Artificial intelligence (AI) is revolutionizing waste management through better planning and disposable strategies, task automation, and route optimization, leading to higher efficiency and reduction in the overall costs of the waste management process. This article discusses the importance and applications of AI techniques in waste management, specifically solid waste management (SWM).
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Importance of AI in Waste Management
Rapid economic development, population growth, and urbanization have substantially increased waste generation worldwide. Thus, efficient waste management processes have become essential to effectively tackle the ever-rising amount of waste. The waste management processes primarily involve several socio-economic, environmental, climatic, and technical parameters.
Optimizing, predicting, and modeling such non-linear processes using conventional methods are extremely challenging. In recent years, AI techniques have received significant attention to address SWM problems as these techniques can efficiently tackle ill-defined problems, handle uncertainty and incomplete data, and learn from experience.
For instance, incorporating AI in the operation and design of urban waste treatment plants can substantially change the SWM process, leading to more sustainable waste management practices and greater operational efficiencies. AI techniques for sorting and treating solid waste have increasingly become critical for efficient waste management.
Improved Waste Management Planning and Disposable Strategies: A better understanding of waste production and consumption patterns is one of the major benefits of employing AI in waste management. This data can be utilized to develop more efficient garbage management plans.
For instance, waste management companies can analyze the data about the place and time of waste generation to optimize the pick-up schedules for the waste collection trucks. Similarly, the data can also be utilized to identify areas where waste generation can be reduced and waste disposal strategies need adjustments.
In specific scenarios, AI can be employed to predict the waste generation levels in the future and plan accordingly to tackle that waste. This approach is extremely beneficial for waste management after major events, such as festivals, when waste generation increases significantly.
Moreover, AI can be used to develop more effective and efficient disposable strategies. AI techniques can assist in identifying areas where waste disposal methods need improvement by analyzing data about the types of waste generated and their existing disposal locations.
Optimized Route Planning: A typical waste management process commences with the waste collection from different establishments, such as businesses and homes, which increases the importance of an efficient route plan for the waste collection trucks to efficiently complete their tasks.
Waste management companies can use AI to develop detailed maps of every area serviced by them and use such maps to identify the best routes for their trucks, which can reduce fuel costs, travel time, and carbon dioxide emissions. AI can also generate route plans for waste collection trucks automatically when frequent changes occur to the waste pick-up schedule. Additionally, AI can instantly consider the prevailing traffic conditions during route planning, another major advantage.
AI can assist waste management companies to instantly optimize the routes of their trucks using real-time data on weather and traffic conditions to ensure maximum operational efficiency. Sustainable Workflow Management: Waste management companies can adopt workflow management to streamline their operations. They can employ AI to develop detailed models of every waste management process step and then use these models to identify areas where the process needs improvement.
AI Techniques Used in Waste Management
Several AI methods can be used for the optimization and modeling of SWM processes, including artificial neural networks (ANN), genetic algorithms (GAs), decision trees (DT), linear regression (LR), and support vector machine (SVM).
ANNs have been used extensively to model different SWM processes owing to their fault tolerance, suitability, and robustness in displaying the complex relationships between variables in multivariate systems. Different ANN algorithms have been used for SWM processes, including recurrent ANNs, autoregressive ANNs, feed-forward, backpropagation (BP), multilayer perceptron (MLP), and radial basis function (RBF).
GAs can be used to address several SWM issues, including waste generation forecasting, waste classification, estimation of the waste heating value and biogas generation, prediction of waste accumulation and facility siting, reduction of environmental impacts due to waste handling, and optimization of management costs and collection routes.
LR, including gradient boosting regression and multivariate LR, can be utilized to predict leachate formation and waste generation and optimize waste collection frequencies and routes, while SVM can be used to predict waste generation, bin fill level, waste classification, waste heating value, and energy recovery.
DTs can be employed for the detection of illegal waste dumping sites and waste generation behavior patterns and prediction of waste generation, classification, and compression.
