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Tao J, Li Z, Chen C, Liang R, Wu S, Lin F, Cheng Z, Yan B, Chen G. Intelligent technologies powering clean incineration of municipal solid waste: A system review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 935:173082. [PMID: 38740220 DOI: 10.1016/j.scitotenv.2024.173082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/01/2024] [Accepted: 05/07/2024] [Indexed: 05/16/2024]
Abstract
Cleanliness has been paramount for municipal solid waste incineration (MSWI) systems. In recent years, the rapid advancement of intelligent technologies has fostered unprecedented opportunities for enhancing the cleanliness of MSWI systems. This paper offers a review and analysis of cutting-edge intelligent technologies in MSWI, which include process monitoring, intelligent algorithms, combustion control, flue gas treatment, and particulate control. The objective is to summarize current applications of these techniques and to forecast future directions. Regarding process monitoring, intelligent image analysis has facilitated real-time tracking of combustion conditions. For intelligent algorithms, machine learning models have shown advantages in accurately forecasting key process parameters and pollutant concentrations. In terms of combustion control, intelligent systems have achieved consistent prediction and regulation of temperature, oxygen content, and other parameters. Intelligent monitoring and forecasting of carbon monoxide and dioxins for flue gas treatment have exhibited satisfactory performance. Concerning particulate control, multi-objective optimization facilitates the sustainable utilization of fly ash. Despite remarkable progress, challenges remain in improving process stability and monitoring instrumentation of intelligent MSWI technologies. By systematically summarizing current applications, this timely review offers valuable insights into the future upgrade of intelligent MSWI systems.
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Affiliation(s)
- Junyu Tao
- Interdisciplinary Innovation Lab for Environment & Energy, School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China
| | - Zaixin Li
- Interdisciplinary Innovation Lab for Environment & Energy, School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China
| | - Chao Chen
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
| | - Rui Liang
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
| | - Shuang Wu
- Interdisciplinary Innovation Lab for Environment & Energy, School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China
| | - Fawei Lin
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
| | - Zhanjun Cheng
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China; Tianjin Key Lab of Biomass Wastes Utilization, Tianjin Engineering Research Center of Bio Gas/Oil Technology, Tianjin 300072, China
| | - Beibei Yan
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China; Tianjin Key Lab of Biomass Wastes Utilization, Tianjin Engineering Research Center of Bio Gas/Oil Technology, Tianjin 300072, China
| | - Guanyi Chen
- Interdisciplinary Innovation Lab for Environment & Energy, School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China; School of Ecology and Environment, Tibet University, Lhasa 850012, China.
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Al-Omran K, Khan E. Predicting medical waste generation and associated factors using machine learning in the Kingdom of Bahrain. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:38343-38357. [PMID: 38801607 DOI: 10.1007/s11356-024-33773-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/19/2024] [Indexed: 05/29/2024]
Abstract
Effective planning and managing medical waste necessitate a crucial focus on both the public and private healthcare sectors. This study uses machine learning techniques to estimate medical waste generation and identify associated factors in a representative private and a governmental hospital in Bahrain. Monthly data spanning from 2018 to 2022 for the private hospital and from 2019 to February 2023 for the governmental hospital was utilized. The ensemble voting regressor was determined as the best model for both datasets. The model of the governmental hospital is robust and successful in explaining 90.4% of the total variance.Similarly, for the private hospital, the model variables are able to explain 91.7% of the total variance. For the governmental hospital, the significant features in predicting medical waste generation were found to be the number of inpatients, population, surgeries, and outpatients, in descending order of importance. In the case of the private hospital, the order of feature importance was the number of inpatients, deliveries, personal income, surgeries, and outpatients. These findings provide insights into the factors influencing medical waste generation in the studied hospitals and highlight the effectiveness of the ensemble voting regressor model in predicting medical waste quantities.
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Affiliation(s)
- Khadija Al-Omran
- Environment and Sustainable Development, College of Science, University of Bahrain, Sakhir, 32038, Kingdom of Bahrain.
- School of Logistics and Maritime Studies, Faculty of Business and Logistics, Bahrain Polytechnic, Isa Town, 33349, Kingdom of Bahrain.
