AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
Biological treatment technologies (such as anaerobic digestion, composting, and insect farming) have been extensively employed to handle various degradable organic wastes. However, the inherent complexity and instability of biological treatment processes adversely affect the production of renewable energy and nutrient-rich products. To ensure stable processes and consistent product quality, researchers have invested heavily in control strategies for biological treatment, with machine learning (ML) recently proving effective in optimizing treatment, predicting parameters, detecting disturbances, and enabling real-time monitoring. This review critically assesses the application of ML in biological treatment, providing an in-depth evaluation of key algorithms. This study reveals that artificial neural networks, tree-based models, support vector machines, and genetic algorithms are the leading algorithms in biological treatment. A thorough investigation of the applications of ML in anaerobic digestion, composting, and insect farming underscores its remarkable capacity to predict products, optimize processes, perform real-time monitoring, and mitigate pollution emissions. Furthermore, this review outlines the challenges and prospects encountered in applying ML to biological treatment, highlighting crucial directions for future research in this area.
Abdel daiem, M. M., Hatata, A., & Said, N. (2022). Modeling and optimization of semi-continuous anaerobic co-digestion of activated sludge and wheat straw using Nonlinear Autoregressive Exogenous neural network and seagull algorithm. Energy, 241, Article 122939.
Aghbashlo, M., Peng, W., Tabatabaei, M., Kalogirou, S. A., Soltanian, S., Hosseinzadeh-Bandbafha, H., Mahian, O., & Lam, S. S. (2021). Machine learning technology in biodiesel research: A review. Progress in Energy and Combustion Science, 85, Article 100904.
Alavi, N., Sarmadi, K., Goudarzi, G., Babaei, A. A., Bakhshoodeh, R., & Paydary, P. (2019). Attenuation of tetracyclines during chicken manure and bagasse co-composting: Degradation, kinetics, and artificial neural network modeling. Journal of Environmental Management, 231, 1203-1210.
Almomani, F. (2020). Prediction of biogas production from chemically treated co-digested agricultural waste using artificial neural network. Fuel, 280, Article 118573.
Arshad, M. Y., Saeed, S., Raza, A., Ahmad, A. S., Urbanowska, A., Jackowski, M., & Niedzwiecki, L. (2023). Integrating life cycle assessment and machine learning to enhance black soldier fly larvae-based composting of kitchen waste. Sustainability, 15, Article 12475.
Asadi, M., Guo, H., & McPhedran, K. (2020). Biogas production estimation using data-driven approaches for cold region municipal wastewater anaerobic digestion. Journal of Environmental Management, 253, Article 109708.
Awhangbo, L., Schmitt, V., Marcilhac, C., Charnier, C., Latrille, E., & Steyer, J. P. (2024). Determination of the optimal feed recipe of anaerobic digesters using a mathematical model and a genetic algorithm. Bioresource Technology, 393, Article 130091.
Aycan Dümenci, N., Cagcag Yolcu, O., Aydin Temel, F., & Turan, N. G. (2021). Identifying the maturity of co-compost of olive mill waste and natural mineral materials: Modelling via ANN and multi-objective optimization. Bioresource Technology, 338, Article 125516.
Aydın Temel, F., Cagcag Yolcu, O., & Turan, N. G. (2023). Artificial intelligence and machine learning approaches in composting process: A review. Bioresource Technology, 370, Article 128539.
Bai, X., Li, Z., Wang, X., He, X., Cheng, S., Bai, X., & Gao, R. (2017). Online measurement of alkalinity in anaerobic co-digestion using linear regression method. International Journal of Agricultural and Biological Engineering, 10, 176-183.
Bayındır, Y., Cagcag Yolcu, O., Aydın Temel, F., & Turan, N. G. (2022). Evaluation of a cascade artificial neural network for modeling and optimization of process parameters in co-composting of cattle manure and municipal solid waste. Journal of Environmental Management, 318, Article 115496.
Beltramo, T., Klocke, M., & Hitzmann, B. (2019). Prediction of the biogas production using GA and ACO input features selection method for ANN model. Information Processing in Agriculture, 6, 349-356.
Bernal, M. P., Alburquerque, J. A., & Moral, R. (2009). Composting of animal manures and chemical criteria for compost maturity assessment. A review. Bioresource Technology, 100, 5444-5453.
Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021). Machine learning for industrial applications: A comprehensive literature review. Expert Systems with Applications, 175, Article 114820.
Biyada, S., Merzouki, M., Elkarrach, K., & Benlemlih, M. (2020). Spectroscopic characterization of organic matter transformation during composting of textile solid waste using UV–Visible spectroscopy, Infrared spectroscopy and X-ray diffraction (XRD). Microchemical Journal, 159, Article 105314.
