Optimization of a Capacitated Vehicle Routing Problem for Sustainable Municipal Solid Waste Collection Management Using the PSO-TS Algorithm

Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review

The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities. These strides have, in turn, impacted smart eco-cities, catalyzing ongoing improvements and driving solutions to address complex environmental challenges. This aligns with the visionary concept of smarter eco-cities, an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies. However, there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions. To bridge this gap, this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability. To ensure thoroughness, the study employs a unified evidence synthesis framework integrating aggregative, configurative, and narrative synthesis approaches. At the core of this study lie these subsequent research inquiries: What are the foundational underpinnings of emerging smarter eco-cities, and how do they intricately interrelate, particularly urbanism paradigms, environmental solutions, and data-driven technologies? What are the key drivers and enablers propelling the materialization of smarter eco-cities? What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities? In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices, and what potential benefits and opportunities do they offer for smarter eco-cities? What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities? The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices, as well as the formidable nature of the challenges they pose. Beyond theoretical enrichment, these findings offer invaluable insights and new perspectives poised to empower policymakers, practitioners, and researchers to advance the integration of eco-urbanism and AI- and AIoT-driven urbanism. Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions, stakeholders gain the necessary groundwork for making well-informed decisions, implementing effective strategies, and designing policies that prioritize environmental well-being.

An optimal solid waste management using red fox optimization and hybrid DenseNet-BiLSTM model

Municipal solid waste is considered to eliminate the problem of dumping and spreading in rural and urban areas of developing countries. Accumulation of solid wastes in open spaces receives greater concern in solid waste management systems because it leads to environmental hazards and health issues. To build a clean environment, it is essential to construct an advanced and intelligent waste management system to handle different compositions of waste materials. The significant step of waste management is the separation of waste components, which is normally carried out by manual operation. As a result, it can generate improper disposal of waste materials, to simplify the separation process mechanically, a novel automated Dense Net- BiLSTM-based red fox (DNBiLSTM-RF) approach is proposed in this paper. The proposed solid waste classification framework is analyzed by using waste data which is gathered from the Tehran waste management organization. The input waste data is preprocessed initially to transform raw amorphous data into appropriate data structures and extract the most significant dense and latent data features. The abnormal variations in waste patterns generate outliers which are effectively removed by applying the interquartile range (IQR) filtering process. Finally, the proposed DNBiLSTM-RF classifier accurately discriminates municipal waste materials into six different categories such as wood waste, textiles, food residues, rubber, paper, and plastics. The hyperparameters of the DenseNet-BiLSTM model are fine-tuned using a red fox (RF) optimization algorithm to enhance the classification performance of the model. The effectiveness of the DNBiLSTM-RF approach is evaluated using performance indicators namely root mean square error (RMSE), mean absolute error (MAE), the ratio of RMSE to the standard deviation (SD), Nash-Sutcliffe efficiency, coefficient of determination, recall, precision, F-measure, and accuracy. The analytic result demonstrates the feasibility of the proposed DNBiLSTM-RF approach in classifying waste materials into respective categories precisely with an accurate rate of about 98.9% over other state-of-the-art approaches.

Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review

Waste sorting is an effective means of enhancing resource or energy recovery from municipal solid waste (MSW). Waste sorting management system is not limited to source separation, but also involves at least three stages, i.e., collection and transportation (C&T), pretreatment, and resource utilization. This review focuses on the whole process of MSW management strategy based on the waste sorting perspective. Firstly, as the sources of MSW play an essential role in the means of subsequent valorization, the factors affecting the generation of MSW and its prediction methods are introduced. Secondly, a detailed comparison of approaches to source separation across countries is presented. Constructing a top-down management system and incentivizing or constraining residents' sorting behavior from the bottom up is believed to be a practical approach to promote source separation. Then, the current state of C&T techniques and its network optimization are reviewed, facilitated by artificial intelligence (AI) and the Internet of Things technologies. Furthermore, the advances in pretreatment strategies for enhanced sorting and resource recovery are introduced briefly. Finally, appropriate methods to valorize different MSW are proposed. It is worth noting that new technologies, such as AI, show high application potential in waste management. The sharing of (intermediate) products or energy of varying processing units will inject vitality into the waste management network and achieve sustainable development.