Accelerating process control and optimization via machine learning: A review

• URL: http://arxiv.org/abs/2412.18529v1
• Date: Tue, 24 Dec 2024 16:24:29 GMT
• Title: Accelerating process control and optimization via machine learning: A review
• Authors: Ilias Mitrai, Prodromos Daoutidis,
• Abstract summary: We discuss recent advances in the representation of decision-making problems for machine learning tasks. We discuss open problems related to the application of machine learning for accelerating process optimization and control.
• Score: 0.0
• License:
• Abstract: Process control and optimization have been widely used to solve decision-making problems in chemical engineering applications. However, identifying and tuning the best solution algorithm is challenging and time-consuming. Machine learning tools can be used to automate these steps by learning the behavior of a numerical solver from data. In this paper, we discuss recent advances in (i) the representation of decision-making problems for machine learning tasks, (ii) algorithm selection, and (iii) algorithm configuration for monolithic and decomposition-based algorithms. Finally, we discuss open problems related to the application of machine learning for accelerating process optimization and control.

Related papers

• Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming [11.293025183996832]
  Mixed Linear Programming (MILP) offers a powerful modeling language for a wide range of applications. In recent years, there has been an explosive development in the use of machine learning algorithms for enhancing all main tasks involved in the branch-and-bound algorithm. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency.
  arXiv  Detail & Related papers  (2024-02-08T09:19:26Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize [0.0]
  The proposed approach can lead to a significant reduction in solution time. Active and supervised learning is used to learn a surrogate model that predicts the computational performance. The results show that the proposed approach can lead to a significant reduction in solution time.
  arXiv  Detail & Related papers  (2023-10-10T23:49:26Z)
• A Machine Learning Approach to Two-Stage Adaptive Robust Optimization [6.943816076962257]
  We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization problems. We encode the optimal here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the optimal wait-and-see decisions. We train a machine learning model that predicts high-quality strategies for the here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the wait-and-see decisions.
  arXiv  Detail & Related papers  (2023-07-23T19:23:06Z)
• Mechanic: A Learning Rate Tuner [52.4242550204696]
  We introduce a technique for tuning the learning rate scale factor of any base optimization algorithm and schedule automatically, which we call textscmechanic. We rigorously evaluate textscmechanic on a range of large scale deep learning tasks with varying batch sizes, schedules, and base optimization algorithms.
  arXiv  Detail & Related papers  (2023-05-31T19:32:43Z)
• A study on a Q-Learning algorithm application to a manufacturing assembly problem [0.8937905773981699]
  This study focuses on the implementation of a reinforcement learning algorithm in an assembly problem of a given object. A model-free Q-Learning algorithm is applied, considering the learning of a matrix of Q-values (Q-table) from the successive interactions with the environment. The optimisation approach achieved very promising results by learning the optimal assembly sequence 98.3% of the times.
  arXiv  Detail & Related papers  (2023-04-17T15:38:34Z)
• Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics [89.24951036534168]
  Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model.
  arXiv  Detail & Related papers  (2022-10-23T22:21:10Z)
• Algorithm Selection on a Meta Level [58.720142291102135]
  We introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework.
  arXiv  Detail & Related papers  (2021-07-20T11:23:21Z)
• A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems [54.61091936472494]
  We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
  arXiv  Detail & Related papers  (2021-03-10T03:16:12Z)
• Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection [0.9281671380673306]
  A key challenge in the application of evolutionary algorithms is the selection of an algorithm instance that best suits the problem at hand. We analyze in this work how such prior performance data can be used to infer informed dynamic algorithm selection schemes for the solution of pseudo-Boolean optimization problems.
  arXiv  Detail & Related papers  (2021-02-12T12:27:02Z)
• AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes [56.65379135797867]
  We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
  arXiv  Detail & Related papers  (2020-08-29T14:57:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.