Machine Learning-Based Operational Modeling of an Electrochemical Reactor: Handling Data Variability and Improving Empirical Models

Article Engineering, Environmental

Deep learning based dynamic behavior modelling and prediction of particulate matter in air

Ravi Kiran Inapakurthi et al.

Summary: The study proposes a method utilizing neural networks to capture the dynamic trends of environmental parameters, achieving a modeling accuracy of 98.97% through balancing accuracy and complexity with an evolutionary algorithm.

CHEMICAL ENGINEERING JOURNAL (2021)

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Article Engineering, Chemical

Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage

Daniel Rangel-Martinez et al.

Summary: This study provides an overview of the current use of machine learning in manufacturing sectors, focusing on renewable energies, smart grids, and energy management. Artificial neural networks are the preferred technique, with a growing demand for ML in the energy sector expected in the future.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2021)

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Article Computer Science, Interdisciplinary Applications

Deep reinforcement learning control of hydraulic fracturing

Mohammed Saad Faizan Bangi et al.

Summary: This article introduces a data-based reinforcement learning controller that learns an optimal control policy through interactions with the process to achieve the goal of obtaining a uniform proppant concentration along the fracture.

COMPUTERS & CHEMICAL ENGINEERING (2021)

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Article Engineering, Chemical

Machine learning-based predictive control using noisy data: evaluating performance and robustness via a large-scale process simulator

Zhe Wu et al.

Summary: This study introduces a method using dropout and co-teaching learning algorithm to develop LSTM neural networks for capturing the true process dynamics from noisy data. The performance and robustness of the modeling approaches are evaluated on an industrial chemical reactor example using data generated from a large-scale process simulator.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2021)

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Article Energy & Fuels

A Data-Driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts

Ali Malek et al.

Summary: In the search for next-generation electrocatalyst materials for electrochemical energy technologies, finding more active, selective, and stable materials for the CO2 reduction reaction is challenging and time-consuming. A material recommendation and screening framework has been introduced to recommend viable materials and reveal relevant correlations governing catalyst performance under different temperature conditions.

FRONTIERS IN ENERGY RESEARCH (2021)

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Article Nanoscience & Nanotechnology

Guiding the Catalytic Properties of Copper for Electrochemical CO2 Reduction by Metal Atom Decoration

Yusaku F. Nishimura et al.

Summary: The study investigates the impact of atomic-scale bimetallic effects on the electrochemical CO2 reduction performance of Cu-based catalysts. Utilizing a systematic approach that unifies protocols for materials synthesis and testing, accurate comparisons of intrinsic catalytic activity and selectivity were made. The results show that metal atoms segregate to under-coordinated Cu sites during physical vapor deposition, affecting the formation of oxygenates and hydrocarbons during CO reduction.

ACS APPLIED MATERIALS & INTERFACES (2021)

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Article Computer Science, Interdisciplinary Applications