On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models

Yang, Yong; Zheng, Shuaishuai; Ai, Zhilu; Jafari, Mohammad Mahdi Molla

doi:10.1155/2021/9202127

Cited by 16 publications

(9 citation statements)

References 37 publications

(32 reference statements)

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“…The least squares support vector machine (LSSVM) (Kulamala et al, 2021; Yong et al, 2021) is a variation of the support vector machine. The support vector machine is based on supervised learning to classify two different types of sample points.…”

Section: Methodsmentioning

confidence: 99%

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

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Cracks in walnuts during processing and storage can adversely affect their quality and cause economic losses. To achieve efficient identification of cracked walnuts, this study proposed a method of walnut crack identification based on acoustic vibration and feature fusion. First, the sound signals of intact and cracked walnuts were collected using an acoustic signal acquisition system, and 44 time‐domain features, 13 frequency‐domain features, and 768 Mel spectrogram features (the number of pixel frequencies corresponding to the gray‐scale values of R, G, and B channels) of the sound signals were extracted. Then, the classification models of support vector machines (SVM), least squares support vector machines (LSSVM), and extreme learning machines (ELM) were established based on single class features data and fusion of different feature groups data respectively. The results indicated that the LSSVM model with the fusion of the three feature sets was optimal, with an accuracy of 85% in the testing set. Next, three feature selection methods were employed to reduce the dimensionality of the best fused feature data. Subsequently, the LSSVM classification model was established based on the feature selection data. Finally, arithmetic optimization algorithm (AOA), particle swarm optimization (PSO), and gray wolf optimization (GWO) were introduced to optimize the parameters c and of the classification model. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, with 95% accuracy in the testing set. This study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.Practical applicationsCracks in walnut during harvesting, transportation and peeling may lead to economic losses and food safety problems. Aiming at the difficulty and low accuracy of crack identification in Yunnan walnut, this paper proposed a method for crack detection based on acoustic vibration identification combine with feature fusion. The features of time domain, frequency domain and Mel spectrogram were extracted from the effective sound signal, and the features were fused. The influence of three feature selection methods, three models and three optimization algorithms on walnut sound signal recognition was analyzed and compared. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, and the method proposed in this study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.

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Section: Methodsmentioning

confidence: 99%

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

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“…The excellent prediction abilities and availability of suitable algorithms have resulted in the extensive use of ANFIS in the domain of FW valorization. Yang et al (2021) employed the ANFIS to predict biogas yield from several kinds of FW. The results of the proposed models show that they are quite capable of forecasting biogas generation.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Intelligent approaches for sustainable management and valorisation of food waste

Said

Sharma²,

Nhuong

et al. 2023

Bioresource Technology

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“…For example, the support vector machine (SVM) has been presented as the most popular machine learning algorithm to predict biogas output in several studies on wastewater treatment plants. The study findings showed that SVMs were able to achieve an accuracy of 95% [16][17][18]. Another researcher explored the contribution of the artificial neural network (ANN) algorithm in biogas prediction and reported the highest accuracy of 92% [19].…”

Section: Introductionmentioning

confidence: 99%

Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach

Mukasine,

Sibomana,

Jayavel

et al. 2024

Energies

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Biogas is a renewable energy source that comes from biological waste. In the biogas generation process, various factors such as feedstock composition, digester volume, and environmental conditions are vital in ensuring promising production. Accurate prediction of biogas yield is crucial for improving biogas operation and increasing energy yield. The purpose of this research was to propose a novel approach to improve the accuracy in predicting biogas yield using the stacking ensemble machine learning approach. This approach integrates three machine learning algorithms: light gradient-boosting machine (LightGBM), categorical boosting (CatBoost), and an evolutionary strategy to attain high performance and accuracy. The proposed model was tested on environmental data collected from biogas production facilities. It employs optimum parameter selection and stacking ensembles and showed better accuracy and variability. A comparative analysis of the proposed model with others such as k-nearest neighbor (KNN), random forest (RF), and decision tree (DT) was performed. The study’s findings demonstrated that the proposed model outperformed the existing models, with a root-mean-square error (RMSE) of 0.004 and a mean absolute error (MAE) of 0.0024 for the accuracy metrics. In conclusion, an accurate predictive model cooperating with a fermentation control system can significantly increase biogas yield. The proposed approach stands as a pivotal step toward meeting the escalating global energy demands.

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On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models

Cited by 16 publications

References 37 publications

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Intelligent approaches for sustainable management and valorisation of food waste

Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach

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