Ehecatl Antonio Del Rio Chanona

Tom Savage, Nausheen Basha, Jonathan McDonough et al.

Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterisations of reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. To address this challenge, we establish a machine learning-assisted approach for the design of the next-generation of chemical reactors, combining the application of high-dimensional parameterisations, computational fluid dynamics, and multi-fidelity Bayesian optimisation. We associate the development of mixing-enhancing vortical flow structures in novel coiled reactors with performance, and use our approach to identify key characteristics of optimal designs. By appealing to the principles of flow dynamics, we rationalise the selection of novel design features that lead to experimental plug flow performance improvements of 60% over conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with `augmented-intelligence' approaches can lead to superior design performance and, consequently, emissions-reduction and sustainability.

Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford et al.

Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossil-based materials. However, they are generally difficult to optimize due to their unsteady-state operation modes and stochastic behaviours. Furthermore, biological systems are highly complex, therefore plant-model mismatch is often present. To address the aforementioned challenges we propose a Reinforcement learning based optimization strategy for batch processes. In this work, we applied the Policy Gradient method from batch-to-batch to update a control policy parametrized by a recurrent neural network. We assume that a preliminary process model is available, which is exploited to obtain a preliminary optimal control policy. Subsequently, this policy is updatedbased on measurements from thetrueplant. The capabilities of our proposed approach were tested on three case studies (one of which is nonsmooth) using a more complex process model for thetruesystemembedded with adequate process disturbance. Lastly, we discussed the advantages and disadvantages of this strategy compared against current existing approaches such as nonlinear model predictive control.

Cesare Caputo, Michel-Alexandre Cardin, Pudong Ge et al.

Ongoing risks from climate change have impacted the livelihood of global nomadic communities, and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced Plug and Play control strategies have been recently developed with such a decentralized framework in mind, more easily allowing for the interconnection of nomadic communities, both to each other and to the main grid. In light of the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) is implemented for the design and planning problem tackled. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important implications for both nomadic communities and policymakers focused on enabling their energy access.

Max Mowbray, Dongda Zhang, Ehecatl Antonio Del Rio Chanona

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 tailored to efficiently address production scheduling problems in the presence of uncertainty. We consider commonly imposed restrictions on these problems such as precedence and disjunctive constraints which are not naturally considered by RL in other contexts. Additionally, this work naturally enables the optimization of risk-sensitive formulations such as the conditional value-at-risk (CVaR), which are essential in realistic scheduling processes. The proposed strategy is investigated thoroughly in a parallel batch production environment, and benchmarked against mixed integer linear programming (MILP) strategies. We show that the policy identified by our approach is able to account for plant uncertainties in online decision-making, with expected performance comparable to existing MILP methods. Additionally, the framework gains the benefits of optimizing for risk-sensitive measures, and identifies online decisions orders of magnitude faster than the most efficient optimization approaches. This promises to mitigate practical issues and ease in handling realizations of process uncertainty in the paradigm of online production scheduling.

Maximilian Bloor, Martha White, Ehecatl Antonio del Rio Chanona et al.

Electrified chemical processes are incentivized by exposure to time-varying electricity markets to operate flexibly, but participating in demand response schemes can require satisfying terminal constraints over long horizons. Specifically, terminal constraints may be required when computing optimal schedules in order to preserve dynamic stability. Model-based optimization methods are computationally costly, and data-driven scheduling via reinforcement learning (RL) faces severe credit-assignment challenges. We integrate Goal-Space Planning (GSP) with Deep Deterministic Policy Gradient (DDPG), using learned temporally abstract models over discrete subgoals to propagate value across extended horizons. Using a simulated air separation benchmark, we demonstrate the proposed approach improves sample efficiency over standard DDPG while satisfying terminal storage constraints, mitigating myopic control behavior.

Tom Savage, Ehecatl Antonio del Rio Chanona

Bayesian optimization has been successfully applied throughout Chemical Engineering for the optimization of functions that are expensive-to-evaluate, or where gradients are not easily obtainable. However, domain experts often possess valuable physical insights that are overlooked in fully automated decision-making approaches, necessitating the inclusion of human input. In this article we re-introduce the human back into the data-driven decision making loop by outlining an approach for collaborative Bayesian optimization. Our methodology exploits the hypothesis that humans are more efficient at making discrete choices rather than continuous ones and enables experts to influence critical early decisions. We apply high-throughput (batch) Bayesian optimization alongside discrete decision theory to enable domain experts to influence the selection of experiments. At every iteration we apply a multi-objective approach that results in a set of alternate solutions that have both high utility and are reasonably distinct. The expert then selects the desired solution for evaluation from this set, allowing for the inclusion of expert knowledge and improving accountability, whilst maintaining the advantages of Bayesian optimization. We demonstrate our approach across a number of applied and numerical case studies including bioprocess optimization and reactor geometry design, demonstrating that even in the case of an uninformed practitioner our algorithm recovers the regret of standard Bayesian optimization. Through the inclusion of continuous expert opinion, our approach enables faster convergence, and improved accountability for Bayesian optimization in engineering systems.

