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Urban computing
Although there are a few books on urban informatics, this is the first book dedicated to urban computing, with a broad spectrum of coverage and an authoritative overview. This book introduces a general framework, key research problems, methodologies and applications of urban computing from a computer science perspective. More specifically, this book focuses on data and computing, distinguishing urban computing from tradition urban science based on classical models and empirical assumptions. Rapid urbanization has led to the expansion of numerous large cities, not only modernizing many people's lives but also posing big challenges, such as air pollution, energy consumption and traffic congestion. Tackling these challenges seemed nearly impossible only a few years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructure have produced a variety of big data, such as human mobility, meteorology, traffic patterns and geographical data. The corresponding big data implies rich knowledge about a city and can help tackle these challenges when used correctly. In addition, the rise of computing technology, such as cloud computing and artificial intelligence (AI), has provided us with unprecedented data processing capabilities-- Provided by publisher.
BOOK
AI- and Security-Empowered End–Edge–Cloud Modular Platform in Complex Industrial Processes: A Case Study on Municipal Solid Waste Incineration
Achieving long-term stable optimization in complex industrial processes (CIPs) is notoriously challenging due to their unclear physical/chemical reaction mechanisms, fluctuating operating conditions, and stringent regulatory constraints. A significant gap persists between promising artificial intelligence (AI) algorithms developed in academic research and their practical deployment in industrial actual processes. To bridge this gap, this article introduces the AI- and security-empowered end–edge–cloud modular platform (AISE3CMP). It consists of four systems such as whole-process AI modeling, end-side basic loop and AI-assisted decision-making, edge-side security isolation and AI control, and cloud-side security transmission and AI optimization. The data isolation collection module of the platform was deployed at a municipal solid waste incineration (MSWI) power plant in Beijing, where it collected multimodal data from real-world industrial sites. The platform’s functionality and effectiveness were validated through the software and hardware developed at the Smart Environmental Protection Beijing Laboratory. The experimental results show efficient and reliable signal transmission between the systems, confirming the platform’s ability to meet the computational demands of AI-based optimization and control algorithms. Compared to previous platforms, AISE3CMP features a dual-security transmission mechanism to mitigate data exchange risks and a modular design to enhance integration efficiency. To the best of our knowledge, this platform is the first prototype of a portable, end-to-end cloud platform with a dual-layer security mechanism for CIPs. While the platform effectively addresses data transmission security, further strengthening of cloud-side data protection and ensuring operational safety on the end-side remain significant challenges for the future. Additionally, utilizing this architecture to enable multi-region and multi-plant data sharing, in order to develop industry-specific large language models, represents a key research direction.
Journal Article
Data analytics in control and operation of municipal wastewater treatment plants: qualitative analysis of needs and barriers
This study aims to identify barriers and needs for the application of data analytics in municipal wastewater treatment. The study was conducted through a series of interviews with stakeholders involved in instrumentation, control, and automation of wastewater treatment plants. Opportunities and limitations observed by different stakeholders were assessed with a thematic analysis. Thematic analysis enabled a broader consideration of social and organizational aspects related to process control, operation, and maintenance. Identified key barriers for applying data analytics included laborious instrumentation maintenance, unstable control loops, and deficient customization of digital tools for users at wastewater treatment plants. Development needs include easier data processing tools, quality assurance of instrumentation, and controller tuning. Results indicate that the perceived potential of data analytics is highly dependent on the performance of underlying physical and digital systems, as well as the control strategies and operating environment of the plant. Despite the barriers, users and developers see many potential applications for data analytics and expect them to have a central role in the control and operation of wastewater treatment plants in the future.
Journal Article
Waste collection in rural communities: challenges under EU regulations. A Case study of Neamt County, Romania
The paper aims to examine the changes in the rural waste management sector at regional scale since the Romania adhesion to the EU in 2007. Traditional waste management based on the mixed waste collection and waste disposal often on improper sites prevailed in municipal waste management options of transitional economies across the globe. The lack of formal waste collection services in rural areas has encouraged the open dumping or backyard burning. The paper analyses the improvements and challenges of local authorities in order to fulfill the new EU requirements in this sector supported by data analysis at local administrative unit levels and field observations. Geographical analysis is compulsory in order to reveal the local disparities. The paper performs an assessment of waste collection issues across 78 rural municipalities within Neamt County. This sector is emerging in rural areas of Eastern Europe, but is far from an efficient municipal waste management system based on the waste hierarchy concept.
