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Insights and Challenges for Applying Bipolar Membranes in Advanced Electrochemical Energy Systems.

ABSTRACT: Bipolar membranes (BPMs) are gaining interest in energy conversion technologies. These membranes are composed of cation- and anion-exchange layers, with an interfacial layer in between. This gives the freedom to operate in different conditions (pH, concentration, composition) at both sides. Such membranes are used in two operational modes, forward and reverse bias. BPMs have been implemented in various electrochemical applications, like water and CO₂ electrolyzers, fuel cells, and flow batteries, while BPMs are historically designed for acid/base production. Therefore, current commercial BPMs are not optimized, as the conditions change per application. Although the ideal BPM has highly conductive layers, high water dissociation kinetics, long lifetime, and low ion crossover, each application has its own priorities to be competitive in its field. We describe the challenges and requirements for future BPMs, and identify existing developments that can be leveraged to develop BPMs toward the scale of practical applications.

SUBMITTER: Blommaert MA

PROVIDER: S-EPMC8276271 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionImage recognition technology has immense potential to be applied in industrial energy systems for energy conservation. However, the low recognition accuracy and generalization ability under actual operation conditions limit its commercial application.ObjectivesTo improve the recognition accuracy and generalization ability, a novel image recognition method integrating deep learning and domain knowledge was applied to assist energy saving and emission reduction for industrial energy systems.MethodsAs a typical industrial scenario, the defrosting control in the refrigeration system was selected as the specific optimization object. By combining deep learning algorithm with domain knowledge, a residual-based convolutional neural network model (RCNN) was proposed specifically for frosty state recognition, which features the residual input and average pooling output. Based on the real-time recognition of frosty levels, a defrosting control optimization method was proposed to initiate and terminate the defrosting operation on demand.ResultsBy combining the advanced image recognition technique with specific energy domain knowledge, the proposed RCNN enables both high recognition accuracy and strong generalization ability. The recognition accuracy of RCNN reached 95.06% for the trained objects and 93.67% for non-trained objects while that of only 75.86% for the conventional CNN. By adopting the presented system optimization method assisted by RCNN, the defrosting frequency, accumulated time and energy consumption were 53.8%, 57.02% and 34.5% less than the original control method. Furthermore, the environmental and cost analysis illustrated that the annual reduction in CO2 emissions is 2145.21 to 3412.84 kg and the payback time was less than 2.5 years which was far below the service life.ConclusionThe technical feasibility and significant energy-saving benefits of deep learning-based image recognition method were demonstrated through the field experiment. Our study shows the great application potential of image recognition technology and promotes carbon neutrality in industrial energy systems.

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Insights and Challenges for Applying Bipolar Membranes in Advanced Electrochemical Energy Systems.

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