Bong, C. P. C., Lim, L. Y., Lee, C. T., Klemeš, J. J., Ho, C. S., & Ho, W. S. (2018). The characterisation and treatment of food waste for improvement of biogas production during anaerobic digestion–A review. Journal of Cleaner Production, 172, 1545-1558.
Boniecki, P., Dach, J., Mueller, W., Koszela, K., Przybyl, J., Pilarski, K., & Olszewski, T. (2013). Neural prediction of heat loss in the pig manure composting process. Applied Thermal Engineering, 58, 650-655.
Chakraborty, S., Das, B. S., Nasim Ali, M., Li, B., Sarathjith, M. C., Majumdar, K., & Ray, D. P. (2014). Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning. Waste Management, 34, 623-631.
Chen, L., Hu, J., Han, Q., Zhang, J., Zhou, Z., Zhou, N., Zhou, H., Lu, X., Mi, B., & Wu, F. (2022a). Resolving the enhancement effect of microwave-assisted pyrolysis on biochar redox properties from the structure-activity relationship. Journal of Analytical and Applied Pyrolysis, 167, Article 105706.
Chen, M., Huang, Y., Liu, H., Xie, S., & Abbas, F. (2019). Impact of different nitrogen source on the compost quality and greenhouse gas emissions during composting of garden waste. Process Safety and Environmental Protection, 124, 326-335.
Chen, J., Jin, C., Sun, S., Yang, D., He, Y., Gan, P., Nalume, W. G., Ma, Y., He, W., & Li, G. (2023). Recognizing the challenges of composting: Critical strategies for control, recycling, and valorization of nitrogen loss. Resources, Conservation and Recycling, 198, Article 107172.
Chen, T., Su, P., Shen, Y., Chen, L., Mahmud, M., Zhao, Y., & Antoniou, G. (2022b). A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia. Frontiers in Neuroscience, 16, Article 867664.
Cheng, X., Xu, R., Wu, Y., Tang, B., Luo, Y., Huang, W., Wang, F., Fang, S., Feng, Q., & Cheng, Y., et al. (2024). Predicting and evaluating different pretreatment methods on methane production from sludge anaerobic digestion via automated machine learning with ensembled semisupervised learning. ACS ES&T Engineering, 4, 525-539.
Chiu, M. C., Wen, C. Y., Hsu, H. W., & Wang, W. C. (2022). Key wastes selection and prediction improvement for biogas production through hybrid machine learning methods. Sustainable Energy Technologies and Assessments, 52, Article 102223.
Chong, D. J. S., Chan, Y. J., Arumugasamy, S. K., Yazdi, S. K., & Lim, J. W. (2023). Optimisation and performance evaluation of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the prediction of biogas production from palm oil mill effluent (POME). Energy, 266, Article 126449.
Cruz, I. A., Andrade, L. R. S., Bharagava, R. N., Nadda, A. K., Bilal, M., Figueiredo, R. T., & Ferreira, L. F. R. (2021). An overview of process monitoring for anaerobic digestion. Biosystems Engineering, 207, 106-119.
Cruz, I. A., Chuenchart, W., Long, F., Surendra, K., Andrade, L. R. S., Bilal, M., Liu, H., Figueiredo, R. T., Khanal, S. K., & Ferreira, L. F. R. (2022). Application of machine learning in anaerobic digestion: Perspectives and challenges. Bioresource Technology, 345, Article 126433.
Cruz, I. A., Nascimento, V. R. S., Felisardo, R. J. A., dos Santos, A. M. G., de Jesus, A. A., de Vasconcelos, B. R., Kumar, V., Cavalcanti, E. B., de Souza, R. L., & Romanholo Ferreira, L. F. (2023). Evaluation of artificial neural network models for predictive monitoring of biogas production from cassava wastewater: A training algorithms approach. Biomass and Bioenergy, 175, Article 106869.
Cruz-Tirado, J. P., Amigo, J. M., & Barbin, D. F. (2023). Determination of protein content in single black fly soldier (Hermetia illucens L.) larvae by near infrared hyperspectral imaging (NIR-HSI) and chemometrics. Food Control, 143, Article 109266.
Díaz, M. J., Eugenio, M. E., López, F., García, J. C., & Yañez, R. (2012). Neural models for optimizing lignocellulosic residues composting process. Waste and Biomass Valorization, 3, 319-331.
Dahunsi, S. O., Oranusi, S., Owolabi, J. B., & Efeovbokhan, V. E. (2016). Mesophilic anaerobic co-digestion of poultry dropping and Carica papaya peels: Modelling and process parameter optimization study. Bioresource Technology, 216, 587-600.
de Clercq, D., Jalota, D., Shang, R., Ni, K., Zhang, Z., Khan, A., Wen, Z., Caicedo, L., & Yuan, K. (2019). Machine learning powered software for accurate prediction of biogas production: A case study on industrial-scale Chinese production data. Journal of Cleaner Production, 218, 390-399.
de Clercq, D., Wen, Z., Fei, F., Caicedo, L., Yuan, K., & Shang, R. (2020). Interpretable machine learning for predicting biomethane production in industrial-scale anaerobic co-digestion. Science of the Total Environment, 712, Article 134574.
