Max Mowbray

Max Mowbray
The University of Manchester · Centre for Process Integration

Doctor of Engineering

About

26
Publications
2,578
Reads
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145
Citations
Citations since 2017
26 Research Items
145 Citations
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2017201820192020202120222023020406080100
2017201820192020202120222023020406080100
Introduction
My current research interests lie in the translation and development of machine learning approaches, particularly reinforcement learning, for (bio)process systems engineering.
Additional affiliations
September 2019 - present
The University of Manchester
Position
  • PhD Student
Education
September 2015 - June 2019
University of Birmingham
Field of study
  • Chemical Engineering

Publications

Publications (26)
Article
Reinforcement Learning (RL) is an effective method to solve stochastic sequential decision-making problems. This is a problem description common to supply chain operations, however, most RL algorithms are tailored for game-based benchmarks. Here, we propose a deep RL method tailored for supply chain problems. The proposed algorithm deploys a deriva...
Article
Constructing predictive models to simulate complex bioprocess dynamics, particularly time-varying (i.e. parameters varying over time) and history-dependent (i.e. current kinetics dependent on historical culture conditions) behaviour, has been a longstanding research challenge. Current advances in hybrid modelling offer a solution to this by integra...
Article
Full-text available
Viscosity represents a key product quality indicator but has been difficult to measure in-process in real-time. This is particularly true if the process involves complex mixing phenomena operated at dynamic conditions. To address this challenge, in this study, we developed an innovative soft sensor by integrating advanced artificial neural networks...
Article
The major promise of the 4th industrial revolution is encompassed by the utilization of real-time process data to inform operational decision-making. Research focus relating to the monitoring of batch process end product qualities has typically been dominated by the use of latent variable models. In this work, we combined latent variable modeling w...
Preprint
Full-text available
Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. Recent works have investigated the application of RL to identify optimal scheduling decision in the presence of uncertainty. In this work, we present a RL methodology to address precedence and disjunctive constraints...
Article
Full-text available
In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to start with examples that are irrelevant to process engineers (e.g. classification of images between cats and dogs, house pricing, types of flowers, etc.). However, process engineering principles are also based on pseudo-empirical correlations and heuristi...
Chapter
Over the last decade, Reinforcement Learning (RL) has received significant attention as it promises novel and efficient solutions to complex control problems. This work builds on model-free RL, namely Q-learning, to determine optimal control policies for nonlinear, complex biochemical processes. We propose convex functions instead of deep neural ne...
Chapter
The field of Reinforcement Learning (RL) has received a lot of attention for decision-making under uncertainty. Lately, much of this focus has been on the application of RL for combinatorial optimisation. Recent work has showcased the use of RL on a single-stage continuous chemical production scheduling problem. This work highlighted the potential...
Chapter
Reinforcement Learning (RL) has generated excitement within the process industries within the context of decision making under uncertainty. The primary benefit of RL is that it provides a flexible and general approach to handling systems subject to both exogenous and endogenous uncertainties. Despite this there has been little reported uptake of RL...
Chapter
Statistical machine learning algorithms have been widely used to analyse industrial data for batch process monitoring and control. In this study, we develop a three-step methodology to identify, visualize and systematically reduce data dimensionality for the construction of robust soft-sensors for end-product quality prediction. The approach first...
Chapter
Reinforcement Learning (RL) has received interest within the context of decision making under uncertainty in the process industries. The primary benefit of RL arises from the formulation of the control problem as a Markov decision process (MDP), meaning that it inherits the benefits of accounting for uncertainty in a closed loop feedback control fr...
Chapter
Viscosity represents a key indicator of product quality but has traditionally been difficult to measure in-process in real-time. This is particularly true if the process involves complex mixing phenomena operated at dynamic conditions. To address this challenge, a promising solution to monitoring product viscosity is to design soft-sensors which co...
Article
Reinforcement learning (RL) is a control approach that can handle nonlinear stochastic optimal control problems. However, despite the promise exhibited, RL has yet to see marked translation to industrial practice primarily due to its inability to satisfy state constraints. In this work we aim to address this challenge. We propose an “oracle”-assist...
Article
Statistical machine learning algorithms have been widely used to analyse industrial data for batch process monitoring and control. In this study, we aimed to take a two-step approach to systematically reduce data dimensionality and to design soft-sensors for product quality prediction. The approach first employs partial least squares to screen the...
Article
To create efficient-high performing processes, one must find an optimal design with its corresponding controller that ensures optimal operation in the presence of uncertainty. When comparing different process designs, for the comparison to be meaningful, each design must involve its optimal operation. Therefore, to optimize a process’ design, one m...
Preprint
Full-text available
To create efficient-high performing processes, one must find an optimal design with its corresponding controller that ensures optimal operation in the presence of uncertainty. When comparing different process designs, for the comparison to be meaningful, each design must involve its optimal operation. Therefore, to optimize a process' design, one m...
Article
Full-text available
Reinforcement learning (RL) is a data‐driven approach to synthesising an optimal control policy. A barrier to wide implementation of RL‐based controllers is its data‐hungry nature during online training and its inability to extract useful information from human operator and historical process operation data. Here, we present a two‐step framework to...
Article
Full-text available
The field of machine learning is comprised of techniques, which have proven powerful approaches to knowledge discovery and construction of ‘digital twins’ in the highly dimensional, nonlinear and stochastic domains common to biochemical engineering. We review the use of machine learning within biochemical engineering within the last 20 years. The m...
Preprint
Full-text available
Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL controllers relative to existing methods is their ability to optimize uncertain systems independently of explicit assumption of process uncertainty. Recent focus on engineering applications has been directed towards the develo...
Article
Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL controllers relative to existing methods is their ability to optimize uncertain systems independently of explicit assumption of process uncertainty. Recent focus on engineering applications has been directed towards the develo...
Article
Full-text available
Traumatic brain injury is a leading cause of mortality worldwide, often affecting individuals at their most economically active yet no primary disease-modifying interventions exist for their treatment. Real-time direct spectroscopic examination of the brain tissue within the context of traumatic brain injury has the potential to improve the underst...
Chapter
The development of advanced process control schemes is continuing driver of research within process systems engineering. In this work, we propose a framework, which leverages existing process data to automatically learn and update a control policy. This framework is underpinned by machine learning methods, namely, apprenticeship (AL) and reinforcem...
Article
Chemical process optimization and control often require satisfaction of constraints for safe operation. Reinforcement learning (RL) has been shown to be a powerful control technique that can handle nonlinear stochastic optimal control problems. Despite this promise, RL has yet to see significant translation to industrial practice due to its inabili...
Article
Model-free reinforcement learning has been recently investigated for use in chemical process control. Through the iterative creation of an approximate process model, control actions are able to be explored and optimal policies generated. Typically, this approximate process model has taken the form of a neural network that is continuously updated. H...
Preprint
Full-text available
Reinforcement learning (RL) is a control approach that can handle nonlinear stochastic optimal control problems. However, despite the promise exhibited, RL has yet to see marked translation to industrial practice primarily due to its inability to satisfy state constraints. In this work we aim to address this challenge. We propose an 'oracle'-assist...

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Projects

Project (1)
Project
Develop reinforcement learning algorithms that are applicable to the area of process systems engineering and process control.