March 26, 2024Environmental Technology & InnovationOpen Access

Prediction and optimization of the efficiency and energy consumption of an ammonia vacuum thermal stripping process using experiments and machine learning models

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YLYoun-Jun LeeHanyang University JKJin-Kyu KangGyeongsang National University SJSung-Hyo JungAjou University

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Implication

Experimental modeling study reveals optimal operating conditions for ammonia removal from digestate, highlighting pathways to minimize stripping energy consumption.

Key Points

Ammonia vacuum thermal stripping achieves minimal energy consumption at 40 °C and pH 11.5, even though overall stripping efficiency continues to rise at higher levels.
With R2 values exceeding 0.98, random forest and extreme gradient boosting models reliably predicted vacuum stripping efficiency across diverse operational conditions.
Experimental validation supports energy-efficient ammonia removal from anaerobic digestion effluent, demonstrating close agreement between predicted and observed runtimes.

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Lee et al. (2024) studied this question.

synapsesocial.com/papers/68e72435b6db64358769e00a https://doi.org/10.1016/j.eti.2024.103610

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Implication

Experimental modeling study reveals optimal operating conditions for ammonia removal from digestate, highlighting pathways to minimize stripping energy consumption.

Prediction and optimization of the efficiency and energy consumption of an ammonia vacuum thermal stripping process using experiments and machine learning models

Discussion

Member takes

Implication

Key Points

Cite This Study

Prediction and optimization of the efficiency and energy consumption of an ammonia vacuum thermal stripping process using experiments and machine learning models

Discussion

Member takes

Implication

Key Points

Cite This Study

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