Dynamic Modeling of Temperature and Moisture Interactions During Poultry Litter Composting Under High-Altitude Climatic Conditions
Keywords:
Poultry litter composting, Temperature dynamics, Humidity interactions, High-altitude conditions, Autoregressive modelingAbstract
The quantitative data-driven modeling approach was employed to study the temperature and humidity behavior during poultry litter composting under high-altitude climatic conditions. For the analysis, 7,378 observations from two experimental composting data sets were used, which included temperature, humidity, temporal measurements and heating and aeration control variables. Process variations and predictive performances were analyzed by descriptive statistics, temporal analysis, correlation analysis and autoregressive modeling with exogenous inputs (ARX). The average compost temperatures in Experiment 1 and 2 were 34.67°C and 30.43°C respectively. For both temperature and recorded humidity, the negative correlations of r = −0.230 and −0.815, respectively, were found, which are experiment-specific and primarily related to the broader temporal trends. Heating and aeration setups were correlated with differences in composting conditions, but causal relationships were not determined. Both ARX models had strong performance in short-term temperature forecasting with R2 equal to 0.829 for ARX(1) and 0.931 for ARX(2), with RMSE of 0.328°C and 0.399°C respectively, while in this case the performance of the ARX models was better than that of the persistence models; however, there was limited improvement for humidity forecasting. The results indicate that dynamic modelling is a promising tool to monitor the temperature of the composting process and to help manage the process. Additional studies using direct moisture measurements and independent validation are suggested.
References
1.Awasthi, M. K., Duan, Y., Awasthi, S. K., Liu, T., & Zhang, Z. (2020). Influence of bamboo biochar on mitigating greenhouse gas emissions and nitrogen loss during poultry manure composting. Bioresource Technology, 303, 122952.
2.Bian, B., Hu, X., Zhang, S., Lv, C., Yang, Z., Yang, W., & Zhang, L. (2019). Pilot-scale composting of typical multiple agricultural wastes: Parameter optimization and mechanisms. Bioresource Technology, 287, 121482.
3.Ding, S., Huang, W., Xu, W., Wu, Y., Zhao, Y., Fang, P., Hu, B., & Lou, L. (2022). Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model. Bioresource Technology, 360, 127606.
4.Ghinea, C., Apostol, L. C., Prisacaru, A. E., & Leahu, A. (2019). Development of a model for food waste composting. Environmental Science and Pollution Research, 26(4), 4056–4069. https://doi.org/10.1007/s11356-018-3939-1
5.Gurla Venkata Kameswari, S., Basavaraju, A., Kumar, C. S., & Bapat, J. (2025). Compost Monitoring System for Kitchen Waste Management: Development, Deployment and Analysis. IoT, 6(4), 64.
6.He, X., Han, L., Ge, J., & Huang, G. (2018). Modelling for reactor-style aerobic composting based on coupling theory of mass-heat-momentum transport and Contois equation. Bioresource Technology, 253, 165–174.
7.Huang, X., He, Y., Zhang, Y., Lu, X., & Xie, L. (2022). Independent and combined effects of biochar and microbial agents on physicochemical parameters and microbial community succession during food waste composting. Bioresource Technology, 366, 128023.
8.Lai, J. C., Then, Y. L., Hwang, S. S., Tam, Y. C., & Chua, C. C. N. (2025). Modelling Temperature Profiles in Food Waste Composting: Monod Kinetics Under Varied Aeration Conditions. Process Integration and Optimization for Sustainability, 9(3), 839–853. https://doi.org/10.1007/s41660-024-00480-w
9.Li, M.-X., He, X.-S., Tang, J., Li, X., Zhao, R., Tao, Y.-Q., Wang, C., & Qiu, Z.-P. (2021). Influence of moisture content on chicken manure stabilization during microbial agent-enhanced composting. Chemosphere, 264, 128549.
10.Li, Y., Ma, J., Yong, X., Luo, L., Wong, J. W., Zhang, Y., Wu, H., & Zhou, J. (2022). Effect of biochar combined with a biotrickling filter on deodorization, nitrogen retention, and microbial community succession during chicken manure composting. Bioresource Technology, 343, 126137.
11.Liu, Z., Yin, Q., Fang, Y., Zhang, X., Xia, W., Jiao, Z., Song, T., Wan, H., & Guo, T. (2024). Effect of exogenous thermophilic biocontrol agent inoculum on the high temperature chicken manure composting. Frontiers in Microbiology, 15, 1484047.
12.Margina, Y., Troegubov, A., Kulikova, Y., & Sliusar, N. (2023). Composting old bark and wood waste in cold weather conditions. Sustainability, 15(14), 10768.