Tom Savage, Ehecatl Antonio del Rio Chanona

Domain experts often possess valuable physical insights that are overlooked in fully automated decision-making processes such as Bayesian optimisation. In this article we apply high-throughput (batch) Bayesian optimisation alongside anthropological decision theory to enable domain experts to influence the selection of optimal experiments. Our methodology exploits the hypothesis that humans are better at making discrete choices than continuous ones and enables experts to influence critical early decisions. At each iteration we solve an augmented multi-objective optimisation problem across a number of alternate solutions, maximising both the sum of their utility function values and the determinant of their covariance matrix, equivalent to their total variability. By taking the solution at the knee point of the Pareto front, we return a set of alternate solutions at each iteration that have both high utility values and are reasonably distinct, from which the expert selects one for evaluation. We demonstrate that even in the case of an uninformed practitioner, our algorithm recovers the regret of standard Bayesian optimisation.

Juan D. Gil, Ehecatl Antonio Del Rio Chanona, José L. Guzmán et al.

The inherent complexity of living cells as production units creates major challenges for maintaining stable and optimal bioprocess conditions, especially in open Photobioreactors (PBRs) exposed to fluctuating environments. To address this, we propose a Reinforcement Learning (RL) control approach, combined with Behavior Cloning (BC), for pH regulation in open PBR systems. This represents, to the best of our knowledge, the first application of an RL-based control strategy to such a nonlinear and disturbance-prone bioprocess. Our method begins with an offline training stage in which the RL agent learns from trajectories generated by a nominal Proportional-Integral-Derivative (PID) controller, without direct interaction with the real system. This is followed by a daily online fine-tuning phase, enabling adaptation to evolving process dynamics and stronger rejection of fast, transient disturbances. This hybrid offline-online strategy allows deployment of an adaptive control policy capable of handling the inherent nonlinearities and external perturbations in open PBRs. Simulation studies highlight the advantages of our method: the Integral of Absolute Error (IAE) was reduced by 8% compared to PID control and by 5% relative to standard off-policy RL. Moreover, control effort decreased substantially-by 54% compared to PID and 7% compared to standard RL-an important factor for minimizing operational costs. Finally, an 8-day experimental validation under varying environmental conditions confirmed the robustness and reliability of the proposed approach. Overall, this work demonstrates the potential of RL-based methods for bioprocess control and paves the way for their broader application to other nonlinear, disturbance-prone systems.

Maximilian Bloor, Max Mowbray, Ehecatl Antonio Del Rio Chanona et al.

Sequential decision making under uncertainty is central to many Process Systems Engineering (PSE) challenges, where traditional methods often face limitations related to controlling and optimizing complex and stochastic systems. Reinforcement Learning (RL) offers a data-driven approach to derive control policies for such challenges. This paper presents a survey and tutorial on RL methods, tailored for the PSE community. We deliver a tutorial on RL, covering fundamental concepts and key algorithmic families including value-based, policy-based and actor-critic methods. Subsequently, we survey existing applications of these RL techniques across various PSE domains, such as in fed-batch and continuous process control, process optimization, and supply chains. We conclude with PSE focused discussion of specialized techniques and emerging directions. By synthesizing the current state of RL algorithm development and implications for PSE this work identifies successes, challenges, trends, and outlines avenues for future research at the interface of these fields.

Niki Kotecha, Ehecatl Antonio del Rio Chanona

In supply chain management, decision-making often involves balancing multiple conflicting objectives, such as cost reduction, service level improvement, and environmental sustainability. Traditional multi-objective optimization methods, such as linear programming and evolutionary algorithms, struggle to adapt in real-time to the dynamic nature of supply chains. In this paper, we propose an approach that combines Reinforcement Learning (RL) and Multi-Objective Evolutionary Algorithms (MOEAs) to address these challenges for dynamic multi-objective optimization under uncertainty. Our method leverages MOEAs to search the parameter space of policy neural networks, generating a Pareto front of policies. This provides decision-makers with a diverse population of policies that can be dynamically switched based on the current system objectives, ensuring flexibility and adaptability in real-time decision-making. We also introduce Conditional Value-at-Risk (CVaR) to incorporate risk-sensitive decision-making, enhancing resilience in uncertain environments. We demonstrate the effectiveness of our approach through case studies, showcasing its ability to respond to supply chain dynamics and outperforming state-of-the-art methods in an inventory management case study. The proposed strategy not only improves decision-making efficiency but also offers a more robust framework for managing uncertainty and optimizing performance in supply chains.