| | - Ezzat Khan
- Environment and Sustainable Development, College of Science, University of Bahrain, Sakhir, 32038, Kingdom of Bahrain
- Department of Chemistry, University of Malakand, Lower Dir, Chakdara, 18800, Khyber Pakhtunkhwa, Pakistan
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Zhao Y, Tao Z, Li Y, Sun H, Tang J, Wang Q, Guo L, Song W, Li BL. Prediction of municipal solid waste generation and analysis of dominant variables in rapidly developing cities based on machine learning - a case study of China. WASTE MANAGEMENT & RESEARCH : THE JOURNAL OF THE INTERNATIONAL SOLID WASTES AND PUBLIC CLEANSING ASSOCIATION, ISWA 2024; 42:476-484. [PMID: 37641494 DOI: 10.1177/0734242x231192766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Prediction of municipal solid waste (MSW) generation plays an essential role in effective waste management. The main objectives of this study were to develop models for accurate prediction of MSW generation (MSWG) and analyze the influence of dominant variables on MSWG. To elevate the model's prediction accuracy, more than 50 municipal variables were considered original variables, which were selected from 12 categories. According to the screening results, the dominant variables are classified into four categories: urban greening, population size and residential density, regional economic development and resident income and expenditure. Among the seven machine learning methods, back propagation (BP) neural network has the best model evaluation effect. The R2 of the BP neural network model of Jiangsu, Zhejiang and Shandong provinces were 0.969, 0.941 and 0.971 respectively. The prediction accuracy of Shandong province (93.8%) was the best, followed by Jiangsu province (92.3%) and Zhejiang province (72.7%). The correlation between dominant variables and the MSWG was mined, suggesting that regional GDP and the total retail sales of consumer goods were the most important dominant variables affecting MSWG. Moreover, the MSWG might not absolutely associate with the population size and residential density. The method used in this study is a practical tool for policymakers on regional/local waste management and MSWG control.
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Affiliation(s)
- Ying Zhao
- School of Environment, Harbin Institute of Technology, Harbin, China
- Ecological Complexity and Modeling Laboratory, Department of Botany and Plant Sciences, University of California, Riverside, CA, USA
| | - Zhe Tao
- School of Environment, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, China
| | - Ying Li
- School of Environment, Harbin Institute of Technology, Harbin, China
| | - Huige Sun
- School of Environment, Harbin Institute of Technology, Harbin, China
| | - Jingrui Tang
- School of Environment, Harbin Institute of Technology, Harbin, China
| | - Qianya Wang
- School of Environment, Harbin Institute of Technology, Harbin, China
| | - Liang Guo
- School of Environment, Harbin Institute of Technology, Harbin, China
- Ecological Complexity and Modeling Laboratory, Department of Botany and Plant Sciences, University of California, Riverside, CA, USA
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, China
| | - Weiwei Song
- School of Environment, Harbin Institute of Technology, Harbin, China
| | - Bailian Larry Li
- Ecological Complexity and Modeling Laboratory, Department of Botany and Plant Sciences, University of California, Riverside, CA, USA
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Zhang B. The analysis of ecological security and tourist satisfaction of ice-and-snow tourism under deep learning and the Internet of Things. Sci Rep 2024; 14:10705. [PMID: 38730047 PMCID: PMC11087544 DOI: 10.1038/s41598-024-61598-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 05/07/2024] [Indexed: 05/12/2024] Open
Abstract
This paper aims to propose a prediction method based on Deep Learning (DL) and Internet of Things (IoT) technology, focusing on the ecological security and tourist satisfaction of Ice-and-Snow Tourism (IST) to solve practical problems in this field. Accurate predictions of ecological security and tourist satisfaction in IST have been achieved by collecting and analyzing environment and tourist behavior data and combining with DL models, such as convolutional and recurrent neural networks. The experimental results show that the proposed method has significant advantages in performance indicators, such as accuracy, F1 score, Mean Squared Error (MSE), and correlation coefficient. Compared to other similar methods, the method proposed improves accuracy by 3.2%, F1 score by 0.03, MSE by 0.006, and correlation coefficient by 0.06. These results emphasize the important role of combining DL with IoT technology in predicting ecological security and tourist satisfaction in IST.
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Affiliation(s)
- Baiju Zhang
- The Tourism College of Changchun University, Jilin Northeast Asia Research Center On Leisure Economics, Jilin Province Research Center for Cultural Tourism Education and Enterprise Development, Changchun Industry Convergence Research Center of Culture and Tourism, Changchun Ice and Snow Industry Research Institute, Changchun, 130607, China.
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Ahmed S, Mubarak S, Du JT, Wibowo S. Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16798. [PMID: 36554676 PMCID: PMC9779277 DOI: 10.3390/ijerph192416798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/10/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in garbage, and many urban areas are becoming increasingly difficult to manage. The advancement of technology allows researchers to transmit data from municipal bins using smart IoT (Internet of Things) devices. These bin data can contribute to a compelling analysis of waste management instead of depending on the historical dataset. Thus, this study proposes forecasting models comprising of 1D CNN (Convolutional Neural Networks) long short-term memory (LSTM), gated recurrent units (GRU) and bidirectional long short-term memory (Bi-LSTM) for time series prediction of public bins. The execution of the models is evaluated by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient determination (R2) and Root Mean Squared Error (RMSE). For different numbers of epochs, hidden layers, dense layers, and different units in hidden layers, the RSME values measured for 1D CNN, LSTM, GRU and Bi-LSTM models are 1.12, 1.57, 1.69 and 1.54, respectively. The best MAPE value is 1.855, which is found for the LSTM model. Therefore, our findings indicate that LSTM can be used for bin emptiness or fullness prediction for improved planning and management due to its proven resilience and increased forecast accuracy.