Dias, B. O., Silva, C. A., Higashikawa, F. S., Roig, A., & nchez-Monedero, M. A. (2010). Use of biochar as bulking agent for the composting of poultry manure: Effect on organic matter degradation and humification. Bioresource Technology, 101, 1239-1246.
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, Article 127606.
Dragoi, E. N., Godini, K., & Koolivand, A. (2021). Modeling of oily sludge composting process by using artificial neural networks and differential evolution: Prediction of removal of petroleum hydrocarbons and organic carbon. Environmental Technology & Innovation, 21, Article 101338.
Fajobi, M. O., Lasode, O. A., Adeleke, A. A., Ikubanni, P. P., Balogun, A. O., & Paramasivam, P. (2023). Prediction of biogas yield from codigestion of lignocellulosic biomass using adaptive neuro-fuzzy inference system (ANFIS) model. Journal of Engineering, 2023, Article 9335814.
Ge, Y., Tao, J., Wang, Z., Chen, C., Mu, L., Ruan, H., Rodríguez Yon, Y., Su, H., Yan, B., & Chen, G. (2023). Modification of anaerobic digestion model No.1 with Machine learning models towards applicable and accurate simulation of biomass anaerobic digestion. Chemical Engineering Journal, Article 454, 140369.
Ghatak, M. D., & Ghatak, A. (2018). Artificial neural network model to predict behavior of biogas production curve from mixed lignocellulosic co-substrates. Fuel, 232, 178-189.
Ghersi, D. E., Loubar, K., Amoura, M., & Tazerout, M. (2021). Multi-objective optimization of micro co-generation spark-ignition engine fueled by biogas with various CH4/CO2 content based on GA-ANN and decision-making approaches. Journal of Cleaner Production, 329, Article 129739.
Gil, A., Toledo, M., Siles, J., & Martín, M. (2018). Multivariate analysis and biodegradability test to evaluate different organic wastes for biological treatments: Anaerobic co-digestion and co-composting. Waste Management, 78, 819-828.
Goyal, S., Dhull, S. K., & Kapoor, K. K. (2005). Chemical and biological changes during composting of different organic wastes and assessment of compost maturity. Bioresource Technology, 96, 1584-1591.
Gueguim Kana, E. B., Oloke, J. K., Lateef, A., & Adesiyan, M. O. (2012). Modeling and optimization of biogas production on saw dust and other co-substrates using Artificial Neural network and Genetic Algorithm. Renewable Energy, 46, 276-281.
Guo, H. N., Liu, H. T., & Wu, S. (2022). Simulation, prediction and optimization of typical heavy metals immobilization in swine manure composting by using machine learning models and genetic algorithm. Journal of Environmental Management, 323, Article 116266.
Guo, X. X., Liu, H. T., & Zhang, J. (2020). The role of biochar in organic waste composting and soil improvement: A review. Waste Management, 102, 884-899.
Guo, H. N., Wu, S. B., Tian, Y., Zhang, J., & Liu, H. T. (2021a). Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review. Bioresource Technology, 319, Article 124114.
Guo, X. X., Wu, S. B., Wang, X., & Liu, H. T. (2021b). Impact of biochar addition on three-dimensional structural changes in aggregates associated with humus during swine manure composting. Journal of Cleaner Production, 280, Article 124380.
Gupta, U., & Gupta, D. (2023). Least squares structural twin bounded support vector machine on class scatter. Applied Intelligence, 53, 15321-15351.
Gupta, R., Zhang, L., Hou, J., Zhang, Z., Liu, H., You, S., Ok, Y. S., & Li, W. (2023). Review of explainable machine learning for anaerobic digestion. Bioresource Technology, 369, Article 128468.
Haffiez, N., Chung, T. H., Zakaria, B. S., Shahidi, M., Mezbahuddin, S., Maal-Bared, R., & Dhar, B. R. (2022). Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion. The Science of the Total Environment, 839, Article 156211.
Harirchi, S., Wainaina, S., Sar, T., Ali Nojoumi, S., Parchami, M., Parchami, M., Varjani, S., Khanal, S. K., Wong, J., & Awasthi, M. K., et al. (2022). Microbiological insights into anaerobic digestion for biogas, hydrogen or volatile fatty acids (VFAs): A review. Bioengineered, 13, 6521-6557.