13.Moncks, P. C. S., Corrêa, É., Guidoni, L. L. C., Moncks, R. B., Corrêa, L. B., Lucia Jr, T., Araujo, R. M., Yamin, A. C., & Marques, F. S. (2022). Moisture content monitoring in industrial-scale composting systems using low-cost sensor-based machine learning techniques. Bioresource Technology, 359, 127456.
14.Patiño-Forero, A. (2024). Dataset Supporting “Dynamic Modeling of Poultry Litter Composting in High Mountain Climates using System Identification Techniques” Study. 1. https://doi.org/10.17632/dgxxj2pk8s.1
15.Patiño-Forero, A. A., Salazar-Caceres, F., Ramirez-Murillo, H., Franceschi, F. F., Rincón, R., & Sierra-Rueda, G. (2025). Dynamic Modeling of Poultry Litter Composting in High Mountain Climates Using System Identification Techniques. Automation, 6(3), 36.
16.Policastro, G., & Cesaro, A. (2022). Composting of organic solid waste of municipal origin: The role of research in enhancing its sustainability. International Journal of Environmental Research and Public Health, 20(1), 312.
17.Qasim, W., Lee, M. H., Moon, B. E., Okyere, F. G., Khan, F., Nafees, M., & Kim, H. T. (2018). Composting of chicken manure with a mixture of sawdust and wood shavings under forced aeration in a closed reactor system. International Journal of Recycling of Organic Waste in Agriculture, 7(3), 261–267. https://doi.org/10.1007/s40093-018-0212-z
18.Qasim, W., Moon, B. E., Okyere, F. G., Khan, F., Nafees, M., & Kim, H. T. (2019). Influence of aeration rate and reactor shape on the composting of poultry manure and sawdust. Journal of the Air & Waste Management Association, 69(5), 633–645. https://doi.org/10.1080/10962247.2019.1569570
19.Ravindran, B., Karmegam, N., Awasthi, M. K., Chang, S. W., Selvi, P. K., Balachandar, R., Chinnappan, S., Azelee, N. I. W., & Munuswamy-Ramanujam, G. (2022). Valorization of food waste and poultry manure through co-composting amending saw dust, biochar and mineral salts for value-added compost production. Bioresource Technology, 346, 126442.
20.Sokač, T., Šalić, A., Kučić Grgić, D., Šabić Runjavec, M., Vidaković, M., Jurinjak Tušek, A., Horvat, Đ., Juras Krnjak, J., Vuković Domanovac, M., & Zelić, B. (2022). An enhanced composting process with bioaugmentation: Mathematical modelling and process optimization. Waste Management & Research: The Journal for a Sustainable Circular Economy, 40(6), 745–753. https://doi.org/10.1177/0734242X211033712
21.Sun, S., Guo, C., Wang, J., Ren, L., Qu, J., Guan, Q., Dou, N., Zhang, J., Chen, Q., & Wang, Q. (2024). Effect of initial moisture content, resulting from different ratios of vegetable waste to maize straw, on compost was mediated by composting temperatures and microbial communities at low temperatures. Chemosphere, 357, 141808.
22.Wan, W., Wang, Y., Tan, J., Qin, Y., Zuo, W., Wu, H., He, H., & He, D. (2020). Alkaline phosphatase-harboring bacterial community and multiple enzyme activity contribute to phosphorus transformation during vegetable waste and chicken manure composting. Bioresource Technology, 297, 122406.
23.Wan, X., Li, J., Xie, L., Wei, Z., Wu, J., Tong, Y. W., Wang, X., He, Y., & Zhang, J. (2022). Machine learning framework for intelligent prediction of compost maturity towards automation of food waste composting system. Bioresource Technology, 365, 128107.
24.Wang, B., Zhang, P., Qi, X., Li, G., & Zhang, J. (2024). Predicting ammonia emissions and global warming potential in composting by machine learning. Bioresource Technology, 411, 131335.
25.Wang, W., Zhang, P., He, H., Liu, H., Yan, J., He, H., Wen, B., Cui, Z., & Yuan, X. (2025). Effects of Chicken Manure Combinations on Compost Maturity and Microbial Community Dynamics in Industrial-Scale Composting. Waste and Biomass Valorization, 16(12), 6805–6814. https://doi.org/10.1007/s12649-025-03056-y
26.Wang, Y., Yu, Q., Zheng, C., Wang, Y., Chen, H., Dong, S., & Hu, X. (2024). The impact of microbial inoculants on large-scale composting of straw and manure under natural low-temperature conditions. Bioresource Technology, 400, 130696.