# Accelerating process control and optimization via machine learning: A review

arXiv: https://arxiv.org/abs/2412.18529 · HTML: https://mlsanity.com/paper/2412.18529/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 93 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 27 of 27)

- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions](https://mlsanity.com/paper/1710.04806/) (2017)
- [End-to-End Constrained Optimization Learning: A Survey](https://mlsanity.com/paper/2103.16378/) (2021)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [ParamILS: An Automatic Algorithm Configuration Framework](https://mlsanity.com/paper/1401.3492/) (2014)
- [Self-Supervised Primal-Dual Learning for Constrained Optimization](https://mlsanity.com/paper/2208.09046/) (2022)
- [Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions](https://mlsanity.com/paper/2209.15565/) (2022)
- [Online Mixed-Integer Optimization in Milliseconds](https://mlsanity.com/paper/1907.02206/) (2019)
- [Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets](https://mlsanity.com/paper/1802.09639/) (2018)
- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [Diagnosing Infeasible Optimization Problems Using Large Language Models](https://mlsanity.com/paper/2308.12923/) (2023)
- [Pitfalls and Best Practices in Algorithm Configuration](https://mlsanity.com/paper/1705.06058/) (2017)
- [Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories](https://mlsanity.com/paper/2205.14345/) (2022)
- [Theoretical challenges towards cutting-plane selection](https://mlsanity.com/paper/1805.02782/) (2018)
- [Branch-and-Bound Performance Estimation Programming: A Unified Methodology for Constructing Optimal Optimization Methods](https://mlsanity.com/paper/2203.07305/) (2022)
- [DASH: Dynamic Approach for Switching Heuristics](https://mlsanity.com/paper/1307.4689/) (2013)
- [Benders Cut Classification via Support Vector Machines for Solving Two-stage Stochastic Programs](https://mlsanity.com/paper/1906.05994/) (2019)
- [Learning for Spatial Branching: An Algorithm Selection Approach](https://mlsanity.com/paper/2204.10834/) (2022)
- [Computationally efficient solution of mixed integer model predictive control problems via machine learning aided Benders Decomposition](https://mlsanity.com/paper/2309.16508/) (2023)
- [Optimal Deterministic Algorithm Generation](https://mlsanity.com/paper/1609.06917/) (2016)
- [Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize](https://mlsanity.com/paper/2310.07082/) (2023)
- [A Graph-Based Modeling Abstraction for Optimization: Concepts and Implementation in Plasmo.jl](https://mlsanity.com/paper/2006.05378/) (2020)
- [Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose](https://mlsanity.com/paper/2310.07068/) (2023)
- [Neural Networks for Predicting Algorithm Runtime Distributions](https://mlsanity.com/paper/1709.07615/) (2017)
- [Graph-Based Modeling and Decomposition of Energy Infrastructures](https://mlsanity.com/paper/2010.02404/) (2020)
- [A Machine Learning-Enhanced Benders Decomposition Approach to Solve the Transmission Expansion Planning Problem under Uncertainty](https://mlsanity.com/paper/2304.07534/) (2023)
- [Deep learning enhanced mixed integer optimization: Learning to reduce model dimensionality](https://mlsanity.com/paper/2401.09556/) (2024)

## Built on by (most cited first, 5 of 5)

- [Learning to control inexact Benders decomposition via reinforcement learning](https://mlsanity.com/paper/2508.06700/) (2025)
- [Graph-Based Imitation and Reinforcement Learning for Efficient Benders Decomposition](https://mlsanity.com/paper/2511.11870/) (2025)
- [Feasibility-Aware Imitation Learning for Benders Decomposition](https://mlsanity.com/paper/2604.04801/) (2026)
- [Convex Hybrid Modeling: An Operator-Based Approach](https://mlsanity.com/paper/2605.23151/) (2026)
- [Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering](https://mlsanity.com/paper/2610.02014/) (2026)

## Abstract (arXiv)

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.