Paper
Optimizing municipal solid waste collection management through data mining: a case study in southern Brazil
This study presents three models based on urban solid waste collection data from three municipalities in southern Brazil to identify collection patterns. With the support of Knowledge Discovery in Databases and Data Mining techniques and algorithms, historical data on the weight of unloaded waste from collection trucks in transfer stations, collection route data, and socio-demographic and climate data were used to predict the amount of solid waste collected at each point and assess collection patterns. Data were collected, pre-processed, modeled, and analyzed using Linear Regression, Gradient Boosting, and Random Forest algorithms. Our results show that the Gradient Boosting algorithm model performed better: Mean Absolute Error (25.244), Root Mean Square Error (87.667), and Coefficient of Determination (0.642). In this sense, this study contributes in two ways: first, it helps organizational decision-making and improves the collection service provided to the local community. Second, this study collaborates with the scholarly literature reinforcing the potential of data mining for urban solid waste management.
Journal Article
Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection
Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions.
Journal Article
Advancements in Plastic Waste Sorting: A Review of Techniques and Applications
The widespread utilization of plastic materials across various industrial sectors drives a continuous increase in global polymer demand. The exponential production growth generates severe environmental challenges regarding municipal solid waste management, as substantial fractions of post-consumer residuals enter landfills due to limited recycling infrastructure. Mitigating the global environmental burden requires the implementation of advanced recovery strategies to transition polymer waste into viable secondary feedstocks. Consequently, deploying efficient sorting techniques constitutes a fundamental requirement to integrate plastic materials into formal waste management protocols and optimize recycling yields. Technological innovations currently drive the transition from traditional manual segregation towards highly sophisticated automated sensor-based sorting architectures, maximizing separation efficiency. In this context, the present study comprehensively reviews pretreatment classification techniques engineered to fractionate heterogeneous waste streams into high-purity material flows. Rather than restricting the analysis to polyolefins, this review encompasses a broad spectrum of commodity polymers predominantly found in urban solid waste environments.
Journal Article
CO emission predictions in municipal solid waste incineration based on reduced depth features and long short-term memory optimization
Carbon monoxide (CO) is a toxic gas emitted during municipal solid waste incineration (MSWI). Its emission prediction is conducive to pollutant reduction and optimized control of MSWI. The variables of MSWI exhibit redundant and interdependent correlations with CO emissions. Furthermore, the mapping relationship is difficult to characterize. Therefore, the work proposed a CO emission prediction method based on reduced depth features and long short-term memory (LSTM) optimization. The particle design for reduced depth feature and LSTM optimization was initially developed—incorporating an adaptive threshold range for feature selection based on the inherent characteristics of modeling data. Secondly, the nonlinear depth features were extracted using ultra-one-dimensional convolution and subsequently fed into an LSTM model for prediction construction. The hyperparameters of the convolutional layer and LSTM were updated based on the loss function. The generalization performance of the model was used as the fitness function of the optimization. Finally, the particle swarm optimization (PSO) was used to adaptively reduce depth features and model’s hyperparameters. The rationality and effectiveness of the proposed method were validated using the benchmark dataset and CO dataset of MSWI.
R
2
of the testing datasets for RB and CO were 0.9097 ± 3.64E-04 and 0.7636 ± 3.19E-03, respectively, by repeating 30 times.
Journal Article
Heterogeneous feature ensemble modeling with stochastic configuration networks for predicting furnace temperature of a municipal solid waste incineration process
Considering the accuracy, generalization ability, stability, and training efficiency of a furnace temperature model in the process of municipal solid waste incineration, a heterogeneous feature ensemble modeling method for furnace temperature is proposed in this paper. First, heterogeneous features are generated according to the operation mechanism of the waste incineration process, and the training subset of the furnace temperature- and grate temperature-based model is determined from the historical data of this process. Second, the base model pools of furnace temperature and grate temperature are constructed by a regularized stochastic configuration network, and a set of optimal base models are retained by selective base model technology. Then, a negative correlation learning strategy is employed to establish a simultaneous training ensemble model of furnace temperature, and a regularized stochastic configuration network is used to establish a secondary training ensemble model of furnace temperature. The final output of the furnace temperature is obtained by the average value of the output of the above two ensemble models. Finally, a comparative experiment is carried out using the historical data of a waste incineration plant. The results show that the furnace temperature model established in this paper has advantages in accuracy, generalization ability, stability, and training efficiency. It can be applied to the field of furnace temperature prediction and control in the waste incineration process.
Journal Article