Heydari, B., Abdollahzadeh Sharghi, E., Rafiee, S., & Mohtasebi, S. S. (2021). Use of artificial neural network and adaptive neuro-fuzzy inference system for prediction of biogas production from spearmint essential oil wastewater treatment in up-flow anaerobic sludge blanket reactor. Fuel, 306, Article 121734.
Hoang, A. T., Varbanov, P. S., Nižetić, S., Sirohi, R., Pandey, A., Luque, R., Ng, K. H., & Pham, V. V. (2022). Perspective review on Municipal Solid Waste-to-energy route: Characteristics, management strategy, and role in circular economy. Journal of Cleaner Production, 359, Article 131897.
Hosseinzadeh, A., Baziar, M., Alidadi, H., Zhou, J. L., Altaee, A., Najafpoor, A. A., & Jafarpour, S. (2020). Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions. Bioresource Technology, 303, Article 122926.
Jacob, S., & Banerjee, R. (2016). Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm. Bioresource Technology, 214, 386-395.
Jaroenpoj, S., Yu, Q. J., & Ness, J. (2014). Development of artificial neural network models for biogas production from co-digestion of leachate and pineapple peel. The Global Environmental Engineers, 1, 42-47.
Jeong, K., Abbas, A., Shin, J., Son, M., Kim, Y. M., & Cho, K. H. (2021). Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models. Water Research, 205, Article 117697.
Jia, R., Song, Y. C., Piao, D., Kim, K., Lee, C., & Park, J. (2022). Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors. Bioresource Technology, 363, Article 127908.
Jiang, J., Han, G., Liu, L., Shu, L., & Guizani, M. (2020). Outlier detection approaches based on machine learning in the internet-of-things. IEEE Wireless Communications, 2, 53-59.
Jiménez, J., Guardia-Puebla, Y., Cisneros-Ortiz, M., Morgan-Sagastume, J., Guerra, G., & Noyola, A. (2015). Optimization of the specific methanogenic activity during the anaerobic co-digestion of pig manure and rice straw, using industrial clay residues as inorganic additive. Chemical Engineering Journal, 259, 703-714.
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences, 374, Article 20150202.
Kabak, E. T., Cagcag Yolcu, O., Aydın Temel, F., & Turan, N. G. (2022). Prediction and optimization of nitrogen losses in co-composting process by using a hybrid cascaded prediction model and genetic algorithm. Chemical Engineering Journal, 437, Article 135499.
Kainthola, J., Kalamdhad, A. S., & Goud, V. V. (2020). Optimization of process parameters for accelerated methane yield from anaerobic co-digestion of rice straw and food waste. Renewable Energy, 149, 1352-1359.
Kang, J., Yin, Z., Pei, F., Ye, Z., Song, G., Ling, H., Gao, D., Jiang, X., Zhang, C., & Ge, J. (2022). Aerobic composting of chicken manure with penicillin G: Community classification and quorum sensing mediating its contribution to humification. Bioresource Technology, 352, Article 127097.
Kazemi, P., Steyer, J. P., Bengoa, C., Font, J., & Giralt, J. (2020). Robust data-driven soft sensors for online monitoring of volatile fatty acids in anaerobic digestion processes. Processes, 8, 67.
Kettaneh, N., Berglund, A., & Wold, S. (2005). PCA and PLS with very large data sets. Computational Statistics & Data Analysis, 48, 69-85.
Khan, M., Chuenchart, W., Surendra, K. C., & Kumar Khanal, S. (2023). Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects. Bioresource Technology, 370, Article 128501.
Khan, M. A., Ngo, H. H., Guo, W. S., Liu, Y., Nghiem, L. D., Hai, F. I., Deng, L. J., Wang, J., & Wu, Y. (2016). Optimization of process parameters for production of volatile fatty acid, biohydrogen and methane from anaerobic digestion. Bioresource Technology, 219, 738-748.
Khashaba, N. H., Ettouney, R. S., Abdelaal, M. M., Ashour, F. H., & El-Rifai, M. A. (2022). Artificial neural network modeling of biochar enhanced anaerobic sewage sludge digestion. Journal of Environmental Chemical Engineering, 10, Article 107988.
Komilis, D. P., & Tziouvaras, I. S. (2009). A statistical analysis to assess the maturity and stability of six composts. Waste Management, 29, 1504-1513.
Krmar, J., Vukićević, M., Kovačević, A., Protić, A., Zečević, M., & Otašević, B. (2020). Performance comparison of nonlinear and linear regression algorithms coupled with different attribute selection methods for quantitative structure - retention relationships modelling in micellar liquid chromatography. Journal of Chromatography A, 1623, Article 461146.
Kujawa, S., Mazurkiewicz, J., & Czekała, W. (2020). Using convolutional neural networks to classify the maturity of compost based on sewage sludge and rapeseed straw. Journal of Cleaner Production, 258, Article 120814.
