Treffer: Optimality assessment with optimality recovery for multi-modal process operations

Title:
Optimality assessment with optimality recovery for multi-modal process operations
Authors:
Contributors:
Louw, Tobias Muller, Bradshaw, Steven Martin, Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
Publisher Information:
Stellenbosch University
Publication Year:
2023
Collection:
Stellenbosch University: SUNScholar Research Repository
Document Type:
Dissertation thesis
File Description:
xiii, 128 pages : illustrations; application/pdf
Language:
English
Rights:
Stellenbosch University
Accession Number:
edsbas.EF365E3E
Database:
BASE

Weitere Informationen

Thesis (MEng)--Stellenbosch University, 2023. ; ENGLISH ABSTRACT: The field of optimality assessment (OA) is a recent development within data-driven process monitoring. OA is a plant-wide approach to real-time optimisation that aims to minimise nonoptimal online operation caused by (1) disturbances that cannot be rejected by the regulatory control system, or (2) inevitable controlled variable setpoint drift. The distinguishing design factor of OA, as opposed to fault- and quality-related process monitoring, is the incorporation of the comprehensive economic index to quantify overall plant optimality or performance. Since optimality is only available in retrospect, the estimation of optimality during real-time operation allows for prompt intervention when nonoptimal conditions arise, so as to prevent prolonged conditions of deteriorated Performance. This work proposes an alternative to the conventional latent variable model-based OA workflows, which employ monitoring charts founded on Shewhart- or similarity-based statistics. The proposed OA workflow is designed to account for continuous and multimodal industrial process data without transition states. The workflow is developed under the framework of a novel optimality landscape which captures various stable modes in the historical process dataset as well as their associated optimality grade. In addition, the proposed optimality landscape captures the cause for the historical operating point shifting from one mode to another, which is termed a modal shift. Two types of modal shifts are captured, namely those that are caused by disturbances, or by SP change(s) that are implemented by the control system or operational team. The offline phase of the proposed OA workflow constructs a holistic reference tool called the optimality graph. The nodes of the optimality graph are discovered by 𝑘-means clustering in the latent variable space, whereas the edges are discovered by the proposed TASLA (time-based alignment of modal shifts and plant log algorithm) technique. ...