Machine Learning in Chemical Engineering: A Perspective

Schweidtmann, Artur M.; Esche, Erik; Fischer, Asja; Kloft, Marius; Repke, J.‐U.; Säger, Sebastian; Mitsos, Alexander

doi:10.1002/cite.202100083

Cited by 95 publications

(60 citation statements)

References 126 publications

(164 reference statements)

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“…Initial learning rate On the other hand, it is necessary to ensure that the model used is suitable for what is desired. In this sense, several works in the literature have pointed to the deep neural network as the most suitable machine learning solution to model complex dynamic systems [10,13,29]. For instance, Rebello et al, 2022 [13] and Oliveira et al, 2020 [10] compared several machine learning approaches, concluding that deep learning was able to better describe the dynamics of a pressure swing adsorption unit.…”

Section: Hyperspace Resultsmentioning

confidence: 99%

“…For instance, Rebello et al, 2022 [13] and Oliveira et al, 2020 [10] compared several machine learning approaches, concluding that deep learning was able to better describe the dynamics of a pressure swing adsorption unit. Schweidtmann et al, 2021 [29] points out that among ML techniques-such as random forests, support vector machines, spline functions, among others-deep learning is the most suitable for learning complex dependencies.…”

Section: Hyperspace Resultsmentioning

confidence: 99%

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Mapping Uncertainties of Soft-Sensors Based on Deep Feedforward Neural Networks through a Novel Monte Carlo Uncertainties Training Process

et al. 2022

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Data-driven sensors are techniques capable of providing real-time information of unmeasured variables based on instrument measurements. They are valuable tools in several engineering fields, from car automation to chemical processes. However, they are subject to several sources of uncertainty, and in this way, they need to be able to deal with uncertainties. A way to deal with this problem is by using soft sensors and evaluating their uncertainties. On the other hand, the advent of deep learning (DL) has been providing a powerful tool for the field of data-driven modeling. The DL presents a potential to improve the soft sensor reliability. However, the uncertainty identification of the soft sensors model is a known issue in the literature. In this scenario, this work presents a strategy to identify the uncertainty of DL models prediction based on a novel Monte Carlo uncertainties training strategy. The proposed methodology is applied to identify a Soft Sensor to provide a real-time prediction of the productivity of a chemical process. The results demonstrate that the proposed methodology can yield a soft sensor based on DL that provides reliable predictions, with precision being proven by its corresponding coverage region.

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Section: Hyperspace Resultsmentioning

confidence: 99%

Section: Hyperspace Resultsmentioning

confidence: 99%

Mapping Uncertainties of Soft-Sensors Based on Deep Feedforward Neural Networks through a Novel Monte Carlo Uncertainties Training Process

et al. 2022

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“…Generally, the optimization of RO systems requires modeling the mass transfer of the employed membrane modules [21], which may result in a complex mathematical model if first order principles are employed [22][23][24]; often a computationally expensive task [25]. Data-driven surrogate models can be utilized to capture the complex mass transfer behavior of membrane systems [26], utilizing machine learning (ML) methods [27]. ML can be classified into unsupervised and supervised learning, the difference between the two being that the former is used to analyze data with no apparent input-output connection, whereas the latter is used to obtain functions mapping an explicit input-output structure within the data [28].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants

2022

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An ever-growing population together with globally depleting water resources pose immense stresses for water supply systems. Desalination technologies can reduce these stresses by generating fresh water from saline water sources. Reverse osmosis (RO), as the industry leading desalination technology, typically involves a complex network of membrane modules that separate unwanted particles from water. The optimal design and operation of these complex RO systems can be computationally expensive. In this work, we present a modeling and optimization strategy for addressing the optimal operation of an industrial-scale RO plant. We employ a feed-forward artificial neural network (ANN) surrogate modeling representation with rectified linear units as activation functions to capture the membrane behavior accurately. Several ANN set-ups and surrogate models are presented and evaluated, based on collected data from the H2Oaks RO desalination plant in South-Central Texas. The developed ANN is then transformed into a mixed-integer linear programming formulation for the purpose of minimizing energy consumption while maximizing water utilization. Trade-offs between the two competing objectives are visualized in a Pareto front, where indirect savings can be uncovered by comparing energy consumption for an array of water recoveries and feed flows.

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“…Simultaneously, innovations are pushed by new possibilities due to emerging digital technologies. Digitization and in particular artificial intelligence (AI) offer new possibilities for process design and therefore have the potential contribute to the transformation of chemical engineering 1,3,5 .…”

Section: Introductionmentioning

confidence: 99%

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks

Stops¹,

Roel²,

Gao³

et al. 2022

Preprint

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Process synthesis experiences a disruptive transformation accelerated by digitization and artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from process graphs. In particular, the graph neural networks are implemented within the agent architecture to process the states and make decisions. Moreover, we implement a hierarchical and hybrid decision-making process to generate flowsheets, where unit operations are placed iteratively as discrete decisions and corresponding design variables are selected as continuous decisions. We demonstrate the potential of our method to design economically viable flowsheets in an illustrative case study comprising equilibrium reactions, azeotropic separation, and recycles. The results show quick learning in discrete, continuous, and hybrid action spaces. Due to the flexible architecture of the proposed reinforcement learning agent, the method is predestined to include large action-state spaces and an interface to process simulators in future research.

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Machine Learning in Chemical Engineering: A Perspective

Cited by 95 publications

References 126 publications

Mapping Uncertainties of Soft-Sensors Based on Deep Feedforward Neural Networks through a Novel Monte Carlo Uncertainties Training Process

Mapping Uncertainties of Soft-Sensors Based on Deep Feedforward Neural Networks through a Novel Monte Carlo Uncertainties Training Process

A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks

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