Kunatsa, T., & Xia, X. (2022). A review on anaerobic digestion with focus on the role of biomass co-digestion, modelling and optimisation on biogas production and enhancement. Bioresource Technology, 344, Article 126311.
Leitão, R. C., van Haandel, A. C., Zeeman, G., & Lettinga, G. (2006). The effects of operational and environmental variations on anaerobic wastewater treatment systems: A review. Bioresource Technology, 97, 1105-1118.
Li, C., He, P., Peng, W., Lü, F., Du, R., & Zhang, H. (2022a). Exploring available input variables for machine learning models to predict biogas production in industrial-scale biogas plants treating food waste. Journal of Cleaner Production, 380, Article 135074.
Li, Y., Li, S., Sun, X., & Hao, D. (2022d). Prediction of carbon dioxide production from green waste composting and identification of critical factors using machine learning algorithms. Bioresource Technology, 360, Article 127587.
Li, S., Sun, K., Latif, A., Si, Y., Gao, Y., & Huang, Q. (2022c). Insights into the applications of extracellular laccase-aided humification in livestock manure composting. Environmental Science & Technology, 56, 7412-7425.
Li, Y., Xue, Z., Li, S., Sun, X., & Hao, D. (2023). Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning. Bioresource Technology, 385, Article 129444.
Li, J., Zhang, L., Li, C., Tian, H., Ning, J., Zhang, J., Tong, Y. W., & Wang, X. (2022b). Data-driven based In-depth interpretation and inverse design of anaerobic digestion for CH4-rich biogas production. ACS ES&T Engineering, 2, 642-652.
Lohri, C. R., Diener, S., Zabaleta, I., Mertenat, A., & Zurbrügg, C. (2017). Treatment technologies for urban solid biowaste to create value products: A review with focus on low- and middle-income settings. Reviews in Environmental Science and Biotechnology, 16, 81-130.
Long, F., Wang, L., Cai, W., Lesnik, K., & Liu, H. (2021). Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data. Water Research, 199, Article 117182.
Lv, Y., Bao, J., Li, S., Liu, D., Dai, D., Qv, M., & Zhu, L. (2022). Biochar amendment of aerobic composting for the effective biodegradation of heavy oil and succession of bacterial community. Bioresource Technology, 362, Article 127820.
Machineni, L., & Anupoju, G. R. (2023). Optimization of biomethane production from sweet sorghum bagasse using artificial neural networks combined with particle swarm algorithm. Environmental Science and Pollution Research, 30, 114095-114110.
Matheri, A. N., Sethunya, V. L., Belaid, M., & Muzenda, E. (2018). Analysis of the biogas productivity from dry anaerobic digestion of organic fraction of municipal solid waste. Renewable and Sustainable Energy Reviews, 81, 2328-2334.
Mo, R., Guo, W., Batstone, D., Makinia, J., & Li, Y. (2023). Modifications to the anaerobic digestion model no. 1 (ADM1) for enhanced understanding and application of the anaerobic treatment processes – a comprehensive review. Water Research, 244, Article 120504.
Moncks, P. C. S., Correa, É.K., 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, Article 127456.
Muinde, J., Tanga, C. M., Olukuru, J., Odhiambo, C., Tonnang, H. E. Z., & Senagi, K. (2023). Application of machine learning techniques to discern optimal rearing conditions for improved black soldier fly farming. Insects, 14, 479.
Najafi, B., & Ardabili, S. F. (2018). Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC). Resources, Conservation and Recycling, 133, 169-178.
Neto, J. G., Ozorio, L. V., Abreu, T. C. C. D., Santos, B. F., & Pradelle, F. (2021). Modeling of biogas production from food, fruits and vegetables wastes using artificial neural network (ANN). Fuel, 285, Article 119081.
Nguyen, D., Gadhamshetty, V., Nitayavardhana, S., & Khanal, S. K. (2015). Automatic process control in anaerobic digestion technology: A critical review. Bioresource Technology, 193, 513-522.
Nie, E., He, P., Peng, W., Zhang, H., & Lü, F. (2023). Microbial volatile organic compounds as novel indicators of anaerobic digestion instability: Potential and challenges. Biotechnology Advances, 67, Article 108204.
Obileke, K., Tangwe, S., Makaka, G., & Mukumba, P. (2023). Comparison of prediction of biogas yield in a batch mode underground fixed dome digester with cow dung. Biomass Conversion and Biorefinery. https://doi.org/10.1007/s13399-023-04593-z.
Ojha, S., Bußler, S., & Schlüter, O. K. (2020). Food waste valorisation and circular economy concepts in insect production and processing. Waste Management, 118, 600-609.
Okwu, M. O., Samuel, O. D., Ewim, D. R. E., & Huan, Z. (2021). Estimation of biogas yields produced from combination of waste by implementing response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). International Journal of Energy and Environmental Engineering, 12, 353-363.
