Author(s)

Shu Ma ¹, Chunhua Feng ¹

Affiliation(s)

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

ABSTRACT

With the continuous advancement of aerospace and deep-sea technologies, the safety of enclosed spaces has increasingly garnered attention, particularly concerning the control of carbon dioxide (CO₂) concentrations. However, there are challenges with existing CO₂ control methods. For instance, the adsorption efficiency cannot be measured when utilizing Lithium Hydroxide for absorption. To address this challenge, this paper presents a new model to quantify LiOH AC. This study integrates Long Short-Term Memory (LSTM) networks with a self-attention mechanism, refined utilizing Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for optimization. The results indicate that the supposed model surpasses traditional LSTM model leading to improved predictive precision and enhanced overall performance in the prediction of LiOH AC.

KEYWORDS

CITE THIS PAPER

Shu Ma, Chunhua Feng, Prediction of Adsorption Efficiency of Lithium Hydroxide Based on an Enhanced NSGAII-LSTM Model. Advances in Computer, Signals and Systems (2024) Vol. 8: 80-85. DOI: http://dx.doi.org/10.23977/acss.2024.080612.

REFERENCES

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[2] Cho Y, Lee J, Bokare A, Kwon S, Park D, Jung W. (2015) LiOH-embedded zeolite for carbon dioxide capture under ambient conditions. Journal of Industrial and Engineering Chemistry, 22, 350356. 
[3] Sikakollu. P, Singh. U, Kumar. A, Tikkiwal. V. (2021) Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach. Chaos, Solitons & Fractals, 142, 110336. 
[4] Zhou. Z, Qiu. F. Zheng. F, Ye. M. (2022) Neural network joint capacity-power control strategy based on nsgaII-bp for interference suppression in LEO satellite uplinks. Computers and Electrical Engineering, 102, 108093.

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