A Real-Time Gas Sensor Network with Adaptive Feedback Control for Automated Composting Management

Abdulqader Ghaleb Naser, Nazmi Mat Nawi (), Mohd Rafein Zakaria, Muhamad Saufi Mohd Kassim, Azimov Abdugani Mutalovich and Kamil Kayode Katibi
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Abdulqader Ghaleb Naser: Department of Agricultural Machinery and Equipment, Faculty of Agriculture, Tikrit University, Tikrit 34001, Iraq
Nazmi Mat Nawi: Department of Biological & Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Mohd Rafein Zakaria: Institute of Plantation Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia
Muhamad Saufi Mohd Kassim: Department of Biological & Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Azimov Abdugani Mutalovich: Research Laboratory Innovative Water Treatment Systems, M. Auezov South Kazakhstan University, Shymkent 16000, Kazakhstan
Kamil Kayode Katibi: Department of Food and Agricultural Engineering, Faculty of Engineering and Technology, Kwara State University, Malete, Ilorin 23431, Nigeria

Sustainability, 2025, vol. 17, issue 22, 1-27

Abstract: This study addressed the persistent limitation of discontinuous and labor-intensive compost monitoring procedures by developing and field-validating a low-cost sensor system for monitoring oxygen (O 2 ), carbon dioxide (CO 2 ), and methane (CH 4 ) under tropical windrow conditions. In contrast to laboratory-restricted studies, this framework integrated rigorous calibration, multi-layer statistical validation, and process optimization into a unified, real-time adaptive design. Experimental validation was performed across three independent composting replicates to ensure reproducibility and account for environmental variability. Calibration using ISO-traceable gas standards generated linear correction models, confirming sensor accuracy within ±1.5% for O 2 , ±304 ppm for CO 2 , and ±1.3 ppm for CH 4 . Expanded uncertainties (U 95 ) remained within acceptable limits for composting applications, reinforcing the precision and reproducibility of the calibration framework. Sensor reliability and agreement with reference instruments were statistically validated using analysis of variance (ANOVA), intraclass correlation coefficient (ICC), and Bland–Altman analysis. Validation against a reference multi-gas analyzer demonstrated laboratory-grade accuracy, with ICC values exceeding 0.97, ANOVA showing no significant phase-wise differences ( p > 0.95), and Bland–Altman plots confirming near-zero bias and narrow agreement limits. Ecological interdependencies were also captured, with O 2 strongly anticorrelated to CO 2 ( r = −0.967) and CH 4 moderately correlated with pH ( r = 0.756), consistent with microbial respiration and methanogenic activities. Nutrient analyses indicated compost maturity, marked by increases in nitrogen (+31.7%), phosphorus (+87.7%), and potassium (+92.3%). Regression analysis revealed that ambient temperature explained 25.8% of CO 2 variability (slope = 520 ppm °C −1 , p = 0.021), whereas O 2 and CH 4 remained unaffected. Overall, these findings validate the developed sensors as accurate and resilient tools, enabling real-time adaptive intervention, advancing sustainable waste valorization, and aligning with the United Nations Sustainable Development Goals (SDGs) 12 and 13.

Keywords: composting; real-time monitoring; sensor validation; greenhouse gases; sustainable agriculture (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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