Okwu, M. O., Samuel, O. D., Otanocha, O. B., Tartibu, L. K., Omoregbee, H. O., & Mbachu, V. M. (2023). Development of ternary models for prediction of biogas yield in a novel modular biodigester: A case of fuzzy Mamdani model (FMM), artificial neural network (ANN), and response surface methodology (RSM). Biomass Conversion and Biorefinery, 13, 917-926.
Olatunji, K. O., Ahmed, N. A., Madyira, D. M., Adebayo, A. O., Ogunkunle, O., & Adeleke, O. (2022). Performance evaluation of ANFIS and RSM modeling in predicting biogas and methane yields from Arachis hypogea shells pretreated with size reduction. Renewable Energy, 189, 288-303.
Oloko-Oba, M. I., Taiwo, A. E., Ajala, S. O., Solomon, B. O., & Betiku, E. (2018). Performance evaluation of three different-shaped bio-digesters for biogas production and optimization by artificial neural network integrated with genetic algorithm. Sustainable Energy Technologies and Assessments, 26, 116-124.
Pornsing, C., Sodhi, M. S., & Lamond, B. F. (2016). Novel self-adaptive particle swarm optimization methods. Soft Computing, 20, 3579-3593.
Puyol, D., Batstone, D. J., Hülsen, T., Astals, S., Peces, M., & Krömer, J. O. (2017). Resource recovery from wastewater by biological technologies: Opportunities, challenges, and prospects. Frontiers in Microbiology, 7, 2106.
Ramachandran, A., Rustum, R., & Adeloye, A. J. (2019). Review of anaerobic digestion modeling and optimization using nature-inspired techniques. Processes, 7, 953.
Renner, G., & Ekárt, A. (2003). Genetic algorithms in computer aided design. Computer-Aided Design, 35, 709-726.
Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45, 341-358.
Sakiewicz, P., Piotrowski, K., Ober, J., & Karwot, J. (2020). Innovative artificial neural network approach for integrated biogas – wastewater treatment system modelling: Effect of plant operating parameters on process intensification. Renewable and Sustainable Energy Reviews, 124, Article 109784.
Sangeetha, J., & Govindarajan, P. (2023). Prediction of agricultural waste compost maturity using fast regions with convolutional neural network(R-CNN). Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2023.01.112.
Sappl, J., Harders, M., & Rauch, W. (2023). Machine learning for quantile regression of biogas production rates in anaerobic digesters. The Science of the Total Environment, 872, Article 161923.
Senthilkumar, N., Deepanraj, B., Vasantharaj, K., & Sivasubramanian, V. (2016). Optimization and performance analysis of process parameters during anaerobic digestion of food waste using hybrid GRA-PCA technique. Journal of Renewable and Sustainable Energy, 8, Article 063107.
Sharma, P., Gaur, V. K., Kim, S. H., & Pandey, A. (2020). Microbial strategies for bio-transforming food waste into resources. Bioresource Technology, 299, Article 122580.
Shehadeh, A., Alshboul, O., Al Mamlook, R. E., & Hamedat, O. (2021). Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression. Automation in Construction, 129, Article 103827.
Shen, G., Chen, Y., Zhang, J., Wu, Y., Yi, Y., Li, S., & Yin, S. (2023). Quantitative analysis of index factors in agricultural compost by infrared spectroscopy. Heliyon, 9, Article e14010.
Shi, C. F., Yang, H. T., Chen, T. T., Guo, L. P., Leng, X. Y., Deng, P. B., Bi, J., Pan, J. G., & Wang, Y. M. (2022). Artificial neural network-genetic algorithm-based optimization of aerobic composting process parameters of Ganoderma lucidum residue. Bioresource Technology, 357, Article 127248.
Sidełko, R., Janowska, B., Szymański, K., Mostowik, N., & Głowacka, A. (2019). Advanced methods to calculation of pressure drop during aeration in composting process. The Science of the Total Environment, 674, 19-25.
Singh, A., & Kumari, K. (2019). An inclusive approach for organic waste treatment and valorisation using black soldier fly larvae: A review. Journal of Environmental Management, 251, Article 109569.
Sipma, J., Osuna, M. B., Emanuelsson, M. A. E., & Castro, P. M. L. (2010). Biotreatment of industrial wastewaters under transient-state conditions: Process stability with fluctuations of organic load, substrates, toxicants, and environmental parameters. Critical Reviews in Environmental Science and Technology, 40, 147-197.
Siva Raman, S., Stringer, C. L., Bruce, C. N., & Chong, S. C. (2022). Opportunities, challenges and solutions for black soldier fly larvae-based animal feed production. Journal of Cleaner Production, 373, Article 133802.
