Integrating Machine Learning Into Process-Based Modeling to Predict Ammonia Losses From Stored Liquid Dairy Manure
| dc.contributor.author | Genedy, Rana Ahmed Kheir | en |
| dc.contributor.committeechair | Ogejo, Jactone Arogo | en |
| dc.contributor.committeemember | Chung, Matthias | en |
| dc.contributor.committeemember | Senger, Ryan S. | en |
| dc.contributor.committeemember | Shortridge, Julie Elizabeth | en |
| dc.contributor.department | Biological Systems Engineering | en |
| dc.date.accessioned | 2023-06-17T08:00:15Z | en |
| dc.date.available | 2023-06-17T08:00:15Z | en |
| dc.date.issued | 2023-06-16 | en |
| dc.description.abstract | Storing manure on dairy farms is essential for maximizing its fertilizer value, reducing management costs, and minimizing potential environmental pollution challenges. However, ammonia loss through volatilization during storage remains a challenge. Quantifying these losses is necessary to inform decision-making processes to improve manure management, and design ammonia mitigation strategies. In 2003, the National Research Council recommended using process-based models to estimate emissions of pollutants, such as ammonia, from animal feeding operations. While much progress has been made to meet this call, still, their accuracy is limited because of the inadequate values of manure properties such as heat and mass transfer coefficients. Additionally, the process-based models lack realistic estimations for manure temperatures; they use ambient air temperature surrogates which was found to underestimate the atmospheric emissions during storage. This study uses machine learning algorithms' unique abilities to address some of the challenges of process-based modeling. Firstly, ammonia concentrations, manure temperature, and local meteorological factors were measured from three dairy farms with different manure management practices and storage types. This data was used to estimate the influence of manure characteristics and meteorological factors on the trend of ammonia emissions. Secondly, the data was subjected to four data-driven machine learning algorithms and a physics-informed neural network (PINN) to predict manure temperature. Finally, a deep-learning approach that combines process-based modeling and recurrent neural networks (LSTM) was introduced to estimate ammonia loss from dairy manure during storage. This method involves inverse problem-solving to estimate the heat and mass transfer coefficients for ammonia transport and emission from stored manure using the hyperparameters optimization tool, Optuna. Results show that ammonia flux patterns mirrored manure temperature closely compared to ambient air temperature, with wind speed and crust thickness significantly influencing ammonia emissions. The data-driven machine learning models used to estimate the ammonia emissions had a high predictive ability; however, their generalization accuracy was poor. However, the PINN model had superior generalization accuracy with R2 during the testing phase exceeded 0.70, in contrast to -0.03 and 0.66 for finite-elements heat transfer and data-driven neural network, respectively. In addition, optimizing the process-based model parameters has significantly improved performance. Finally, Physics-informed LSTM has the potential to replace conventional process-based models due to its computational efficiency and does not require extensive data collection. The outcomes of this study contribute to precision agriculture, specifically designing suitable on-farm strategies to minimize nutrient loss and greenhouse gas emissions during the manure storage periods. | en |
| dc.description.abstractgeneral | Dairy farming is critical for meeting the global demand for animal protein products; however, it generates a lot of manure that must be appropriately managed. Manure can only be applied to crop or pasture lands during growing seasons. Typically, manure is stored on farms until time permits for land application. During storage, microbial processes occur in the manure, releasing gases such as ammonia. Ammonia emitted contributes to the degradation of ambient air quality, human and animal health problems, biodiversity loss, and soil health deterioration. Furthermore, releasing ammonia from stored manure reduces the nitrogen fertilizer value of stored manure. Implementing control measures to mitigate ammonia emission is necessary to reduce nitrogen loss from stored manure. Deciding and applying appropriate control measures require knowledge of the rate of ammonia emission and when it occurs. Process-based models are a less expensive and more reliable method for estimating ammonia emissions from stored liquid dairy manure. Process-based model is a mathematical model that simulates processes related to ammonia production and emission from stored manure. However, process-based models have limitations because they require estimates of manure properties, which vary depending on the manure management. Additionally, these models use air temperature instead of manure temperature, underestimating the ammonia lost during storage. Therefore, this study used machine learning algorithms to develop more accurate models for predicting manure temperature and estimating ammonia emissions. First, we collected manure temperature, ammonia emissions, and weather data from three dairy farms with different manure management practices and storage structures. We used it to estimate the factors that affect ammonia emissions. The data was then used to develop four machine-learning models and one integrated machine-learning-based to assess their ability to predict manure temperature. Finally, a different machine learning approach that combines process-based modeling and neural networks was used to directly estimate ammonia loss from dairy manure during storage. The results show that manure temperature is closely related to the amount of ammonia lost, and factors like wind speed and crust thickness also influence the amount of ammonia lost. Machine learning algorithms offer a more accurate way to predict manure temperature than traditional methods. Finally, combining machine learning and process-based modeling improved the ammonia emission estimates. This study contributes to precision agriculture by designing suitable on-farm strategies to minimize nutrient loss during manure storage periods. It provides valuable information for dairy farmers and policymakers on managing manure storage more effectively and sustainably. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:37409 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/115453 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Physics-informed neural networks | en |
| dc.subject | Machine and deep learning | en |
| dc.subject | Dairy manure | en |
| dc.subject | Ammonia emissions | en |
| dc.subject | Manure management | en |
| dc.title | Integrating Machine Learning Into Process-Based Modeling to Predict Ammonia Losses From Stored Liquid Dairy Manure | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Biological Systems Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
Files
Original bundle
1 - 1 of 1