[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127979-en":3,"doc-seo-127979-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127979,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Advances in Optimisation and Machine Learning for Process Systems Engineering - Doctoral thesis","Optimisation is a key tool across process systems engineering, supporting design, control, process identification, and more. This doctoral thesis introduces new optimisation applications and reliable, efficient methods for solving optimisation problems. Recurrent themes include using machine learning to reduce online computational effort, optimisation under uncertainty, and model predictive control. The thesis compiles research results in two parts: application-driven studies and theory/algorithm-driven studies.","Doctoral theses at NTNU, 2024:85  \nEvren Mert Turan  \nAdvances in Optimisation and Machine Learning for Process Systems Engineering  \nDoctora l thesis  \nNT NU  \nNorwegian University of Science and Technology Thesis for the Degree of Ph ilosophiae Doctor Faculty of Natural Sciences  \nDepartment of Chemical Engineering  \nEvren Mert Turan  \nAdvances in Optimisation and Machine Learning  \nfor Process Systems Engineering  \nThesis for the Degree of Philosophiae Doctor Trondheim, March 2024  \nNorwegian University of Science and Technology Faculty of Natural Sciences  \nDepartment of Chemical Engineering  \nNTNU  \nNorwegian University of Science and Technology Thesis for the Degree of Philosophiae Doctor Faculty of Natural Sciences  \nDepartment of Chemical Engineering  \n© Evren Mert Turan  \nISBN 978-82-326-7764-1 (printed ver.)  \nISBN 978-82-326-7763-4 (electronic ver.) ISSN 1503-8181 (printed ver.)  \nISSN 2703-8084 (online ver.) Doctoral theses at NTNU, 2024:85 Printed by NTNU Grafisk senter  \nTo my family, and all who have supported me throughout my education.  \nAbstract  \nOptimisation is a valuable tool in process systems engineering, and has been widely used in design, control, process identification and many other areas. This thesis proposes novel applications of optimisation, as well as methods to solve optimisation problems efficiently and reliably. Recurring topics are the use of machine learning to reduce online computational effort, model predictive control, and optimisation under uncertainty. This thesis is a collation of research outputsand is divided in two parts: i) application driven works; ii) theory and algorithm driven works.  \nThe application driven research comprises of three works. The first focuses on training an output-feedback neural network control policy for a distillation column in closed-loop. This is a large problem and is particularly interesting because the control policies can be trained to only use a few measurements along the column. The second work demonstrates a model predictive control formulation for optimal inventory allocation, with the key aspect of the formulation being that we do not require accurate economic modelling or disturbance forecasting. The third work proposes a optimisation formulation for PID tuning in the frequency domain and solves it as a semi-infinite program. This formulation is a natural way to specify controller robustness and noise attenuation.  \nThe theoretical and algorithmic part of the thesis consists of four works. The first two aim to reduce the online computational demand of model predictive control by moving most of the demand offline. In the first of these a convex terminal cost is learned to allow the use of a one-step horizon, whilst in the seconda method is proposed for closed-loop optimisation of neural network control policies under uncertainty. The third study demonstrates how multiple shooting can be used to improve the reliability of training neural networks embedded in differential equations. Lastly, the final work focuses on the development of improved lower bounding algorithms for the global optimisation of nonconvex semi-infinite programs.  \nThe key contributions of this thesis are the works on training neural network control policies in closed loop. Under mild conditions the proposed formulations enables trained policies to approximate model predictive control laws. However, the methodology is not restrictive and permits flexible design of controllers that can handle uncertainty and directly use measurements as feedback in a manner that cannot be done with traditional model predictive control.  \nContents  \nAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v  \nContents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii  \nAcknowledgments ................................... xiii  \n1 Introduction ..................................... 1  \n1.1 Motivation ..........................","cbCaiuj7gbD4wcHU","https://ap.wps.com/l/cbCaiuj7gbD4wcHU","pdf",16050128,4,1,240,"English","en",105,"# Introduction\n## Motivation\n## Thesis overview\n## Publications\n# Background\n## Optimisation preliminaries\n## Model Predictive Control\n## Neural networks\n# Applications\n## Closed-loop training of neural network controllers","[{\"question\":\"What main areas of optimisation does the thesis focus on?\",\"answer\":\"It covers optimisation methods used in process systems engineering, including applications such as design, control, and process identification, and it proposes new optimisation approaches for those settings.\"},{\"question\":\"How does the thesis use machine learning in optimisation problems?\",\"answer\":\"Machine learning is used to reduce online computational effort, including approaches that enable neural-network-based control policies and offline/learned components for model predictive control.\"},{\"question\":\"What are the two parts of the thesis?\",\"answer\":\"The thesis is divided into application-driven works and theory/algorithm-driven works, covering control policy training, inventory allocation, PID tuning, and algorithmic developments for global optimisation.\"}]","Advances in Optimisation and Machine Learning for Process Systems Engineering - 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