Sun, Y., Dai, H. L., Moayedi, H., Nguyen Le, B., & Muhammad Adnan, R. (2024). Predicting steady-state biogas production from waste using advanced machine learning-metaheuristic approaches. Fuel, 355, Article 129493.
Surendra, K. C., Tomberlin, J. K., van Huis, A., Cammack, J. A., Heckmann, L. H L., & Khanal, S. K. (2020). Rethinking organic wastes bioconversion: Evaluating the potential of the black soldier fly (Hermetia illucens (L.)) (Diptera: Stratiomyidae) (BSF). Waste Management, 117, 58-80.
Tang, L., Zeng, G., Liu, J., Xu, X., Zhang, Y., Shen, G., Li, Y., & Liu, C. (2008). Catechol determination in compost bioremediation using a laccase sensor and artificial neural networks. Analytical and Bioanalytical Chemistry, 391, 679-685.
Tedeschi, L. O. (2006). Assessment of the adequacy of mathematical models. Agricultural Systems, 89, 225-247.
Todeschini, R., Consonni, V., Mauri, A., & Pavan, M. (2004). Detecting “bad” regression models: Multicriteria fitness functions in regression analysis. Analytica Chimica Acta, 515, 199-208.
Tufaner, F., & Demirci, Y. (2020). Prediction of biogas production rate from anaerobic hybrid reactor by artificial neural network and nonlinear regressions models. Clean Technologies and Environmental Policy, 22, 713-724.
Uddin, S., Haque, I., Lu, H., Ali Moni, M., & Gide, E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports, 12, 6256.
Vergnoux, A., Guiliano, M., Le Dréau, Y., Kister, J., Dupuy, N., & Doumenq, P. (2009). Monitoring of the evolution of an industrial compost and prediction of some compost properties by NIR spectroscopy. Science of the Total Environment, 407, 2390-2403.
Waltham, B., & Örmeci, B. (2020). Fluorescence intensity, conductivity, and UV–vis absorbance as surrogate parameters for real-time monitoring of anaerobic digestion of wastewater sludge. Journal of Water Process Engineering, 37, Article 101395.
Wan, X., Li, J., Xie, L., Wei, Z., Wu, J., Wah Tong, Y., 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, Article 128107.
Wang, X., Bai, X., Li, Z., Zhou, X., Cheng, S., Sun, J., & Liu, T. (2018). Evaluation of artificial neural network models for online monitoring of alkalinity in anaerobic co-digestion system. Biochemical Engineering Journal, 140, 85-92.
Wang, Y., Huntington, T., & Scown, C. D. (2021). Tree-based automated machine learning to predict biogas production for anaerobic co-digestion of organic waste. ACS Sustainable Chemistry & Engineering, 9, 12990-13000.
Wang, Y., Ma, F., Zhu, T., Liu, Z., Ma, Y., Li, T., & Hao, L. (2023a). Electric heating promotes sludge composting process: Optimization of heating method through machine learning algorithms. Bioresource Technology, 382, Article 129177.
Wang, Z., Peng, X., Xia, A., Shah, A. A., Yan, H., Huang, Y., Zhu, X., Zhu, X., & Liao, Q. (2023b). Comparison of machine learning methods for predicting the methane production from anaerobic digestion of lignocellulosic biomass. Energy, 263, Article 125883.
Wang, X., Rashid, I., Zhao, Z., Oladele, M., Xiang, W., Huang, Y., Wazer, E., McCutcheon, J., Bollas, G., & Contreras, J., et al. (2024). Machine learning algorithm integrated with real-time In situ sensors and physiochemical principle-driven soft sensors toward an anaerobic digestion-data fusion framework. ACS ES&T Water, 4, 1061-1072.
Ward, A. J., Hobbs, P. J., Holliman, P. J., & Jones, D. L. (2011). Evaluation of near infrared spectroscopy and software sensor methods for determination of total alkalinity in anaerobic digesters. Bioresource Technology, 102, 4083-4090.
Wu, G., Kechavarzi, C., Li, X., Wu, S., Pollard, S. J. T., Sui, H., & Coulon, F. (2013). Machine learning models for predicting PAHs bioavailability in compost amended soils. Chemical Engineering Journal, 223, 747-754.
Wu, D., Li, L., Zhao, X., Peng, Y., Yang, P., & Peng, X. (2019). Anaerobic digestion: A review on process monitoring. Renewable and Sustainable Energy Reviews, 103, 1-12.
Wu, L., Wei, W., Song, L., Wozniak-Karczewska, M., Chrzanowski, Ł., & Ni, B. J. (2021). Upgrading biogas produced in anaerobic digestion: Biological removal and bioconversion of CO2 in biogas. Renewable and Sustainable Energy Reviews, 150, Article 111448.
