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Title: Scheduling and rescheduling for batch chemical plants
Author: Park, Sangdae
ISNI:       0000 0004 2671 9515
Awarding Body: The University of Manchester
Current Institution: University of Manchester
Date of Award: 2004
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The awareness for the schedule modification under process disturbances so-called rescheduling, has been growing in the area of chemical batch plants. For the last three decades, planning and scheduling have played a practical and crucial role in not only reducing the inefficiency of batch operations, but also increasing the productivity of batch plants. However, the off-line planning/scheduling can be very inefficient, or even infeasible to be performed when particularly certain undesirable disturbances occur during the operation period. In these cases, therefore, the schedule modification will be inevitably required to reduce or minimise the effects of the disturbances arisen. In this sense, a systematic methodology for the schedule modification is needed to support and guide decision-makers and operators. The development of the methodology is the main objective of this thesis, and the focus mainly lies on the integration between scheduling and rescheduling for chemical batch plants. Two different scheduling algorithms have been proposed in this thesis. The formulation (Model I) based on the concept of State-Task Network (STN) is proposed for the scheduling of multipurpose batch processes, while Model II facilitates the scheduling of multiple product batch plants. Both algorithms are based on the deterministic methods, and the global optimality can be guaranteed. Although Model I is formulated as a Mixed Integer Non-Linear Programming (MINLP) problem, the global optimality of Model I is guaranteed due to the convexity proved. On the other hand, Model II results in a Mixed Integer Linear Programming (MILP), hence the global optimality guaranteed. The performances of the scheduling algorithms are far better than other precedent algorithms, and the details of the computational results are shown in the corresponding sections. In particular, these two scheduling algorithms are reutilised as a deterministic-based rescheduling algorithms after certain modification such as fixing variables, adding or removing constraints, change of an objective function, etc. These modifications are highly dependent upon the given conditions, namely, case-by-case basis. Nevertheless, it provides us the good concept in the sense that the global optimality for the rescheduling can be guaranteed if non-convexity does not take place in the models by the modifications. As far as the global optimality for scheduling and rescheduling is guaranteed, the difference between scheduling and rescheduling will be the minimum (or maximum) effect caused by the disturbance occurred. On the other hand, heuristic or rule-based methods have advantages for the simplicity of the adaptation and/or the similarity with the original schedule, even though their optimality is not guaranteed. In multiple product batch plants, a rule-based method by using completion time algorithm is proposed for the processing time delays and unit failures. In contrast, a rule-based method for multipurpose batch processes is based on the recalculation of material balances that will be required for accommodating the losses of intermediates. For the selection of a rescheduling option against the disturbances arisen, the variability test has been performed in order to identify the most sensitive process variability, so called key variability. To identify the key variability, the accumulated loss of profit function has been introduced as a performance index. Then, the key variability against a process variation occurred has been determined by a variation with maximum index. Based on the key variability identified, the determination of a rescheduling option is made by the rescheduling methodology proposed. From the various examples tested, it is shown the that the approach proposed enables to guide for the selection of rescheduling options available by using the concept of key variability, and the identification of key variability provides good guidelines for decision-making of reactive schedule modification.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available