Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems

• URL: http://arxiv.org/abs/2408.02551v1
• Date: Mon, 5 Aug 2024 15:26:39 GMT
• Title: Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems
• Authors: Markus Grimm, Sébastien Paul, Pierre Chainais,
• Abstract summary: This work proposes a novel approach to optimize the yield of a reaction in a multi-reactor system. It integrates experimental constraints and balances between exploration and exploitation in a sequential batch optimization strategy.
• Score: 2.812898346527047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The optimization of yields in multi-reactor systems, which are advanced tools in heterogeneous catalysis research, presents a significant challenge due to hierarchical technical constraints. To this respect, this work introduces a novel approach called process-constrained batch Bayesian optimization via Thompson sampling (pc-BO-TS) and its generalized hierarchical extension (hpc-BO-TS). This method, tailored for the efficiency demands in multi-reactor systems, integrates experimental constraints and balances between exploration and exploitation in a sequential batch optimization strategy. It offers an improvement over other Bayesian optimization methods. The performance of pc-BO-TS and hpc-BO-TS is validated in synthetic cases as well as in a realistic scenario based on data obtained from high-throughput experiments done on a multi-reactor system available in the REALCAT platform. The proposed methods often outperform other sequential Bayesian optimizations and existing process-constrained batch Bayesian optimization methods. This work proposes a novel approach to optimize the yield of a reaction in a multi-reactor system, marking a significant step forward in digital catalysis and generally in optimization methods for chemical engineering.

Related papers

• Sample-efficient Bayesian Optimisation Using Known Invariances [56.34916328814857]
  We show that vanilla and constrained BO algorithms are inefficient when optimising invariant objectives. We derive a bound on the maximum information gain of these invariant kernels. We use our method to design a current drive system for a nuclear fusion reactor, finding a high-performance solution.
  arXiv  Detail & Related papers  (2024-10-22T12:51:46Z)
• Batched Bayesian optimization with correlated candidate uncertainties [44.38372821900645]
  We propose an acquisition strategy for discrete optimization motivated by pure exploitation, qPO (multipoint of Optimality) We apply our method to the model-guided exploration of large chemical libraries and provide empirical evidence that it performs better than or on par with state-of-the-art methods in batched Bayesian optimization.
  arXiv  Detail & Related papers  (2024-10-08T20:13:12Z)
• Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization [50.38518771642365]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form. This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method. A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
  arXiv  Detail & Related papers  (2023-12-28T23:15:18Z)
• Differentiable Multi-Target Causal Bayesian Experimental Design [43.76697029708785]
  We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting. Existing methods rely on greedy approximations to construct a batch of experiments. We propose a conceptually simple end-to-end gradient-based optimization procedure to acquire a set of optimal intervention target-state pairs.
  arXiv  Detail & Related papers  (2023-02-21T11:32:59Z)
• An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization [78.36413169647408]
  We study the effectiveness of various ZO optimization methods for optimizing molecular objectives. We show the advantages of ZO sign-based gradient descent (ZO-signGD) We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
  arXiv  Detail & Related papers  (2022-10-27T01:58:10Z)
• New Paradigms for Exploiting Parallel Experiments in Bayesian Optimization [0.0]
  We present new parallel BO paradigms that exploit the structure of the system to partition the design space. Specifically, we propose an approach that partitions the design space by following the level sets of the performance function. Our results show that our approaches significantly reduce the required search time and increase the probability of finding a global (rather than local) solution.
  arXiv  Detail & Related papers  (2022-10-03T16:45:23Z)
• Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm [62.997667081978825]
  We propose a computationally efficient and powerful Bayesian approach for sparse high-dimensional linear regression. Minimal prior assumptions on the parameters are used through the use of plug-in empirical Bayes estimates. The proposed approach is implemented in the R package probe.
  arXiv  Detail & Related papers  (2022-09-16T19:15:50Z)
• Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning [5.900286890213338]
  The paper proposes a multi-agent reinforcement learning (MARL) framework to transform the optimization process into a game. A utilitarian selection mechanism was employed in the game to avoid the interruption of multiple equilibriumlibria. The proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches.
  arXiv  Detail & Related papers  (2020-12-02T11:37:44Z)
• Bilevel Optimization: Convergence Analysis and Enhanced Design [63.64636047748605]
  Bilevel optimization is a tool for many machine learning problems. We propose a novel stoc-efficientgradient estimator named stoc-BiO.
  arXiv  Detail & Related papers  (2020-10-15T18:09:48Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Simple and Scalable Parallelized Bayesian Optimization [2.512827436728378]
  We propose a simple and scalable BO method for asynchronous parallel settings. Experiments are carried out with a benchmark function and hyperparameter optimization of multi-layer perceptrons.
  arXiv  Detail & Related papers  (2020-06-24T10:25:27Z)

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.