Citation

Mishuk, Mahadi Hasan and Mohamad, Norhidayah and Ab. Aziz, Nor Azlina and Ghazali, Anith Khairunisa (2026) Swarm Intelligence vs. ML: A Comparative Review of Modeling Approaches for Ammonia Emissions from Urea Fertilizer Usage and Manufacturing. In: 16th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2026, 25 April 2026 - 26 April 2026, Hybrid, Penang.

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Abstract

Ammonia emissions from urea fertilizers lead to severe environmental problems, such as soil degradation, water pollution, and greenhouse gas formation. In addition, the reduction of nitrogen losses will help in raising crop yield and promoting sustainable agricultural. Scholars have adopted computational intelligence, particularly machine learning (ML), response surface methodology (RSM) and swarm intelligence like the particle swarm optimization (PSO), to minimize ammonia emissions and nitrate leaching during manufacturing and application processes. This paper comparatively reviews two studies: one that used ML-RSM to predict nitrate leaching from coated urea super granules and another that applied PSO as a tool for optimizing the ammonia emissions model in the process of producing urea fertilizer. Both techniques were found to perform well, which contribute to better planning to reduce ammonia emission and nitrogen loss. However, these two studies are restricted to a small range of parameters and are based on laboratory and pilot-scale controlled environments. In general, this paper reveals the possible application areas of computational intelligence toward more sustainable urea fertilizer and provides some useful insight that may interest researchers, practitioners, and policymakers.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Ammonia Emissions
Subjects:	T Technology > TP Chemical technology > TP155-156 Chemical engineering
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	31 Jul 2026 06:41
Last Modified:	31 Jul 2026 06:41
URII:	http://shdl.mmu.edu.my/id/eprint/16419

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