Xie, S., Hai, F. I., Zhan, X., Guo, W., Ngo, H. H., Price, W. E., & Nghiem, L. D. (2016). Anaerobic co-digestion: A critical review of mathematical modelling for performance optimization. Bioresource Technology, 222, 498-512.
Xu, R. Z., Cao, J. S., Wu, Y., Wang, S. N., Luo, J. Y., Chen, X., & Fang, F. (2020). An integrated approach based on virtual data augmentation and deep neural networks modeling for VFA production prediction in anaerobic fermentation process. Water Research, 184, Article 116103.
Xue, W., Hu, X., Wei, Z., Mei, X., Chen, X., & Xu, Y. (2019). A fast and easy method for predicting agricultural waste compost maturity by image-based deep learning. Bioresource Technology, 290, Article 121761.
Yılmaz, E. C., Aydın Temel, F., Cagcag Yolcu, O., & Turan, N. G. (2022). Modeling and optimization of process parameters in co-composting of tea waste and food waste: Radial basis function neural networks and genetic algorithm. Bioresource Technology, 363, Article 127910.
Yang, Y., Kumar Awasthi, M., Du, W., Ren, X., Lei, T., & Lv, J. (2020). Compost supplementation with nitrogen loss and greenhouse gas emissions during pig manure composting. Bioresource Technology, 297, Article 122435.
Yang, G., Li, Y., Zhen, F., Xu, Y., Liu, J., Li, N., Sun, Y., Luo, L., Wang, M., & Zhang, L. (2021a). Biochemical methane potential prediction for mixed feedstocks of straw and manure in anaerobic co-digestion. Bioresource Technology, 326, Article 124745.
Yang, Y., Wang, G., Li, G., Ma, R., Kong, Y., & Yuan, J. (2021b). Selection of sensitive seeds for evaluation of compost maturity with the seed germination index. Waste Management, 136, 238-243.
Yildirim, O., & Ozkaya, B. (2023). Prediction of biogas production of industrial scale anaerobic digestion plant by machine learning algorithms. Chemosphere, 335, Article 138976.
Yuan, H., & Zhu, N. (2016). Progress in inhibition mechanisms and process control of intermediates and by-products in sewage sludge anaerobic digestion. Renewable and Sustainable Energy Reviews, 58, 429-438.
Zaied, B. K., Rashid, M., Nasrullah, M., Bari, B. S., Zularisam, A. W., Singh, L., Kumar, D., & Krishnan, S. (2023). Prediction and optimization of biogas production from POME co-digestion in solar bioreactor using artificial neural network coupled with particle swarm optimization (ANN-PSO). Biomass Conversion and Biorefinery, 13, 73-88.
Zamri, M. F. M. A., Hasmady, S., Akhiar, A., Ideris, F., Shamsuddin, A. H., Mofijur, M., Rizwanul Fattah, I. M., & Mahlia, T. M. I. (2021). A comprehensive review on anaerobic digestion of organic fraction of municipal solid waste. Renewable and Sustainable Energy Reviews, 137, Article 110637.
Zareei, S., & Khodaei, J. (2017). Modeling and optimization of biogas production from cow manure and maize straw using an adaptive neuro-fuzzy inference system. Renewable Energy, 114, 423-427.
Zhan, Y., & Zhu, J. (2024). Response surface methodology and artificial neural network-genetic algorithm for modeling and optimization of bioenergy production from biochar-improved anaerobic digestion. Applied Energy, 355, Article 122336.
Zhang, Y., Feng, Y., Ren, Z., Zuo, R., Zhang, T., Li, Y., Wang, Y., Liu, Z., Sun, Z., & Han, Y., et al. (2023). Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion. Bioresource Technology, 374, Article 128746.
Zhang, Y., Li, L., Ren, Z., Yu, Y., Li, Y., Pan, J., Lu, Y., Feng, L., Zhang, W., & Han, Y. (2022). Plant-scale biogas production prediction based on multiple hybrid machine learning technique. Bioresource Technology, 363, Article 127899.
Zhang, Y., Li, W. W., Zeng, G. M., Tang, L., Feng, C. L., Huang, D. L., & Li, Y. P. (2009). Novel neural network-based prediction model for quantifying hydroquinone in compost with biosensor measurements. Environmental Engineering Science, 26, 1063-1070.
Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2018). Efficient kNN classification with different numbers of nearest neighbors. IEEE Transactions on Neural Networks and Learning Systems, 29, 1774-1785.
Zhou, Y., Tang, L., Zeng, G., Zhang, Y., Li, Z., Liu, Y., Chen, J., Yang, G., Zhou, L., & Zhang, S. (2014). Simultaneous determination of hydroquinone and catechol in compost bioremediation using a tyrosinase biosensor and artificial neural networks. Analytical Methods, 6, 2371-2378.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
京公网安备11010802044758号