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We propose a novel solution algorithm for the integrated scheduling and dynamic optimization for sequential batch processes in this work. The integrated problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which could be large scale and challenging to solve. To address this computational challenge, we propose an efficient and adaptive surrogate-based algorithm for solving...
In this work, an integrated scheduling and dynamic optimization problem for sequential batch processes is proposed. We then address the issue of uncertainty in sequential batch processes using a robust counterpart approach. When uncertainty occurs, which is inevitable in industrial processes, the robust solution guarantees feasibility while the deterministic solution may lead to significant drop in...
We solve the challenging problem of integrated planning, scheduling, and dynamic optimization for sequential batch processes with fixed batch sizes. The integrated problem is first formulated into a complicated mixed-integer nonlinear programing (MINLP) problem. There are a planning model, multiple scheduling models in planning periods, and a number of dynamic models describing task execution processes...
Integration of scheduling and dynamic optimization significantly improves the overall performance of a production process compared to the traditional sequential method. However, most integrated methods focus on solving deterministic problems without explicitly taking process uncertainty into account. We propose a novel integrated method for sequential batch processes under uncertainty. The integrated...
We address the integration of scheduling and dynamic optimization for batch chemical processes. The processes can have complex network structures, allowing material splitting and mixing. The integrated problem is formulated as a mixed-integer dynamic optimization problem. To reduce the computational complexity, we develop a tailored and efficient decomposition method based on the framework of generalized...
Online integration of scheduling and control is crucial to cope with process uncertainties. We propose a new online integrated method for sequential batch processes, where the integrated problem is solved to determine controller references rather than process inputs. To achieve the goal of computational efficiency and rescheduling stability, a rolling horizon approach is developed. A reduced integrated...
Scheduling and control problems are traditionally solved sequentially. However, integration of both problems can result in a better overall performance. A main challenge in the integration is the solution to the derived mixed-integer dynamic optimization (MIDO) problem. As a consequence, most integration methods can only be applied offline. To overcome the challenge, we present a novel integration...
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