Other AI methods that can be used for SWM processes include adaptive neuro-fuzzy inference system (ANFIS), random forest (RF), Naïve Bayes (NB), ant colony optimization (ACO), Q-type clustering, artificial immune system (AIS), non-inferior set estimation (NISE), and logistic model tree.
Major Applications of AI in SWM
Solid Waste Characteristics Prediction: Efficient disposal, treatment, and collection of solid wastes, specifically municipal solid wastes (MSWs), are dependent on accurate waste characteristic prediction. Several studies have evaluated the feasibility of using AI for solid waste characteristics prediction, including waste compression ratio, waste generation trends, and waste classification.
Waste generation forecasting has been investigated extensively in these studies. ANNs were primarily used in these studies, followed by SVMs and GAs. Principal component analysis, Viterbi algorithms, hidden Markov model (HMM), Gaussian mixture model (GMM), hybridized wavelet de-noising, partial least square, gene expression programming, response surface model, correlation analysis, and spectral analysis were used in combination with the AI models.
Many studies have also focused on classifying waste materials to be utilized in automated sorting systems, which eliminate the need for manual waste segregation. Most of these studies employed ANNs to identify different waste fractions. For instance, one study used multi-layer ANNs and hyperspectral imaging to recognize different types of plastics in e-waste.
Similarly, deep convolutional neural networks (CNNs) have been used to automate the garbage sorting process to improve the waste sorting and classification time compared to manual sorting. Another study used a hybrid methodology based on CNNs for feature extraction and MLP for waste segregation into non-recyclables and recyclables and achieved a maximum classification accuracy of 98.2%.
DT using Quinlan’s M5 algorithm was employed to predict the MSW compression ratio, which is crucial for assessing waste settlement during municipal landfill design. The model demonstrated satisfactory performance with a 0.92 correlation coefficient during testing.
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Bin Level Detection: Bin level detection models have been developed to forecast the fill level of waste bins, which can be utilized to effectively tackle waste bin overloading and improper waste disposal. The smart waste collection system performance is primarily affected by the temporal variations in the disposed quantities.
Several studies have investigated the application of AI for waste level monitoring within bins in real time to improve the solid waste collection process. For instance, studies showed that a high bin level classification/waste level classification within bins of above 95% can be obtained using an MLP BP neural network, MLP, and k-nearest neighbor (KNN) classifier.
Vehicle Routing: Efficient waste collection routing is crucial for the success of an integrated SWM strategy as collection works account for 70 to 85% of the overall SWM costs. Studies have developed several waste collection route planning and frequency optimization models primarily using GA and its hybrid versions. For instance, GA can be implemented for route optimization during electronic and electrical household waste collection to reduce waste collection costs by decreasing the number of employees and collection vehicles required for the task.
Similarly, a study has developed software that integrates geographic information system (GIS) with hybrid GA to optimize vehicle routing considering path constraints such as U-turns, traffic directions, and road inclinations. Cellular GA can maximize the waste collection amount and the number of locations served while minimizing the number of vehicles utilized and the distance traveled for waste collection. ANN and regression models can also be employed for waste collection routing.
For instance, nonlinear autoregressive neural networks can be combined with GIS route optimization to evaluate the impact of waste weight and composition on the optimized vehicle routes and emissions. Similarly, multiple linear regression (MLR) and ANN models can be used to predict the waste collection frequency required at various locations.
References and Further Reading
Abdallah, M., Abu Talib, M., Feroz, S., Nasir, Q., Abdalla, H., Mahfood, B. (2020). Artificial intelligence applications in solid waste management: A systematic research review. Waste Management, 109, 231-246. https://doi.org/10.1016/j.wasman.2020.04.057
Mahendra, S. (2023). Artificial Intelligence in Waste Management. [Online] Available at https://www.aiplusinfo.com/blog/artificial-intelligence-in-waste-management/ (Accessed on 16 October 2023)
Sharma, P., Vaid, U. (2021). Emerging role of artificial intelligence in waste management practices. IOP Conference Series: Earth and Environmental Science, 889. https://doi.org/10.1088/1755-1315/889/1/012047