Prediction of biogas production rate from dry anaerobic digestion of food waste: Process-based approach vs. recurrent neural network black-box model

Seo, Kyu Won; Seo, Jangwon; Kim, Kyungil; Lim, Seung Ji; Chung, Jaeshik

doi:10.1016/j.biortech.2021.125829

Cited by 32 publications

(5 citation statements)

References 31 publications

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“…These two networks are commonly used in deep learning. Nevertheless, because the inside of the neural network is a black box, we can only determine which network can better meet our needs by comparing speci c problems 33,34 . Accordingly, we hoped to use different network structures to solve clinical problems to select a more suitable network structure and train a model with higher accuracy.…”

Section: Resultsmentioning

confidence: 99%

Deep learning for automatic diagnosis of fundus diseases on optical coherence tomography images

Yang

Wang

Huang

et al. 2022

Preprint

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Background There has been increasing attention on the use of deep learning systems and transfer learning to identify disease features and progression. In this study, we aimed to apply deep learning algorithms to Optical coherence tomography (OCT) images to quickly identify common referable fundus diseases. Method A total of 24000 OCT images (test 1) retrospectively acquired from the Kaggle database with age-related macular degeneration, choroidal neovascularization, central serous chorioretinopathy, diabetic macular edema, diabetic retinopathy, drusen, macular hole, and NOR were used to develop the model. Images were split into training, validation, and testing sets. The convolutional neural networks ResNet101 and DenseNet121 were trained to classify images. The area under the receiver operating characteristic curve (AUC), accuracy, and F1 score were calculated to evaluate the performance of the models. A total of 800 OCT images (test 2) diagnosed with the above eight diseases were collected from the Zhengzhou Eye Hospital to retest the accuracy of the models. Results ResNet101 performed better than DenseNet121 did. The classification performance in terms of accuracy and F1 score of ResNet101 were 0.9398 and 0.9360, respectively, in test 2. The AUC of ResNet101 for the eight diseases based on test 2 were 0.9956 (macro-average) and 0.9932 (micro-average). When using DenseNet121 in test 2, the accuracy was 0.7130, and the F1 score was 0.7116. The macro-average AUC was 0.8519, and the micro-average AUC was 0.8366. Conclusions Convolutional neural network ResNet101 and transfer learning showed good performance in discriminating between OCT images. As a promising adjunctive tool, our model can provide rapid provisional diagnosis for patients with common referable fundus diseases.

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Section: Resultsmentioning

confidence: 99%

Deep learning for automatic diagnosis of fundus diseases on optical coherence tomography images

Yang

Wang

Huang

et al. 2022

Preprint

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“…The linear regression models (MLR and PLSR) were 11% of all data-driven methods applied to model anaerobic digestion processes. These methods were applied using feedstock properties (e.g., lignin, cellulose, hemicellulose content) as model input from both primary and secondary data for agricultural residue and animal waste. − Apart from the limited applications of linear models, 39% of the data-driven anaerobic digestion models were based on MPR, ,,,− and 41% were ANN and ANFIS ,,,,− (Figure a and Table S9). These models used primary data comprised of operational parameters (e.g., temperature, hydraulic retention time) as well as the quantity of feedstocks (agricultural residue and animal waste).…”

Section: Applications Of Data Science In Rrcc From Organic Waste Streamsmentioning

confidence: 99%

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Zaki,

Rowles,

Adjeroh

et al. 2023

ACS EST Eng.

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Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002−2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to costeffectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

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“…Seo et al (2021) [100] tried to predict the biogas yield rate from continuous SSAD of food waste with the aid of the process-wise modeling or the neural network approach. The sludge retention time (SRT), soluble COD, total VFA, total ammonia-nitrogen (TAN) and free ammonia are used as model input.…”

Section: Advanced Statistical Modelsmentioning

confidence: 99%

Overview of Numerical Simulation of Solid-State Anaerobic Digestion Considering Hydrodynamic Behaviors, Phenomena of Transfer, Biochemical Kinetics and Statistical Approaches

Liu

Coutu²,

Mottelet

et al. 2023

Energies

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Anaerobic digestion (AD) is a promising way to produce renewable energy. The solid-state anaerobic digestion (SSAD) with a dry matter content more than 15% in the reactors is seeing its increasing potential in biogas plant deployment. The relevant processes involve multiple of evolving chemical and physical phenomena that are not crucial to conventional liquid-state anaerobic digestion processes (LSAD). A good simulation of SSAD is of great importance to better control and operate the reactors. The modeling of SSAD reactors could be realized either by theoretical or statistical approaches. Both have been studied to a certain extent but are still not sound. This paper introduces the existing mathematical tools for SSAD simulation using theoretical, empirical and advanced statistical approaches and gives a critical review on each type of model. The issues of parameter identifiability, preference of modeling approaches, multiscale simulations, sensibility analysis, particularity of SSAD operations and global lack of knowledge in SSAD media evolution were discussed. The authors call for a stronger collaboration of multidisciplinary research in order to further developing the numeric simulation tools for SSAD.

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Prediction of biogas production rate from dry anaerobic digestion of food waste: Process-based approach vs. recurrent neural network black-box model

Cited by 32 publications

References 31 publications

Deep learning for automatic diagnosis of fundus diseases on optical coherence tomography images

Deep learning for automatic diagnosis of fundus diseases on optical coherence tomography images

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Overview of Numerical Simulation of Solid-State Anaerobic Digestion Considering Hydrodynamic Behaviors, Phenomena of Transfer, Biochemical Kinetics and Statistical Approaches

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