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Affiliation(s)
- Sabbir Ahmed
- UniSA STEM, University of South Australia, Adelaide, SA 5001, Australia
| | - Sameera Mubarak
- UniSA STEM, University of South Australia, Adelaide, SA 5001, Australia
| | - Jia Tina Du
- UniSA STEM, University of South Australia, Adelaide, SA 5001, Australia
| | - Santoso Wibowo
- School of Engineering and Technology, Central Queensland University, Melbourne, VIC 3000, Australia
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Jassim MS, Coskuner G, Sultana N, Hossain SZ. Forecasting domestic waste generation during successive COVID - 19 lockdowns by Bidirectional LSTM super learner neural network. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Ihsanullah I, Alam G, Jamal A, Shaik F. Recent advances in applications of artificial intelligence in solid waste management: A review. CHEMOSPHERE 2022; 309:136631. [PMID: 36183887 DOI: 10.1016/j.chemosphere.2022.136631] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/09/2022] [Accepted: 09/25/2022] [Indexed: 05/17/2023]
Abstract
Efficient management of solid waste is essential to lessen its potential health and environmental impacts. However, the current solid waste management practices encounter several challenges. The development of effective waste management systems using advanced technologies is vital to overcome the challenges faced by the current approaches. Artificial Intelligence (AI) has emerged as a powerful tool for applications in various fields. Several studies also reported the applications of AI techniques in the management of solid waste. This article critically reviews the recent advancements in the applications of AI techniques for the management of solid waste. Various AI and hybrid techniques have been successfully employed to predict the performance of various methods used for the generation, segregation, storage, and treatment of solid waste. The key challenges that limit the applications of AI in solid waste are highlighted. These include the availability and selection of applicable data, poor reproducibility, and less evidence of applications in real solid waste. Based on identified gaps and challenges, recommendations for future work are provided. This review is beneficial for all stakeholders in the field of solid waste management, including policy-makers, governments, waste management organizations, municipalities, and researchers.
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Affiliation(s)
- I Ihsanullah
- Center for Environment and Water, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
| | - Gulzar Alam
- School of Computing, Ulster University, Belfast, Northern Ireland, United Kingdom
| | - Arshad Jamal
- Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31451, Saudi Arabia
| | - Feroz Shaik
- Department of Mechanical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia
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WSN-Based SHM Optimisation Algorithm for Civil Engineering Structures. Processes (Basel) 2022. [DOI: 10.3390/pr10102113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
With the development of economy and the improvement of architectural aesthetics, civil structure buildings show a trend of diversification and complexity, which brings great challenges to the Structural Health Monitoring (SHM) of civil structure buildings. In order to optimise the structural health monitoring effect of civil structures, reduce monitoring costs, and improve the ability of civil structures to deal with risks, a civil structure health monitoring method combining Variational Modal Decomposition (VMD) and the Gated Recurrent Unit (GRU) is proposed. The gated neural network algorithm of modal decomposition is used, and then a wireless sensor network (WSN) civil structure health monitoring model is constructed on this basis. Finally, the application effect of the model is tested and analysed. The results show that the network energy consumption of this model can reach a minimum of 0.05 J, which is 0.05 J less than that of the Gate Recurrent Unit (GRU) model. The minimum loss value is 0.08. Its Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE), and Mean Absolute Percent Error (MAPE) are 0.03, 0.04, and 0.06, respectively; the prediction error is the smallest, the overall amplitude difference monitored by the model remains at a low level of less than 0.01, and the changes are closest to the real situation. This shows that the model improves the operation efficiency, improves the accuracy of health monitoring, enhances the adaptability of building structural health monitoring to complex structures, provides a new way for the development of building structural health monitoring technology, and is conducive to enhancing civil structures. The safety and stability of buildings promote the high-quality development of civil and structural buildings.
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Namoun A, Hussein BR, Tufail A, Alrehaili A, Syed TA, BenRhouma O. An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation. SENSORS (BASEL, SWITZERLAND) 2022; 22:3506. [PMID: 35591195 PMCID: PMC9104882 DOI: 10.3390/s22093506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/20/2022] [Accepted: 04/26/2022] [Indexed: 05/07/2023]
Abstract
With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed.
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Affiliation(s)
- Abdallah Namoun
- Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; (A.A.); (T.A.S.); (O.B.)
| | - Burhan Rashid Hussein
- School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei; (B.R.H.); (A.T.)
| | - Ali Tufail
- School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei; (B.R.H.); (A.T.)
| | - Ahmed Alrehaili
- Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; (A.A.); (T.A.S.); (O.B.)
- Department of Informatics, University of Sussex, Brighton BN1 9RH, UK
| | - Toqeer Ali Syed
- Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; (A.A.); (T.A.S.); (O.B.)
| | - Oussama BenRhouma
- Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; (A.A.); (T.A.S.); (O.B.)
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