short period of time. An easy-to-use simulator based framework can be developed to support scheduling decisions made by operations personnel. In this paper, first the complexities of batch recipes and operations are discussed, followed by examples of using the BATCHES simulator for off-line scheduling studies and for day-to-day scheduling

Incorporating Enhanced Decision-Making Capabilities into a Hybrid Simulator for Scheduling of Batch Processes

A simulation model can accurately capture the details of product recipes in a batch process. By incorporating enhanced capabilities for making key assignment decisions in the simulation executive a model can mimic the experiential knowledge and rules employed in operating a process. As the process complexity and problem size increase using the mathematical programming (MP) techniques to generate schedules becomes increasingly difficult. A simulation run typically takes very little computation time and generates a schedule that is verifiable. Moreover, the model can be used to explore a wide range of parametric space to evaluate alternate policies and the impact of process uncertainties. Although there is no guarantee of optimality, the quality of schedules thus generated is very good and can be deployed in operations. In this paper the decision-making capabilities of the BATCHES simulator are presented with its application to a set of scheduling problems reported extensively in the literature. The results show that ‘smart’ simulation can be used effectively for a large set of scheduling problems

Incorporating Enhanced Decision-Making Capabilities into a Hybrid Simulator for Scheduling of Batch Processes

A simulation model can accurately capture the details of product recipes in a batch process. By incorporating enhanced capabilities for making key assignment decisions in the simulation executive a model can mimic the experiential knowledge and rules employed in operating a process. As the process complexity and problem size increase using the mathematical programming (MP) techniques to generate schedules becomes increasingly difficult. A simulation run typically takes very little computation time and generates a schedule that is verifiable. Moreover, the model can be used to explore a wide range of parametric space to evaluate alternate policies and the impact of process uncertainties. Although there is no guarantee of optimality, the quality of schedules thus generated is very good and can be deployed in operations. In this paper the decision-making capabilities of the BATCHES simulator are presented with its application to a set of scheduling problems reported extensively in the literature. The results show that ‘smart’ simulation can be used effectively for a large set of scheduling problems

Orbit optimisation

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Enchancing abnormal events management by the use of quantitative process hazards analysis results

Thorough process abnormal events management (AEM) requires accurate fault diagnosis and also a complete supporting system for making correct decisions. In this work, an effective and dynamic integration of AEM and process hazards analysis (PHA) is carried out. In the integrated system, the HAZOP analysis is extended for its use as a supporting tool when facing online abnormal situations. Thus, the extended HAZOP analysis includes quantitative information, in the form of key variables thresholds, and incorporates a set of corrective actions (CAs) to mitigate the harmful considered consequences. The dynamic integration is achieved by generating a protocol where CAs are ranked by the seriousness and urgency of the consequences that they mitigate. The supporting system is developed in a Matlab and Aspen Dynamics integrated framework. An industrial sour water stripping plant is used as case study.Peer Reviewe